from typing import List

# imports
import pytest  # used for our unit tests
import torch
from src.diffusers.models.controlnets.controlnet_xs import get_up_block_adapter
from torch import nn

# function to test
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

class UpBlockControlNetXSAdapter(nn.Module):
    """Minimal stub for testing purposes."""
    def __init__(self, ctrl_to_base):
        super().__init__()
        self.ctrl_to_base = ctrl_to_base
from src.diffusers.models.controlnets.controlnet_xs import get_up_block_adapter

# unit tests

# --- Basic Test Cases ---

def test_basic_shapes_and_types():
    # Test with standard small positive integer values
    out_channels = 8
    prev_output_channel = 4
    ctrl_skip_channels = [2, 3, 4]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    # Check that each module is a Conv2d
    for i, conv in enumerate(adapter.ctrl_to_base):
        # Check out_channels
        expected_out = prev_output_channel if i == 0 else out_channels
        if conv.bias is not None:
            pass

def test_basic_forward_pass():
    # Test that the convs accept input of the right shape and produce correct output shape
    out_channels = 6
    prev_output_channel = 5
    ctrl_skip_channels = [2, 3, 4]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    batch_size = 2
    height = 8
    width = 8
    # First conv
    x0 = torch.randn(batch_size, ctrl_skip_channels[0], height, width)
    y0 = adapter.ctrl_to_base[0](x0)
    # Second conv
    x1 = torch.randn(batch_size, ctrl_skip_channels[1], height, width)
    y1 = adapter.ctrl_to_base[1](x1)
    # Third conv
    x2 = torch.randn(batch_size, ctrl_skip_channels[2], height, width)
    y2 = adapter.ctrl_to_base[2](x2)

def test_basic_different_in_out_channels():
    # Test with different values for in/out channels
    out_channels = 10
    prev_output_channel = 7
    ctrl_skip_channels = [1, 2, 3]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output

# --- Edge Test Cases ---




def test_edge_ctrl_skip_channels_type():
    # ctrl_skip_channels must be a list of ints
    with pytest.raises(TypeError):
        get_up_block_adapter(8, 4, "not a list")
    with pytest.raises(TypeError):
        get_up_block_adapter(8, 4, [2, 3, "four"])

def test_edge_large_single_channel():
    # Very large single in/out channels but still under 100MB tensor limit
    # 1000x1000x1x1 float32 = 4MB, so 512 is safe
    out_channels = 512
    prev_output_channel = 256
    ctrl_skip_channels = [128, 256, 512]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output


def test_large_scale_many_channels():
    # Test with the largest allowed sizes under 100MB
    # 256 channels in/out, 3 convs, 1x1 kernel: 256*256*3*4B = 786KB
    out_channels = 256
    prev_output_channel = 256
    ctrl_skip_channels = [256, 256, 256]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    # Check all convs
    for i in range(3):
        expected_out = prev_output_channel if i == 0 else out_channels
        # Try a forward pass with a large batch
        x = torch.randn(8, 256, 32, 32)  # 8*256*32*32*4B = 1MB per tensor
        y = adapter.ctrl_to_base[i](x)

def test_large_scale_many_adapters():
    # Test instantiating many adapters in a loop to check for memory leaks or slowdowns
    for i in range(20):
        out_channels = 32 + i
        prev_output_channel = 16 + i
        ctrl_skip_channels = [8 + i, 12 + i, 16 + i]
        codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output

def test_large_scale_varied_ctrl_skip_channels():
    # Test with varied ctrl_skip_channels values, including small and large
    out_channels = 64
    prev_output_channel = 32
    ctrl_skip_channels = [1, 32, 64]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    # Forward pass with matching input shapes
    for i in range(3):
        batch = 4
        h, w = 16, 16
        x = torch.randn(batch, ctrl_skip_channels[i], h, w)
        y = adapter.ctrl_to_base[i](x)
        expected_out = prev_output_channel if i == 0 else out_channels

def test_large_scale_forward_zeros():
    # Test that output is all zeros for zero input (since weights and bias are zero)
    out_channels = 32
    prev_output_channel = 16
    ctrl_skip_channels = [8, 12, 16]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    for i in range(3):
        x = torch.zeros(2, ctrl_skip_channels[i], 8, 8)
        y = adapter.ctrl_to_base[i](x)

# --- Miscellaneous and Robustness ---

def test_repr_and_str():
    # The adapter should have a __repr__ and __str__ that do not raise
    codeflash_output = get_up_block_adapter(8, 4, [2, 3, 4]); adapter = codeflash_output
    _ = str(adapter)
    _ = repr(adapter)

def test_module_is_registered():
    # All submodules should be registered as children of the adapter
    codeflash_output = get_up_block_adapter(8, 4, [2, 3, 4]); adapter = codeflash_output
    children = list(adapter.children())

def test_gradients_flow():
    # Check that gradients can flow through the convs
    out_channels = 8
    prev_output_channel = 4
    ctrl_skip_channels = [2, 3, 4]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    x = torch.randn(1, 2, 8, 8, requires_grad=True)
    y = adapter.ctrl_to_base[0](x)
    y.sum().backward()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import List

# imports
import pytest  # used for our unit tests
import torch
from src.diffusers.models.controlnets.controlnet_xs import get_up_block_adapter
from torch import nn

# function to test
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

class UpBlockControlNetXSAdapter(nn.Module):
    def __init__(self, ctrl_to_base: nn.ModuleList):
        super().__init__()
        self.ctrl_to_base = ctrl_to_base
from src.diffusers.models.controlnets.controlnet_xs import get_up_block_adapter

# unit tests

# ------------------- BASIC TEST CASES -------------------

def test_basic_shape_and_type():
    # Test with standard sizes and types
    out_channels = 8
    prev_output_channel = 4
    ctrl_skip_channels = [2, 3, 5]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output

def test_basic_zero_weights():
    # Ensure that all weights and biases are initialized to zero
    codeflash_output = get_up_block_adapter(4, 2, [1, 1, 1]); adapter = codeflash_output
    for conv in adapter.ctrl_to_base:
        for param in conv.parameters():
            pass

def test_basic_forward_pass():
    # Test that the Conv2d layers can process a forward pass
    out_channels = 6
    prev_output_channel = 3
    ctrl_skip_channels = [2, 4, 5]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    # For each conv, pass a tensor of shape (batch, in_channels, H, W)
    for idx, conv in enumerate(adapter.ctrl_to_base):
        in_channels = conv.in_channels
        out_ch = conv.out_channels
        x = torch.randn(1, in_channels, 8, 8)
        y = conv(x)

# ------------------- EDGE TEST CASES -------------------

def test_edge_minimal_channels():
    # Test with minimal positive channels (1)
    codeflash_output = get_up_block_adapter(1, 1, [1, 1, 1]); adapter = codeflash_output
    for conv in adapter.ctrl_to_base:
        # Forward pass
        x = torch.randn(1, 1, 4, 4)
        y = conv(x)

def test_edge_different_prev_and_out_channels():
    # prev_output_channel different from out_channels
    codeflash_output = get_up_block_adapter(7, 3, [2, 2, 2]); adapter = codeflash_output



def test_edge_large_kernel_size():
    # The kernel size is always 1, but test that it cannot be changed via API
    codeflash_output = get_up_block_adapter(4, 2, [3, 3, 3]); adapter = codeflash_output
    for conv in adapter.ctrl_to_base:
        pass

def test_edge_module_is_zeroed():
    # After construction, all parameters are zero
    codeflash_output = get_up_block_adapter(3, 2, [1, 2, 3]); adapter = codeflash_output
    for conv in adapter.ctrl_to_base:
        for param in conv.parameters():
            pass

# ------------------- LARGE SCALE TEST CASES -------------------

def test_large_scale_channels_and_batch():
    # Use large but reasonable channel/batch sizes within memory constraints
    out_channels = 64
    prev_output_channel = 32
    ctrl_skip_channels = [16, 32, 64]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    # Forward pass with large input
    for idx, conv in enumerate(adapter.ctrl_to_base):
        in_channels = conv.in_channels
        out_ch = conv.out_channels
        # Use batch size 8, 32x32 spatial size
        x = torch.randn(8, in_channels, 32, 32)
        y = conv(x)

def test_large_scale_many_channels():
    # Test with the maximum allowed channels under 100MB per tensor
    # 128 channels * 128x128 * 4 bytes = 8MB per tensor, safe for 3 layers
    out_channels = 128
    prev_output_channel = 64
    ctrl_skip_channels = [32, 64, 128]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    for idx, conv in enumerate(adapter.ctrl_to_base):
        in_channels = conv.in_channels
        out_ch = conv.out_channels
        x = torch.randn(2, in_channels, 128, 128)
        y = conv(x)

def test_large_scale_multiple_adapters():
    # Test creating several adapters in a loop (stress test for memory leaks)
    for i in range(10):
        out_channels = 8 + i
        prev_output_channel = 4 + i
        ctrl_skip_channels = [2 + i, 3 + i, 4 + i]
        codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output

def test_large_scale_forward_speed():
    # This test ensures that even with large channels, forward is not too slow
    import time
    out_channels = 64
    prev_output_channel = 32
    ctrl_skip_channels = [16, 32, 64]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    x = torch.randn(4, 16, 64, 64)
    start = time.time()
    y = adapter.ctrl_to_base[0](x)
    elapsed = time.time() - start

def test_large_scale_memory_limit():
    # Ensure that we do not exceed 100MB per tensor
    # 256 channels * 64 * 64 * 4 bytes = 4MB per tensor
    out_channels = 256
    prev_output_channel = 128
    ctrl_skip_channels = [64, 128, 256]
    codeflash_output = get_up_block_adapter(out_channels, prev_output_channel, ctrl_skip_channels); adapter = codeflash_output
    for idx, conv in enumerate(adapter.ctrl_to_base):
        in_channels = conv.in_channels
        out_ch = conv.out_channels
        x = torch.randn(1, in_channels, 64, 64)
        y = conv(x)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.