
'''simple docstring'''

import argparse

import torch

from diffusers import UNetaDConditionModel


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Any					, _lowerCamelCase  :    List[str]=None					, _lowerCamelCase  :    Optional[Any]=None					, _lowerCamelCase  :    Dict=None						)      ->  Tuple:
  assert isinstance(_lowerCamelCase					, _lowerCamelCase						), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _lowerCAmelCase    :					List[str]							=   old_checkpoint[path]
      _lowerCAmelCase    :					int							=   old_tensor.shape[0] // 3

      _lowerCAmelCase    :					Optional[int]							=   (-1, channels) if len(old_tensor.shape						) == 3 else (-1)

      _lowerCAmelCase    :					Optional[Any]							=   old_tensor.shape[0] // config["""num_head_channels"""] // 3

      _lowerCAmelCase    :					Tuple							=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
      _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					Union[str, Any]							=   old_tensor.split(channels // num_heads					, dim=1						)

      _lowerCAmelCase    :					Dict							=   query.reshape(_lowerCamelCase						)
      _lowerCAmelCase    :					str							=   key.reshape(_lowerCamelCase						)
      _lowerCAmelCase    :					str							=   value.reshape(_lowerCamelCase						)

  for path in paths:
    _lowerCAmelCase    :					Union[str, Any]							=   path["""new"""]

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _lowerCAmelCase    :					Any							=   new_path.replace(replacement["""old"""]					, replacement["""new"""]						)

        # proj_attn.weight has to be converted from conv 1D to linear
    _lowerCAmelCase    :					Dict							=   old_checkpoint[path["""old"""]]
    _lowerCAmelCase    :					Dict							=   ["""proj_attn.weight"""]
    _lowerCAmelCase    :					List[Any]							=   ["""proj_out.weight""", """proj_in.weight"""]
    if any(k in new_path for k in names						):
      _lowerCAmelCase    :					str							=   weight[:, :, 0]
    elif any(k in new_path for k in names_a						) and len(weight.shape						) > 2 and ".attentions." not in new_path:
      _lowerCAmelCase    :					int							=   weight[:, :, 0]
    else:
      _lowerCAmelCase    :					Tuple							=   weight


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Any=0						)      ->  Union[str, Any]:
  _lowerCAmelCase    :					Dict							=   []
  for old_item in old_list:
    _lowerCAmelCase    :					Tuple							=   old_item

    #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
    #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

    #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
    #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

    #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

    mapping.append({"""old""": old_item, """new""": new_item}						)

  return mapping


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[str]					, _lowerCamelCase  :    int=1						)      ->  str:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split("""."""						)[n_shave_prefix_segments:]						)
  else:
    return ".".join(path.split("""."""						)[:n_shave_prefix_segments]						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Tuple=0						)      ->  Tuple:
  _lowerCAmelCase    :					int							=   []
  for old_item in old_list:
    mapping.append({"""old""": old_item, """new""": old_item}						)

  return mapping


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Any					, _lowerCamelCase  :    List[Any]=0						)      ->  str:
  _lowerCAmelCase    :					List[str]							=   []
  for old_item in old_list:
    _lowerCAmelCase    :					Dict							=   old_item.replace("""in_layers.0"""					, """norm1"""						)
    _lowerCAmelCase    :					Optional[int]							=   new_item.replace("""in_layers.2"""					, """conv1"""						)

    _lowerCAmelCase    :					Dict							=   new_item.replace("""out_layers.0"""					, """norm2"""						)
    _lowerCAmelCase    :					int							=   new_item.replace("""out_layers.3"""					, """conv2"""						)

    _lowerCAmelCase    :					List[Any]							=   new_item.replace("""emb_layers.1"""					, """time_emb_proj"""						)
    _lowerCAmelCase    :					str							=   new_item.replace("""skip_connection"""					, """conv_shortcut"""						)

    _lowerCAmelCase    :					str							=   shave_segments(_lowerCamelCase					, n_shave_prefix_segments=_lowerCamelCase						)

    if "temopral_conv" not in old_item:
      mapping.append({"""old""": old_item, """new""": new_item}						)

  return mapping


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    str					, _lowerCamelCase  :    Any=None					, _lowerCamelCase  :    Optional[Any]=False						)      ->  List[str]:
  _lowerCAmelCase    :					Tuple							=   {}
  _lowerCAmelCase    :					Optional[Any]							=   list(checkpoint.keys()						)

  _lowerCAmelCase    :					List[str]							=   """model.diffusion_model."""

  # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  if sum(k.startswith("""model_ema"""						) for k in keys						) > 1_00 and extract_ema:
    print(f'Checkpoint {path} has both EMA and non-EMA weights.'						)
    print(
        """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
        """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""						)
    for key in keys:
      if key.startswith("""model.diffusion_model"""						):
        _lowerCAmelCase    :					Tuple							=   """model_ema.""" + """""".join(key.split("""."""						)[1:]						)
        _lowerCAmelCase    :					int							=   checkpoint.pop(_lowerCamelCase						)
  else:
    if sum(k.startswith("""model_ema"""						) for k in keys						) > 1_00:
      print(
          """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
          """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""						)

    for key in keys:
      _lowerCAmelCase    :					Dict							=   checkpoint.pop(_lowerCamelCase						)

  _lowerCAmelCase    :					str							=   {}

  _lowerCAmelCase    :					Dict							=   unet_state_dict["""time_embed.0.weight"""]
  _lowerCAmelCase    :					Dict							=   unet_state_dict["""time_embed.0.bias"""]
  _lowerCAmelCase    :					Union[str, Any]							=   unet_state_dict["""time_embed.2.weight"""]
  _lowerCAmelCase    :					Optional[int]							=   unet_state_dict["""time_embed.2.bias"""]

  if config["class_embed_type"] is None:
    # No parameters to port
    ...
  elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
    _lowerCAmelCase    :					Optional[int]							=   unet_state_dict["""label_emb.0.0.weight"""]
    _lowerCAmelCase    :					Optional[Any]							=   unet_state_dict["""label_emb.0.0.bias"""]
    _lowerCAmelCase    :					List[str]							=   unet_state_dict["""label_emb.0.2.weight"""]
    _lowerCAmelCase    :					Optional[int]							=   unet_state_dict["""label_emb.0.2.bias"""]
  else:
    raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'						)

  _lowerCAmelCase    :					int							=   unet_state_dict["""input_blocks.0.0.weight"""]
  _lowerCAmelCase    :					List[Any]							=   unet_state_dict["""input_blocks.0.0.bias"""]

  _lowerCAmelCase    :					List[str]							=   [v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""						)]
  _lowerCAmelCase    :					Optional[int]							=   renew_attention_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					Optional[int]							=   {"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
  assign_to_checkpoint(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

  _lowerCAmelCase    :					List[Any]							=   unet_state_dict["""out.0.weight"""]
  _lowerCAmelCase    :					Dict							=   unet_state_dict["""out.0.bias"""]
  _lowerCAmelCase    :					Any							=   unet_state_dict["""out.2.weight"""]
  _lowerCAmelCase    :					List[Any]							=   unet_state_dict["""out.2.bias"""]

  # Retrieves the keys for the input blocks only
  _lowerCAmelCase    :					str							=   len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """input_blocks""" in layer}						)
  _lowerCAmelCase    :					Tuple							=   {
      layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
      for layer_id in range(_lowerCamelCase						)
  }

  # Retrieves the keys for the middle blocks only
  _lowerCAmelCase    :					str							=   len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """middle_block""" in layer}						)
  _lowerCAmelCase    :					Any							=   {
      layer_id: [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]
      for layer_id in range(_lowerCamelCase						)
  }

  # Retrieves the keys for the output blocks only
  _lowerCAmelCase    :					Optional[Any]							=   len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """output_blocks""" in layer}						)
  _lowerCAmelCase    :					Union[str, Any]							=   {
      layer_id: [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]
      for layer_id in range(_lowerCamelCase						)
  }

  for i in range(1					, _lowerCamelCase						):
    _lowerCAmelCase    :					List[str]							=   (i - 1) // (config["""layers_per_block"""] + 1)
    _lowerCAmelCase    :					Optional[Any]							=   (i - 1) % (config["""layers_per_block"""] + 1)

    _lowerCAmelCase    :					List[Any]							=   [
        key for key in input_blocks[i] if f'input_blocks.{i}.0' in key and f'input_blocks.{i}.0.op' not in key
    ]
    _lowerCAmelCase    :					Union[str, Any]							=   [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
    _lowerCAmelCase    :					Tuple							=   [key for key in input_blocks[i] if f'input_blocks.{i}.2' in key]

    if f'input_blocks.{i}.op.weight' in unet_state_dict:
      _lowerCAmelCase    :					Dict							=   unet_state_dict.pop(
          f'input_blocks.{i}.op.weight'						)
      _lowerCAmelCase    :					List[str]							=   unet_state_dict.pop(
          f'input_blocks.{i}.op.bias'						)

    _lowerCAmelCase    :					List[str]							=   renew_resnet_paths(_lowerCamelCase						)
    _lowerCAmelCase    :					List[str]							=   {"""old""": f'input_blocks.{i}.0', """new""": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
    assign_to_checkpoint(
        _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

    _lowerCAmelCase    :					Optional[int]							=   [key for key in resnets if """temopral_conv""" in key]
    _lowerCAmelCase    :					Optional[int]							=   renew_temp_conv_paths(_lowerCamelCase						)
    _lowerCAmelCase    :					Tuple							=   {
        """old""": f'input_blocks.{i}.0.temopral_conv',
        """new""": f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
    }
    assign_to_checkpoint(
        _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

    if len(_lowerCamelCase						):
      _lowerCAmelCase    :					List[Any]							=   renew_attention_paths(_lowerCamelCase						)
      _lowerCAmelCase    :					Dict							=   {"""old""": f'input_blocks.{i}.1', """new""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
      assign_to_checkpoint(
          _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

    if len(_lowerCamelCase						):
      _lowerCAmelCase    :					str							=   renew_attention_paths(_lowerCamelCase						)
      _lowerCAmelCase    :					Optional[int]							=   {
          """old""": f'input_blocks.{i}.2',
          """new""": f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
      }
      assign_to_checkpoint(
          _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

  _lowerCAmelCase    :					List[str]							=   middle_blocks[0]
  _lowerCAmelCase    :					Dict							=   [key for key in resnet_a if """temopral_conv""" in key]
  _lowerCAmelCase    :					Any							=   middle_blocks[1]
  _lowerCAmelCase    :					str							=   middle_blocks[2]
  _lowerCAmelCase    :					int							=   middle_blocks[3]
  _lowerCAmelCase    :					str							=   [key for key in resnet_a if """temopral_conv""" in key]

  _lowerCAmelCase    :					int							=   renew_resnet_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   {"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
  assign_to_checkpoint(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, config=_lowerCamelCase					, additional_replacements=[meta_path]						)

  _lowerCAmelCase    :					Union[str, Any]							=   renew_temp_conv_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   {"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
  assign_to_checkpoint(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, config=_lowerCamelCase					, additional_replacements=[meta_path]						)

  _lowerCAmelCase    :					Tuple							=   renew_resnet_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					int							=   {"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
  assign_to_checkpoint(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, config=_lowerCamelCase					, additional_replacements=[meta_path]						)

  _lowerCAmelCase    :					Optional[Any]							=   renew_temp_conv_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					Optional[int]							=   {"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
  assign_to_checkpoint(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, config=_lowerCamelCase					, additional_replacements=[meta_path]						)

  _lowerCAmelCase    :					Dict							=   renew_attention_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					Union[str, Any]							=   {"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

  _lowerCAmelCase    :					Any							=   renew_attention_paths(_lowerCamelCase						)
  _lowerCAmelCase    :					Tuple							=   {"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
  assign_to_checkpoint(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

  for i in range(_lowerCamelCase						):
    _lowerCAmelCase    :					str							=   i // (config["""layers_per_block"""] + 1)
    _lowerCAmelCase    :					Any							=   i % (config["""layers_per_block"""] + 1)
    _lowerCAmelCase    :					Dict							=   [shave_segments(_lowerCamelCase					, 2						) for name in output_blocks[i]]
    _lowerCAmelCase    :					Union[str, Any]							=   {}

    for layer in output_block_layers:
      _lowerCAmelCase			,   _lowerCAmelCase    :					Optional[int]							=   layer.split("""."""						)[0], shave_segments(_lowerCamelCase					, 1						)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(_lowerCamelCase						)
      else:
        _lowerCAmelCase    :					int							=   [layer_name]

    if len(_lowerCamelCase						) > 1:
      _lowerCAmelCase    :					Any							=   [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
      _lowerCAmelCase    :					List[Any]							=   [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
      _lowerCAmelCase    :					List[Any]							=   [key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

      _lowerCAmelCase    :					List[str]							=   renew_resnet_paths(_lowerCamelCase						)
      _lowerCAmelCase    :					int							=   renew_resnet_paths(_lowerCamelCase						)

      _lowerCAmelCase    :					Tuple							=   {"""old""": f'output_blocks.{i}.0', """new""": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
      assign_to_checkpoint(
          _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

      _lowerCAmelCase    :					int							=   [key for key in resnets if """temopral_conv""" in key]
      _lowerCAmelCase    :					Any							=   renew_temp_conv_paths(_lowerCamelCase						)
      _lowerCAmelCase    :					Optional[int]							=   {
          """old""": f'output_blocks.{i}.0.temopral_conv',
          """new""": f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
      }
      assign_to_checkpoint(
          _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

      _lowerCAmelCase    :					Dict							=   {k: sorted(_lowerCamelCase						) for k, v in output_block_list.items()}
      if ["conv.bias", "conv.weight"] in output_block_list.values():
        _lowerCAmelCase    :					Dict							=   list(output_block_list.values()						).index(["""conv.bias""", """conv.weight"""]						)
        _lowerCAmelCase    :					List[str]							=   unet_state_dict[
            f'output_blocks.{i}.{index}.conv.weight'
        ]
        _lowerCAmelCase    :					Tuple							=   unet_state_dict[
            f'output_blocks.{i}.{index}.conv.bias'
        ]

        # Clear attentions as they have been attributed above.
        if len(_lowerCamelCase						) == 2:
          _lowerCAmelCase    :					List[Any]							=   []

      if len(_lowerCamelCase						):
        _lowerCAmelCase    :					Optional[Any]							=   renew_attention_paths(_lowerCamelCase						)
        _lowerCAmelCase    :					List[Any]							=   {
            """old""": f'output_blocks.{i}.1',
            """new""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
        }
        assign_to_checkpoint(
            _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)

      if len(_lowerCamelCase						):
        _lowerCAmelCase    :					int							=   renew_attention_paths(_lowerCamelCase						)
        _lowerCAmelCase    :					Optional[int]							=   {
            """old""": f'output_blocks.{i}.2',
            """new""": f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
        }
        assign_to_checkpoint(
            _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, additional_replacements=[meta_path]					, config=_lowerCamelCase						)
    else:
      _lowerCAmelCase    :					Union[str, Any]							=   renew_resnet_paths(_lowerCamelCase					, n_shave_prefix_segments=1						)
      for path in resnet_0_paths:
        _lowerCAmelCase    :					Dict							=   """.""".join(["""output_blocks""", str(_lowerCamelCase						), path["""old"""]]						)
        _lowerCAmelCase    :					str							=   """.""".join(["""up_blocks""", str(_lowerCamelCase						), """resnets""", str(_lowerCamelCase						), path["""new"""]]						)
        _lowerCAmelCase    :					Optional[Any]							=   unet_state_dict[old_path]

      _lowerCAmelCase    :					List[str]							=   [l for l in output_block_layers if """temopral_conv""" in l]
      for path in temopral_conv_paths:
        _lowerCAmelCase    :					Optional[Any]							=   path.split("""temopral_conv."""						)[-1]
        _lowerCAmelCase    :					Optional[int]							=   """.""".join(["""output_blocks""", str(_lowerCamelCase						), str(_lowerCamelCase						), """temopral_conv""", pruned_path]						)
        _lowerCAmelCase    :					List[Any]							=   """.""".join(["""up_blocks""", str(_lowerCamelCase						), """temp_convs""", str(_lowerCamelCase						), pruned_path]						)
        _lowerCAmelCase    :					str							=   unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
     UpperCamelCase_            =    argparse.ArgumentParser()

     parser.add_argument(
         """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
     )
     parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
     UpperCamelCase_            =    parser.parse_args()

     UpperCamelCase_            =    torch.load(args.checkpoint_path, map_location="""cpu""")
     UpperCamelCase_            =    UNetaDConditionModel()

     UpperCamelCase_            =    convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

     UpperCamelCase_            =    set(unet.state_dict().keys()) - set(converted_ckpt.keys())
     UpperCamelCase_            =    set(converted_ckpt.keys()) - set(unet.state_dict().keys())

     assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

     # load state_dict
     unet.load_state_dict(converted_ckpt)

     unet.save_pretrained(args.dump_path)

     # -- finish converting the unet --




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       self					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




       @property
       def 							__UpperCamelCase    (       self					):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)







       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				torch.Tensor					)

         # Test not batched input
         _lowerCAmelCase    :					List[str]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)


