


'''simple docstring'''

from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


a_						:       List[str]      			=				logging.get_logger(__name__)

# General docstring
a_						:       Optional[int]      			=				"""RegNetConfig"""

# Base docstring
a_						:       Optional[Any]      			=				"""facebook/regnet-y-040"""
a_						:       Union[str, Any]      			=				[1, 1088, 7, 7]

# Image classification docstring
a_						:       List[Any]      			=				"""facebook/regnet-y-040"""
a_						:       str      			=				"""tabby, tabby cat"""

a_						:       Union[str, Any]      			=				[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase = 3    ,						UpperCamelCase = 1    ,						UpperCamelCase = 1    ,						UpperCamelCase = "relu"    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   # The padding and conv has been verified in
   # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
   lowerCamelCase_  				=						tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2  )
   lowerCamelCase_  				=						tf.keras.layers.ConvaD(
       filters=UpperCamelCase    ,						kernel_size=UpperCamelCase    ,						strides=UpperCamelCase    ,						padding="VALID"    ,						groups=UpperCamelCase    ,						use_bias=UpperCamelCase    ,						name="convolution"    ,						)
   lowerCamelCase_  				=						tf.keras.layers.BatchNormalization(epsilon=1e-5    ,						momentum=0.9    ,						name="normalization"  )
   lowerCamelCase_  				=						ACTaFN[activation] if activation is not None else tf.identity







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.convolution(self.padding(UpperCamelCase  )  )
   lowerCamelCase_  				=						self.normalization(UpperCamelCase  )
   lowerCamelCase_  				=						self.activation(UpperCamelCase  )
   return hidden_state





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						config.num_channels
   lowerCamelCase_  				=						TFRegNetConvLayer(
       out_channels=config.embedding_size    ,						kernel_size=3    ,						stride=2    ,						activation=config.hidden_act    ,						name="embedder"    ,						)







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						shape_list(UpperCamelCase  )[1]
   if tf.executing_eagerly() and num_channels != self.num_channels:
    raise ValueError(
        "Make sure that the channel dimension of the pixel values match with the one set in the configuration."  )

   # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
   # So change the input format from `NCHW` to `NHWC`.
   # shape = (batch_size, in_height, in_width, in_channels=num_channels)
   lowerCamelCase_  				=						tf.transpose(UpperCamelCase    ,						perm=(0, 2, 3, 1)  )
   lowerCamelCase_  				=						self.embedder(UpperCamelCase  )
   return hidden_state





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase = 2    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						tf.keras.layers.ConvaD(
       filters=UpperCamelCase    ,						kernel_size=1    ,						strides=UpperCamelCase    ,						use_bias=UpperCamelCase    ,						name="convolution"  )
   lowerCamelCase_  				=						tf.keras.layers.BatchNormalization(epsilon=1e-5    ,						momentum=0.9    ,						name="normalization"  )







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = False  ):







   """simple docstring"""
   return self.normalization(self.convolution(UpperCamelCase  )    ,						training=UpperCamelCase  )





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase    ,						name="pooler"  )
   lowerCamelCase_  				=						[
       tf.keras.layers.ConvaD(filters=UpperCamelCase    ,						kernel_size=1    ,						activation="relu"    ,						name="attention.0"  ),
       tf.keras.layers.ConvaD(filters=UpperCamelCase    ,						kernel_size=1    ,						activation="sigmoid"    ,						name="attention.2"  ),
   ]







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
   lowerCamelCase_  				=						self.pooler(UpperCamelCase  )
   for layer_module in self.attention:
    lowerCamelCase_  				=						layer_module(UpperCamelCase  )
   lowerCamelCase_  				=						hidden_state * pooled
   return hidden_state





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = 1    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						in_channels != out_channels or stride != 1
   lowerCamelCase_  				=						max(1    ,						out_channels // config.groups_width  )
   lowerCamelCase_  				=						(
       TFRegNetShortCut(UpperCamelCase    ,						stride=UpperCamelCase    ,						name="shortcut"  )
       if should_apply_shortcut
       else tf.keras.layers.Activation("linear"    ,						name="shortcut"  )
   )
   # `self.layers` instead of `self.layer` because that is a reserved argument.
   lowerCamelCase_  				=						[
       TFRegNetConvLayer(UpperCamelCase    ,						kernel_size=1    ,						activation=config.hidden_act    ,						name="layer.0"  ),
       TFRegNetConvLayer(
           UpperCamelCase    ,						stride=UpperCamelCase    ,						groups=UpperCamelCase    ,						activation=config.hidden_act    ,						name="layer.1"  ),
       TFRegNetConvLayer(UpperCamelCase    ,						kernel_size=1    ,						activation=UpperCamelCase    ,						name="layer.2"  ),
   ]
   lowerCamelCase_  				=						ACTaFN[config.hidden_act]







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						hidden_state
   for layer_module in self.layers:
    lowerCamelCase_  				=						layer_module(UpperCamelCase  )
   lowerCamelCase_  				=						self.shortcut(UpperCamelCase  )
   hidden_state += residual
   lowerCamelCase_  				=						self.activation(UpperCamelCase  )
   return hidden_state





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = 1    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						in_channels != out_channels or stride != 1
   lowerCamelCase_  				=						max(1    ,						out_channels // config.groups_width  )
   lowerCamelCase_  				=						(
       TFRegNetShortCut(UpperCamelCase    ,						stride=UpperCamelCase    ,						name="shortcut"  )
       if should_apply_shortcut
       else tf.keras.layers.Activation("linear"    ,						name="shortcut"  )
   )
   lowerCamelCase_  				=						[
       TFRegNetConvLayer(UpperCamelCase    ,						kernel_size=1    ,						activation=config.hidden_act    ,						name="layer.0"  ),
       TFRegNetConvLayer(
           UpperCamelCase    ,						stride=UpperCamelCase    ,						groups=UpperCamelCase    ,						activation=config.hidden_act    ,						name="layer.1"  ),
       TFRegNetSELayer(UpperCamelCase    ,						reduced_channels=int(round(in_channels / 4  )  )    ,						name="layer.2"  ),
       TFRegNetConvLayer(UpperCamelCase    ,						kernel_size=1    ,						activation=UpperCamelCase    ,						name="layer.3"  ),
   ]
   lowerCamelCase_  				=						ACTaFN[config.hidden_act]







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						hidden_state
   for layer_module in self.layers:
    lowerCamelCase_  				=						layer_module(UpperCamelCase  )
   lowerCamelCase_  				=						self.shortcut(UpperCamelCase  )
   hidden_state += residual
   lowerCamelCase_  				=						self.activation(UpperCamelCase  )
   return hidden_state





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = 2    ,						UpperCamelCase = 2    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )

   lowerCamelCase_  				=						TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
   lowerCamelCase_  				=						[
       # downsampling is done in the first layer with stride of 2
       layer(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						stride=UpperCamelCase    ,						name="layers.0"  ),
       *[layer(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						name=f'''layers.{i+1}'''  ) for i in range(depth - 1  )],
   ]







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   for layer_module in self.layers:
    lowerCamelCase_  				=						layer_module(UpperCamelCase  )
   return hidden_state





class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						[]
   # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
   self.stages.append(
       TFRegNetStage(
           UpperCamelCase    ,						config.embedding_size    ,						config.hidden_sizes[0]    ,						stride=2 if config.downsample_in_first_stage else 1    ,						depth=config.depths[0]    ,						name="stages.0"    ,						)  )
   lowerCamelCase_  				=						zip(config.hidden_sizes    ,						config.hidden_sizes[1:]  )
   for i, ((in_channels, out_channels), depth) in enumerate(zip(UpperCamelCase    ,						config.depths[1:]  )  ):
    self.stages.append(TFRegNetStage(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						depth=UpperCamelCase    ,						name=f'''stages.{i+1}'''  )  )







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = False    ,						UpperCamelCase = True  ):







   """simple docstring"""
   lowerCamelCase_  				=						() if output_hidden_states else None

   for stage_module in self.stages:
    if output_hidden_states:
     lowerCamelCase_  				=						hidden_states + (hidden_state,)

    lowerCamelCase_  				=						stage_module(UpperCamelCase  )

   if output_hidden_states:
    lowerCamelCase_  				=						hidden_states + (hidden_state,)

   if not return_dict:
    return tuple(v for v in [hidden_state, hidden_states] if v is not None  )

   return TFBaseModelOutputWithNoAttention(last_hidden_state=UpperCamelCase    ,						hidden_states=UpperCamelCase  )





@keras_serializable
class   snake_case					(					tf.keras.layers.Layer    ):





  """simple docstring"""




  _lowerCamelCase						=						RegNetConfig






  def __init__(					self    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase  )
   lowerCamelCase_  				=						config
   lowerCamelCase_  				=						TFRegNetEmbeddings(UpperCamelCase    ,						name="embedder"  )
   lowerCamelCase_  				=						TFRegNetEncoder(UpperCamelCase    ,						name="encoder"  )
   lowerCamelCase_  				=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase    ,						name="pooler"  )







  @unpack_inputs
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = False    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						(
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.embedder(UpperCamelCase    ,						training=UpperCamelCase  )

   lowerCamelCase_  				=						self.encoder(
       UpperCamelCase    ,						output_hidden_states=UpperCamelCase    ,						return_dict=UpperCamelCase    ,						training=UpperCamelCase  )

   lowerCamelCase_  				=						encoder_outputs[0]
   lowerCamelCase_  				=						self.pooler(UpperCamelCase  )

   # Change to NCHW output format have uniformity in the modules
   lowerCamelCase_  				=						tf.transpose(UpperCamelCase    ,						perm=(0, 3, 1, 2)  )
   lowerCamelCase_  				=						tf.transpose(UpperCamelCase    ,						perm=(0, 3, 1, 2)  )

   # Change the other hidden state outputs to NCHW as well
   if output_hidden_states:
    lowerCamelCase_  				=						tuple([tf.transpose(UpperCamelCase    ,						perm=(0, 3, 1, 2)  ) for h in encoder_outputs[1]]  )

   if not return_dict:
    return (last_hidden_state, pooled_output) + encoder_outputs[1:]

   return TFBaseModelOutputWithPoolingAndNoAttention(
       last_hidden_state=UpperCamelCase    ,						pooler_output=UpperCamelCase    ,						hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states    ,						)





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						RegNetConfig
  _lowerCamelCase						=						"regnet"
  _lowerCamelCase						=						"pixel_values"






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)    ,						dtype=tf.floataa  )}



a_						:       str      			=				R"""
    Parameters:
    This model is a Tensorflow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
    behavior.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

a_						:       List[str]      			=				R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""





@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."    ,		lowercase    ,		)
class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						TFRegNetMainLayer(UpperCamelCase    ,						name="regnet"  )







  @unpack_inputs
  @add_start_docstrings_to_model_forward(UpperCamelCase  )
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC    ,						output_type=UpperCamelCase    ,						config_class=_CONFIG_FOR_DOC    ,						modality="vision"    ,						expected_output=_EXPECTED_OUTPUT_SHAPE    ,						)
  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase=False    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						(
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.regnet(
       pixel_values=UpperCamelCase    ,						output_hidden_states=UpperCamelCase    ,						return_dict=UpperCamelCase    ,						training=UpperCamelCase    ,						)
   if not return_dict:
    return (outputs[0],) + outputs[1:]

   return TFBaseModelOutputWithPoolingAndNoAttention(
       last_hidden_state=outputs.last_hidden_state    ,						pooler_output=outputs.pooler_output    ,						hidden_states=outputs.hidden_states    ,						)





@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "    ,		lowercase    ,		)
class   snake_case					(					lowercase    ,		lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						config.num_labels
   lowerCamelCase_  				=						TFRegNetMainLayer(UpperCamelCase    ,						name="regnet"  )
   # classification head
   lowerCamelCase_  				=						[
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(config.num_labels    ,						name="classifier.1"  ) if config.num_labels > 0 else tf.identity,
   ]







  @unpack_inputs
  @add_start_docstrings_to_model_forward(UpperCamelCase  )
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT    ,						output_type=UpperCamelCase    ,						config_class=_CONFIG_FOR_DOC    ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,						)
  def 						snake_case	(					self    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase=False    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						(
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   lowerCamelCase_  				=						return_dict if return_dict is not None else self.config.use_return_dict

   lowerCamelCase_  				=						self.regnet(
       UpperCamelCase    ,						output_hidden_states=UpperCamelCase    ,						return_dict=UpperCamelCase    ,						training=UpperCamelCase  )

   lowerCamelCase_  				=						outputs.pooler_output if return_dict else outputs[1]

   lowerCamelCase_  				=						self.classifier[0](UpperCamelCase  )
   lowerCamelCase_  				=						self.classifier[1](UpperCamelCase  )

   lowerCamelCase_  				=						None if labels is None else self.hf_compute_loss(labels=UpperCamelCase    ,						logits=UpperCamelCase  )

   if not return_dict:
    lowerCamelCase_  				=						(logits,) + outputs[2:]
    return ((loss,) + output) if loss is not None else output

   return TFSequenceClassifierOutput(loss=UpperCamelCase    ,						logits=UpperCamelCase    ,						hidden_states=outputs.hidden_states  )







'''simple docstring'''

import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_						:       str      			=				"""src/diffusers"""
a_						:       int      			=				"""."""


# This is to make sure the diffusers module imported is the one in the repo.
a_						:       int      			=				importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a_						:       List[Any]      			=				spec.loader.load_module()





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		str	):
 return line.startswith(UpperCAmelCase_	) or len(UpperCAmelCase_	) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"      ,       UpperCAmelCase_	) is not None





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 lowerCamelCase_  				=						object_name.split("."	)
 lowerCamelCase_  				=						0

 # First let's find the module where our object lives.
 lowerCamelCase_  				=						parts[i]
 while i < len(UpperCAmelCase_	) and not os.path.isfile(os.path.join(UpperCAmelCase_      ,       F'''{module}.py'''	)	):
  i += 1
  if i < len(UpperCAmelCase_	):
   lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       parts[i]	)
 if i >= len(UpperCAmelCase_	):
  raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''	)

 with open(os.path.join(UpperCAmelCase_      ,       F'''{module}.py'''	)      ,       "r"      ,       encoding="utf-8"      ,       newline="\n"	) as f:
  lowerCamelCase_  				=						f.readlines()

 # Now let's find the class / func in the code!
 lowerCamelCase_  				=						""
 lowerCamelCase_  				=						0
 for name in parts[i + 1 :]:
  while (
      line_index < len(UpperCAmelCase_	) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)'''      ,       lines[line_index]	) is None
  ):
   line_index += 1
  indent += "    "
  line_index += 1

 if line_index >= len(UpperCAmelCase_	):
  raise ValueError(F''' {object_name} does not match any function or class in {module}.'''	)

 # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
 lowerCamelCase_  				=						line_index
 while line_index < len(UpperCAmelCase_	) and _should_continue(lines[line_index]      ,       UpperCAmelCase_	):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]	) <= 1:
  line_index -= 1

 lowerCamelCase_  				=						lines[start_index:line_index]
 return "".join(UpperCAmelCase_	)


a_						:       Optional[Any]      			=				re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
a_						:       Optional[int]      			=				re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
a_						:       List[str]      			=				re.compile(R"""<FILL\s+[^>]*>""")





def __snake_case      (   UpperCAmelCase_		:		Optional[int]	):
 lowerCamelCase_  				=						code.split("\n"	)
 lowerCamelCase_  				=						0
 while idx < len(UpperCAmelCase_	) and len(lines[idx]	) == 0:
  idx += 1
 if idx < len(UpperCAmelCase_	):
  return re.search(r"^(\s*)\S"      ,       lines[idx]	).groups()[0]
 return ""





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
 lowerCamelCase_  				=						len(get_indent(UpperCAmelCase_	)	) > 0
 if has_indent:
  lowerCamelCase_  				=						F'''class Bla:\n{code}'''
 lowerCamelCase_  				=						black.Mode(target_versions={black.TargetVersion.PYaa}      ,       line_length=119      ,       preview=UpperCAmelCase_	)
 lowerCamelCase_  				=						black.format_str(UpperCAmelCase_      ,       mode=UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_  				=						style_docstrings_in_code(UpperCAmelCase_	)
 return result[len("class Bla:\n"	) :] if has_indent else result





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Tuple=False	):
 with open(UpperCAmelCase_      ,       "r"      ,       encoding="utf-8"      ,       newline="\n"	) as f:
  lowerCamelCase_  				=						f.readlines()
 lowerCamelCase_  				=						[]
 lowerCamelCase_  				=						0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(UpperCAmelCase_	):
  lowerCamelCase_  				=						_re_copy_warning.search(lines[line_index]	)
  if search is None:
   line_index += 1
   continue

  # There is some copied code here, let's retrieve the original.
  lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						search.groups()
  lowerCamelCase_  				=						find_code_in_diffusers(UpperCAmelCase_	)
  lowerCamelCase_  				=						get_indent(UpperCAmelCase_	)

  lowerCamelCase_  				=						line_index + 1 if indent == theoretical_indent else line_index + 2
  lowerCamelCase_  				=						theoretical_indent
  lowerCamelCase_  				=						start_index

  # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
  lowerCamelCase_  				=						True
  while line_index < len(UpperCAmelCase_	) and should_continue:
   line_index += 1
   if line_index >= len(UpperCAmelCase_	):
    break
   lowerCamelCase_  				=						lines[line_index]
   lowerCamelCase_  				=						_should_continue(UpperCAmelCase_      ,       UpperCAmelCase_	) and re.search(F'''^{indent}# End copy'''      ,       UpperCAmelCase_	) is None
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]	) <= 1:
   line_index -= 1

  lowerCamelCase_  				=						lines[start_index:line_index]
  lowerCamelCase_  				=						"".join(UpperCAmelCase_	)

  # Remove any nested `Copied from` comments to avoid circular copies
  lowerCamelCase_  				=						[line for line in theoretical_code.split("\n"	) if _re_copy_warning.search(UpperCAmelCase_	) is None]
  lowerCamelCase_  				=						"\n".join(UpperCAmelCase_	)

  # Before comparing, use the `replace_pattern` on the original code.
  if len(UpperCAmelCase_	) > 0:
   lowerCamelCase_  				=						replace_pattern.replace("with"      ,       ""	).split(","	)
   lowerCamelCase_  				=						[_re_replace_pattern.search(UpperCAmelCase_	) for p in patterns]
   for pattern in patterns:
    if pattern is None:
     continue
    lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						pattern.groups()
    lowerCamelCase_  				=						re.sub(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)
    if option.strip() == "all-casing":
     lowerCamelCase_  				=						re.sub(obja.lower()      ,       obja.lower()      ,       UpperCAmelCase_	)
     lowerCamelCase_  				=						re.sub(obja.upper()      ,       obja.upper()      ,       UpperCAmelCase_	)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
   lowerCamelCase_  				=						blackify(lines[start_index - 1] + theoretical_code	)
   lowerCamelCase_  				=						theoretical_code[len(lines[start_index - 1]	) :]

  # Test for a diff and act accordingly.
  if observed_code != theoretical_code:
   diffs.append([object_name, start_index]	)
   if overwrite:
    lowerCamelCase_  				=						lines[:start_index] + [theoretical_code] + lines[line_index:]
    lowerCamelCase_  				=						start_index + 1

 if overwrite and len(UpperCAmelCase_	) > 0:
  # Warn the user a file has been modified.
  print(F'''Detected changes, rewriting {filename}.'''	)
  with open(UpperCAmelCase_      ,       "w"      ,       encoding="utf-8"      ,       newline="\n"	) as f:
   f.writelines(UpperCAmelCase_	)
 return diffs





def __snake_case      (   UpperCAmelCase_		:		bool = False	):
 lowerCamelCase_  				=						glob.glob(os.path.join(UpperCAmelCase_      ,       "**/*.py"	)      ,       recursive=UpperCAmelCase_	)
 lowerCamelCase_  				=						[]
 for filename in all_files:
  lowerCamelCase_  				=						is_copy_consistent(UpperCAmelCase_      ,       UpperCAmelCase_	)
  diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
 if not overwrite and len(UpperCAmelCase_	) > 0:
  lowerCamelCase_  				=						"\n".join(UpperCAmelCase_	)
  raise Exception(
      "Found the following copy inconsistencies:\n"
      + diff
      + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."	)


if __name__ == "__main__":
     a_						:       Tuple      			=				argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     a_						:       Optional[int]      			=				parser.parse_args()

     check_copies(args.fix_and_overwrite)



