





"""simple docstring"""





import argparse
import os
import re


_UpperCamelCase							=      'src/diffusers'

# Pattern that looks at the indentation in a line.
_UpperCamelCase							=      re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCamelCase							=      re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_UpperCamelCase							=      re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
_UpperCamelCase							=      re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_UpperCamelCase							=      re.compile(r'\[([^\]]+)\]')



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =_re_indent.search(SCREAMING_SNAKE_CASE   )
							return "" if search is None else search.groups()[0]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[Any]=""    ,						SCREAMING_SNAKE_CASE       :							List[Any]=None    ,						SCREAMING_SNAKE_CASE       :							List[Any]=None   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[str]         =0
							__lowerCamelCase      :      Optional[Any]         =code.split('''\n'''   )
							if start_prompt is not None:
														while not lines[index].startswith(SCREAMING_SNAKE_CASE   ):
																					index += 1
														__lowerCamelCase      :      List[str]         =['''\n'''.join(lines[:index]   )]
							else:
														__lowerCamelCase      :      str         =[]

							# We split into blocks until we get to the `end_prompt` (or the end of the block).
							__lowerCamelCase      :      Optional[int]         =[lines[index]]
							index += 1
							while index < len(SCREAMING_SNAKE_CASE   ) and (end_prompt is None or not lines[index].startswith(SCREAMING_SNAKE_CASE   )):
														if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
																					if len(SCREAMING_SNAKE_CASE   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + ''' '''   ):
																												current_block.append(lines[index]   )
																												blocks.append('''\n'''.join(SCREAMING_SNAKE_CASE   )   )
																												if index < len(SCREAMING_SNAKE_CASE   ) - 1:
																																			__lowerCamelCase      :      Dict         =[lines[index + 1]]
																																			index += 1
																												else:
																																			__lowerCamelCase      :      Union[str, Any]         =[]
																					else:
																												blocks.append('''\n'''.join(SCREAMING_SNAKE_CASE   )   )
																												__lowerCamelCase      :      Dict         =[lines[index]]
														else:
																					current_block.append(lines[index]   )
														index += 1

							# Adds current block if it's nonempty.
							if len(SCREAMING_SNAKE_CASE   ) > 0:
														blocks.append('''\n'''.join(SCREAMING_SNAKE_CASE   )   )

							# Add final block after end_prompt if provided.
							if end_prompt is not None and index < len(SCREAMING_SNAKE_CASE   ):
														blocks.append('''\n'''.join(lines[index:]   )   )

							return blocks



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							def _inner(SCREAMING_SNAKE_CASE       :							Any   ):
														return key(SCREAMING_SNAKE_CASE   ).lower().replace('''_'''    ,						''''''   )

							return _inner



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=None   ):


							'''simple docstring'''







							def noop(SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):
														return x

							if key is None:
														__lowerCamelCase      :      int         =noop
							# Constants are all uppercase, they go first.
							__lowerCamelCase      :      Optional[Any]         =[obj for obj in objects if key(SCREAMING_SNAKE_CASE   ).isupper()]
							# Classes are not all uppercase but start with a capital, they go second.
							__lowerCamelCase      :      List[str]         =[obj for obj in objects if key(SCREAMING_SNAKE_CASE   )[0].isupper() and not key(SCREAMING_SNAKE_CASE   ).isupper()]
							# Functions begin with a lowercase, they go last.
							__lowerCamelCase      :      Optional[Any]         =[obj for obj in objects if not key(SCREAMING_SNAKE_CASE   )[0].isupper()]

							__lowerCamelCase      :      str         =ignore_underscore(SCREAMING_SNAKE_CASE   )
							return sorted(SCREAMING_SNAKE_CASE    ,						key=SCREAMING_SNAKE_CASE   ) + sorted(SCREAMING_SNAKE_CASE    ,						key=SCREAMING_SNAKE_CASE   ) + sorted(SCREAMING_SNAKE_CASE    ,						key=SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							def _replace(SCREAMING_SNAKE_CASE       :							Optional[int]   ):
														__lowerCamelCase      :      Any         =match.groups()[0]
														if "," not in imports:
																					return F'[{imports}]'
														__lowerCamelCase      :      Dict         =[part.strip().replace('''"'''    ,						''''''   ) for part in imports.split(''','''   )]
														# We will have a final empty element if the line finished with a comma.
														if len(keys[-1]   ) == 0:
																					__lowerCamelCase      :      Optional[Any]         =keys[:-1]
														return "[" + ", ".join([F'"{k}"' for k in sort_objects(SCREAMING_SNAKE_CASE   )]   ) + "]"

							__lowerCamelCase      :      Optional[int]         =import_statement.split('''\n'''   )
							if len(SCREAMING_SNAKE_CASE   ) > 3:
														# Here we have to sort internal imports that are on several lines (one per name):
														# key: [
														#     "object1",
														#     "object2",
														#     ...
														# ]

														# We may have to ignore one or two lines on each side.
														__lowerCamelCase      :      List[Any]         =2 if lines[1].strip() == '''[''' else 1
														__lowerCamelCase      :      Optional[int]         =[(i, _re_strip_line.search(SCREAMING_SNAKE_CASE   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
														__lowerCamelCase      :      int         =sort_objects(SCREAMING_SNAKE_CASE    ,						key=lambda SCREAMING_SNAKE_CASE   : x[1]   )
														__lowerCamelCase      :      Dict         =[lines[x[0] + idx] for x in sorted_indices]
														return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
							elif len(SCREAMING_SNAKE_CASE   ) == 3:
														# Here we have to sort internal imports that are on one separate line:
														# key: [
														#     "object1", "object2", ...
														# ]
														if _re_bracket_content.search(lines[1]   ) is not None:
																					__lowerCamelCase      :      Union[str, Any]         =_re_bracket_content.sub(_replace    ,						lines[1]   )
														else:
																					__lowerCamelCase      :      List[Any]         =[part.strip().replace('''"'''    ,						''''''   ) for part in lines[1].split(''','''   )]
																					# We will have a final empty element if the line finished with a comma.
																					if len(keys[-1]   ) == 0:
																												__lowerCamelCase      :      Dict         =keys[:-1]
																					__lowerCamelCase      :      Any         =get_indent(lines[1]   ) + ''', '''.join([F'"{k}"' for k in sort_objects(SCREAMING_SNAKE_CASE   )]   )
														return "\n".join(SCREAMING_SNAKE_CASE   )
							else:
														# Finally we have to deal with imports fitting on one line
														__lowerCamelCase      :      Tuple         =_re_bracket_content.sub(_replace    ,						SCREAMING_SNAKE_CASE   )
														return import_statement



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]=True   ):


							'''simple docstring'''







							with open(SCREAMING_SNAKE_CASE    ,						'''r'''   ) as f:
														__lowerCamelCase      :      Any         =f.read()

							if "_import_structure" not in code:
														return

							# Blocks of indent level 0
							__lowerCamelCase      :      int         =split_code_in_indented_blocks(
							    SCREAMING_SNAKE_CASE    ,						start_prompt='''_import_structure = {'''    ,						end_prompt='''if TYPE_CHECKING:'''   )

							# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
							for block_idx in range(1    ,						len(SCREAMING_SNAKE_CASE   ) - 1   ):
														# Check if the block contains some `_import_structure`s thingy to sort.
														__lowerCamelCase      :      Tuple         =main_blocks[block_idx]
														__lowerCamelCase      :      Union[str, Any]         =block.split('''\n'''   )

														# Get to the start of the imports.
														__lowerCamelCase      :      Tuple         =0
														while line_idx < len(SCREAMING_SNAKE_CASE   ) and "_import_structure" not in block_lines[line_idx]:
																					# Skip dummy import blocks
																					if "import dummy" in block_lines[line_idx]:
																												__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
																					else:
																												line_idx += 1
														if line_idx >= len(SCREAMING_SNAKE_CASE   ):
																					continue

														# Ignore beginning and last line: they don't contain anything.
														__lowerCamelCase      :      List[Any]         ='''\n'''.join(block_lines[line_idx:-1]   )
														__lowerCamelCase      :      Dict         =get_indent(block_lines[1]   )
														# Slit the internal block into blocks of indent level 1.
														__lowerCamelCase      :      int         =split_code_in_indented_blocks(SCREAMING_SNAKE_CASE    ,						indent_level=SCREAMING_SNAKE_CASE   )
														# We have two categories of import key: list or _import_structure[key].append/extend
														__lowerCamelCase      :      Union[str, Any]         =_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
														# Grab the keys, but there is a trap: some lines are empty or just comments.
														__lowerCamelCase      :      str         =[(pattern.search(SCREAMING_SNAKE_CASE   ).groups()[0] if pattern.search(SCREAMING_SNAKE_CASE   ) is not None else None) for b in internal_blocks]
														# We only sort the lines with a key.
														__lowerCamelCase      :      Tuple         =[(i, key) for i, key in enumerate(SCREAMING_SNAKE_CASE   ) if key is not None]
														__lowerCamelCase      :      List[Any]         =[x[0] for x in sorted(SCREAMING_SNAKE_CASE    ,						key=lambda SCREAMING_SNAKE_CASE   : x[1]   )]

														# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
														__lowerCamelCase      :      List[Any]         =0
														__lowerCamelCase      :      int         =[]
														for i in range(len(SCREAMING_SNAKE_CASE   )   ):
																					if keys[i] is None:
																												reordered_blocks.append(internal_blocks[i]   )
																					else:
																												__lowerCamelCase      :      Union[str, Any]         =sort_objects_in_import(internal_blocks[sorted_indices[count]]   )
																												reordered_blocks.append(SCREAMING_SNAKE_CASE   )
																												count += 1

        # And we put our main block back together with its first and last line.
														__lowerCamelCase      :      List[Any]         ='''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]   )

							if code != "\n".join(SCREAMING_SNAKE_CASE   ):
														if check_only:
																					return True
														else:
																					print(F'Overwriting {file}.'   )
																					with open(SCREAMING_SNAKE_CASE    ,						'''w'''   ) as f:
																												f.write('''\n'''.join(SCREAMING_SNAKE_CASE   )   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple=True   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =[]
							for root, _, files in os.walk(SCREAMING_SNAKE_CASE   ):
														if "__init__.py" in files:
																					__lowerCamelCase      :      Dict         =sort_imports(os.path.join(SCREAMING_SNAKE_CASE    ,						'''__init__.py'''   )    ,						check_only=SCREAMING_SNAKE_CASE   )
																					if result:
																												__lowerCamelCase      :      Optional[int]         =[os.path.join(SCREAMING_SNAKE_CASE    ,						'''__init__.py'''   )]
							if len(SCREAMING_SNAKE_CASE   ) > 0:
														raise ValueError(F'Would overwrite {len(SCREAMING_SNAKE_CASE   )} files, run `make style`.'   )


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()
							parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
							_UpperCamelCase							=      parser.parse_args()

							sort_imports_in_all_inits(check_only=args.check_only)











"""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


_UpperCamelCase							=      logging.get_logger(__name__)

# General docstring
_UpperCamelCase							=      'RegNetConfig'

# Base docstring
_UpperCamelCase							=      'facebook/regnet-y-040'
_UpperCamelCase							=      [1, 1088, 7, 7]

# Image classification docstring
_UpperCamelCase							=      'facebook/regnet-y-040'
_UpperCamelCase							=      'tabby, tabby cat'

_UpperCamelCase							=      [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Dict						,    __lowercase  :int						,    __lowercase  :int = 3						,    __lowercase  :int = 1						,    __lowercase  :int = 1						,    __lowercase  :Optional[str] = "relu"						,    **__lowercase  :int						,    ):
											super().__init__(**__lowercase			)
											# The padding and conv has been verified in
											# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
											__lowerCamelCase      :      int         =tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2			)
											__lowerCamelCase      :      str         =tf.keras.layers.ConvaD(
											    filters=__lowercase						,    kernel_size=__lowercase						,    strides=__lowercase						,    padding='''VALID'''						,    groups=__lowercase						,    use_bias=__lowercase						,    name='''convolution'''						,    )
											__lowerCamelCase      :      str         =tf.keras.layers.BatchNormalization(epsilon=1e-5						,    momentum=0.9						,    name='''normalization'''			)
											__lowerCamelCase      :      Optional[int]         =ACTaFN[activation] if activation is not None else tf.identity




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :Any			):
											__lowerCamelCase      :      str         =self.convolution(self.padding(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =self.normalization(__lowercase			)
											__lowerCamelCase      :      Any         =self.activation(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    **__lowercase  :Any			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Tuple         =config.num_channels
											__lowerCamelCase      :      Union[str, Any]         =TFRegNetConvLayer(
											    out_channels=config.embedding_size						,    kernel_size=3						,    stride=2						,    activation=config.hidden_act						,    name='''embedder'''						,    )




				def 					__lowercase  (     self  :int						,    __lowercase  :List[str]			):
											__lowerCamelCase      :      int         =shape_list(__lowercase			)[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      :      Union[str, Any]         =tf.transpose(__lowercase						,    perm=(0, 2, 3, 1)			)
											__lowerCamelCase      :      Optional[int]         =self.embedder(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :List[Any]						,    __lowercase  :int						,    __lowercase  :int = 2						,    **__lowercase  :Optional[int]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      int         =tf.keras.layers.ConvaD(
											    filters=__lowercase						,    kernel_size=1						,    strides=__lowercase						,    use_bias=__lowercase						,    name='''convolution'''			)
											__lowerCamelCase      :      List[str]         =tf.keras.layers.BatchNormalization(epsilon=1e-5						,    momentum=0.9						,    name='''normalization'''			)




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :tf.Tensor						,    __lowercase  :bool = False			):
											return self.normalization(self.convolution(__lowercase			)						,    training=__lowercase			)

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Dict						,    __lowercase  :int						,    __lowercase  :int						,    **__lowercase  :List[str]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      int         =tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase						,    name='''pooler'''			)
											__lowerCamelCase      :      int         =[
											    tf.keras.layers.ConvaD(filters=__lowercase						,    kernel_size=1						,    activation='''relu'''						,    name='''attention.0'''			),
											    tf.keras.layers.ConvaD(filters=__lowercase						,    kernel_size=1						,    activation='''sigmoid'''						,    name='''attention.2'''			),
											]




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Union[str, Any]			):
											# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
											__lowerCamelCase      :      Any         =self.pooler(__lowercase			)
											for layer_module in self.attention:
																		__lowerCamelCase      :      Any         =layer_module(__lowercase			)
											__lowerCamelCase      :      Dict         =hidden_state * pooled
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Optional[int]						,    __lowercase  :RegNetConfig						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :int = 1						,    **__lowercase  :str			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =in_channels != out_channels or stride != 1
											__lowerCamelCase      :      int         =max(1						,    out_channels // config.groups_width			)
											__lowerCamelCase      :      List[str]         =(
											    TFRegNetShortCut(__lowercase						,    stride=__lowercase						,    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      :      str         =[
											    TFRegNetConvLayer(__lowercase						,    kernel_size=1						,    activation=config.hidden_act						,    name='''layer.0'''			),
											    TFRegNetConvLayer(
											        __lowercase						,    stride=__lowercase						,    groups=__lowercase						,    activation=config.hidden_act						,    name='''layer.1'''			),
											    TFRegNetConvLayer(__lowercase						,    kernel_size=1						,    activation=__lowercase						,    name='''layer.2'''			),
											]
											__lowerCamelCase      :      Optional[int]         =ACTaFN[config.hidden_act]




				def 					__lowercase  (     self  :int						,    __lowercase  :Optional[int]			):
											__lowerCamelCase      :      List[Any]         =hidden_state
											for layer_module in self.layers:
																		__lowerCamelCase      :      str         =layer_module(__lowercase			)
											__lowerCamelCase      :      List[Any]         =self.shortcut(__lowercase			)
											hidden_state += residual
											__lowerCamelCase      :      Optional[int]         =self.activation(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :int = 1						,    **__lowercase  :List[str]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Optional[Any]         =in_channels != out_channels or stride != 1
											__lowerCamelCase      :      Optional[Any]         =max(1						,    out_channels // config.groups_width			)
											__lowerCamelCase      :      Dict         =(
											    TFRegNetShortCut(__lowercase						,    stride=__lowercase						,    name='''shortcut'''			)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('''linear'''						,    name='''shortcut'''			)
											)
											__lowerCamelCase      :      Union[str, Any]         =[
											    TFRegNetConvLayer(__lowercase						,    kernel_size=1						,    activation=config.hidden_act						,    name='''layer.0'''			),
											    TFRegNetConvLayer(
											        __lowercase						,    stride=__lowercase						,    groups=__lowercase						,    activation=config.hidden_act						,    name='''layer.1'''			),
											    TFRegNetSELayer(__lowercase						,    reduced_channels=int(round(in_channels / 4			)			)						,    name='''layer.2'''			),
											    TFRegNetConvLayer(__lowercase						,    kernel_size=1						,    activation=__lowercase						,    name='''layer.3'''			),
											]
											__lowerCamelCase      :      Tuple         =ACTaFN[config.hidden_act]




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Tuple			):
											__lowerCamelCase      :      List[Any]         =hidden_state
											for layer_module in self.layers:
																		__lowerCamelCase      :      int         =layer_module(__lowercase			)
											__lowerCamelCase      :      List[str]         =self.shortcut(__lowercase			)
											hidden_state += residual
											__lowerCamelCase      :      List[str]         =self.activation(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :int						,    __lowercase  :RegNetConfig						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    **__lowercase  :Union[str, Any]			):
											super().__init__(**__lowercase			)

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




				def 					__lowercase  (     self  :int						,    __lowercase  :List[str]			):
											for layer_module in self.layers:
																		__lowerCamelCase      :      int         =layer_module(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :List[Any]						,    __lowercase  :RegNetConfig						,    **__lowercase  :List[str]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Optional[int]         =[]
											# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    TFRegNetStage(
											        __lowercase						,    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      :      Any         =zip(config.hidden_sizes						,    config.hidden_sizes[1:]			)
											for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowercase						,    config.depths[1:]			)			):
																		self.stages.append(TFRegNetStage(__lowercase						,    __lowercase						,    __lowercase						,    depth=__lowercase						,    name=f'stages.{i+1}'			)			)




				def 					__lowercase  (     self  :str						,    __lowercase  :tf.Tensor						,    __lowercase  :bool = False						,    __lowercase  :bool = True			):
											__lowerCamelCase      :      Optional[Any]         =() if output_hidden_states else None

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

																		__lowerCamelCase      :      List[Any]         =stage_module(__lowercase			)

											if output_hidden_states:
																		__lowerCamelCase      :      Union[str, Any]         =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=__lowercase						,    hidden_states=__lowercase			)

@keras_serializable
class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""
				__snake_case   :       Optional[int]	 =		RegNetConfig




				def __init__(     self  :List[Any]						,    __lowercase  :Dict						,    **__lowercase  :Union[str, Any]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      int         =config
											__lowerCamelCase      :      List[str]         =TFRegNetEmbeddings(__lowercase						,    name='''embedder'''			)
											__lowerCamelCase      :      List[str]         =TFRegNetEncoder(__lowercase						,    name='''encoder'''			)
											__lowerCamelCase      :      List[Any]         =tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase						,    name='''pooler'''			)




				@unpack_inputs
				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :tf.Tensor						,    __lowercase  :Optional[bool] = None						,    __lowercase  :Optional[bool] = None						,    __lowercase  :bool = False						,    ):
											__lowerCamelCase      :      Union[str, Any]         =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase      :      Tuple         =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase      :      Tuple         =self.embedder(__lowercase						,    training=__lowercase			)

											__lowerCamelCase      :      Optional[Any]         =self.encoder(
											    __lowercase						,    output_hidden_states=__lowercase						,    return_dict=__lowercase						,    training=__lowercase			)

											__lowerCamelCase      :      str         =encoder_outputs[0]
											__lowerCamelCase      :      Tuple         =self.pooler(__lowercase			)

											# Change to NCHW output format have uniformity in the modules
											__lowerCamelCase      :      int         =tf.transpose(__lowercase						,    perm=(0, 3, 1, 2)			)
											__lowerCamelCase      :      Any         =tf.transpose(__lowercase						,    perm=(0, 3, 1, 2)			)

											# Change the other hidden state outputs to NCHW as well
											if output_hidden_states:
																		__lowerCamelCase      :      str         =tuple([tf.transpose(__lowercase						,    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=__lowercase						,    pooler_output=__lowercase						,    hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states						,    )

class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[int]	 =		RegNetConfig
				__snake_case   :       int	 =		"""regnet"""
				__snake_case   :       int	 =		"""pixel_values"""




				@property
				def 					__lowercase  (     self  :List[str]			):
											return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)						,    dtype=tf.floataa			)}

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

_UpperCamelCase							=      r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""       ,  snake_case__       ,  )
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    *__lowercase  :List[str]						,    **__lowercase  :int			):
											super().__init__(__lowercase						,    *__lowercase						,    **__lowercase			)
											__lowerCamelCase      :      Tuple         =TFRegNetMainLayer(__lowercase						,    name='''regnet'''			)




				@unpack_inputs
				@add_start_docstrings_to_model_forward(__lowercase			)
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC						,    output_type=__lowercase						,    config_class=_CONFIG_FOR_DOC						,    modality='''vision'''						,    expected_output=_EXPECTED_OUTPUT_SHAPE						,    )
				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :tf.Tensor						,    __lowercase  :Optional[bool] = None						,    __lowercase  :Optional[bool] = None						,    __lowercase  :Optional[int]=False						,    ):
											__lowerCamelCase      :      List[Any]         =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase      :      Optional[int]         =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase      :      Dict         =self.regnet(
											    pixel_values=__lowercase						,    output_hidden_states=__lowercase						,    return_dict=__lowercase						,    training=__lowercase						,    )
											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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """       ,  snake_case__       ,  )
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    *__lowercase  :List[Any]						,    **__lowercase  :Dict			):
											super().__init__(__lowercase						,    *__lowercase						,    **__lowercase			)
											__lowerCamelCase      :      Optional[int]         =config.num_labels
											__lowerCamelCase      :      Optional[int]         =TFRegNetMainLayer(__lowercase						,    name='''regnet'''			)
											# classification head
											__lowerCamelCase      :      Union[str, Any]         =[
											    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(__lowercase			)
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT						,    output_type=__lowercase						,    config_class=_CONFIG_FOR_DOC						,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,    )
				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :tf.Tensor = None						,    __lowercase  :tf.Tensor = None						,    __lowercase  :bool = None						,    __lowercase  :bool = None						,    __lowercase  :int=False						,    ):
											__lowerCamelCase      :      str         =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase      :      Optional[int]         =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase      :      str         =self.regnet(
											    __lowercase						,    output_hidden_states=__lowercase						,    return_dict=__lowercase						,    training=__lowercase			)

											__lowerCamelCase      :      Any         =outputs.pooler_output if return_dict else outputs[1]

											__lowerCamelCase      :      List[str]         =self.classifier[0](__lowercase			)
											__lowerCamelCase      :      str         =self.classifier[1](__lowercase			)

											__lowerCamelCase      :      str         =None if labels is None else self.hf_compute_loss(labels=__lowercase						,    logits=__lowercase			)

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

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




