






from abc import ABC, abstractmethod
from typing import List, Optional



class 				SCREAMING_SNAKE_CASE__		(							snake_case_		):





					"""simple docstring"""






					def __init__(   self	:    List[Any]			):
						self.test()



					def 			_lowercase	(   self	:    List[Any]			):
						snake_case__					: List[Any]  =    0
						snake_case__					: Union[str, Any]  =    False
						while not completed:
							if counter == 1:
								self.reset()
							snake_case__					: int  =    self.advance()
							if not self.does_advance(A_			):
								raise Exception(
								    "Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."			)

							snake_case__,		snake_case__,		snake_case__					: List[Any]  =    self.update(A_			)
							counter += 1

							if counter > 1_0_0_0_0:
								raise Exception("update() does not fulfill the constraint."			)

						if self.remaining() != 0:
							raise Exception("Custom Constraint is not defined correctly."			)



					@abstractmethod
					def 			_lowercase	(   self	:    str			):
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''			)



					@abstractmethod
					def 			_lowercase	(   self	:    int				,						__A	:    Dict			):
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''			)



					@abstractmethod
					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    Any			):
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''			)



					@abstractmethod
					def 			_lowercase	(   self	:    Dict			):
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''			)



					@abstractmethod
					def 			_lowercase	(   self	:    Tuple			):
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''			)






					@abstractmethod
					def 			_lowercase	(   self	:    List[str]				,						__A	:    Optional[int]=False			):
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''			)



class 				SCREAMING_SNAKE_CASE__		(							snake_case_		):





					"""simple docstring"""






					def __init__(   self	:    List[Any]				,						__A	:    Optional[int]			):
						super(A_				,						self			).__init__()

						if not isinstance(A_				,						A_			) or len(A_			) == 0:
							raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.'''			)
						if any((not isinstance(A_				,						A_			) or token_id < 0) for token_id in token_ids			):
							raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''			)

						snake_case__					: Union[str, Any]  =    token_ids

						snake_case__					: Optional[Any]  =    len(self.token_ids			)
						snake_case__					: str  =    -1  # the index of the currently fulfilled step
						snake_case__					: int  =    False



					def 			_lowercase	(   self	:    int			):






						if self.completed:
							return None
						return self.token_ids[self.fulfilled_idx + 1]



					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    str			):






						if not isinstance(A_				,						A_			):
							raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(A_			)}'''			)

						if self.completed:
							return False

						return token_id == self.token_ids[self.fulfilled_idx + 1]



					def 			_lowercase	(   self	:    Tuple				,						__A	:    Dict			):






						if not isinstance(A_				,						A_			):
							raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(A_			)}'''			)

						snake_case__					: List[str]  =    False
						snake_case__					: Dict  =    False
						snake_case__					: List[str]  =    False

						if self.does_advance(A_			):
							self.fulfilled_idx += 1
							snake_case__					: int  =    True
							if self.fulfilled_idx == (self.seqlen - 1):
								snake_case__					: Optional[int]  =    True
							snake_case__					: Any  =    completed
						else:
							# failed to make progress.
							snake_case__					: Any  =    True
							self.reset()
						return stepped, completed, reset



					def 			_lowercase	(   self	:    Optional[Any]			):
						snake_case__					: List[Any]  =    False
						snake_case__					: List[Any]  =    0



					def 			_lowercase	(   self	:    List[str]			):
						return self.seqlen - (self.fulfilled_idx + 1)






					def 			_lowercase	(   self	:    str				,						__A	:    Tuple=False			):
						snake_case__					: int  =    PhrasalConstraint(self.token_ids			)

						if stateful:
							snake_case__					: Dict  =    self.seqlen
							snake_case__					: Dict  =    self.fulfilled_idx
							snake_case__					: Tuple  =    self.completed

						return new_constraint



class 				SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(   self	:    Union[str, Any]				,						__A	:    Union[str, Any]				,						__A	:    Dict=True			):
						snake_case__					: Optional[Any]  =    max([len(A_			) for one in nested_token_ids]			)

						snake_case__					: int  =    {}
						for token_ids in nested_token_ids:
							snake_case__					: List[Any]  =    root
							for tidx, token_id in enumerate(A_			):
								if token_id not in level:
									snake_case__					: int  =    {}

								snake_case__					: Optional[int]  =    level[token_id]

						if no_subsets and self.has_subsets(A_				,						A_			):
							raise ValueError(
							    "Each list in `nested_token_ids` can\'t be a complete subset of another list, but is"
							    f''' {nested_token_ids}.'''			)

						snake_case__					: Optional[Any]  =    root



					def 			_lowercase	(   self	:    List[str]				,						__A	:    str			):
						snake_case__					: str  =    self.trie

						for current_token in current_seq:
							snake_case__					: Union[str, Any]  =    start[current_token]

						snake_case__					: Optional[int]  =    list(start.keys()			)

						return next_tokens



					def 			_lowercase	(   self	:    List[Any]				,						__A	:    Optional[Any]			):
						snake_case__					: List[str]  =    self.next_tokens(A_			)

						return len(A_			) == 0



					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    Dict			):
						snake_case__					: Tuple  =    list(root.values()			)
						if len(A_			) == 0:
							return 1
						else:
							return sum([self.count_leaves(A_			) for nn in next_nodes]			)






					def 			_lowercase	(   self	:    Tuple				,						__A	:    str				,						__A	:    Dict			):
						snake_case__					: int  =    self.count_leaves(A_			)
						return len(A_			) != leaf_count



class 				SCREAMING_SNAKE_CASE__		(							snake_case_		):





					"""simple docstring"""






					def __init__(   self	:    Dict				,						__A	:    Tuple			):
						super(A_				,						self			).__init__()

						if not isinstance(A_				,						A_			) or len(A_			) == 0:
							raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''			)
						if any(not isinstance(A_				,						A_			) for token_ids in nested_token_ids			):
							raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''			)
						if any(
						    any((not isinstance(A_				,						A_			) or token_id < 0) for token_id in token_ids			)
						    for token_ids in nested_token_ids			):
							raise ValueError(
							    f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''			)

						snake_case__					: Any  =    DisjunctiveTrie(A_			)
						snake_case__					: Optional[Any]  =    nested_token_ids

						snake_case__					: Tuple  =    self.trie.max_height
						snake_case__					: Union[str, Any]  =    []
						snake_case__					: int  =    False



					def 			_lowercase	(   self	:    Union[str, Any]			):
						snake_case__					: Optional[int]  =    self.trie.next_tokens(self.current_seq			)

						if len(A_			) == 0:
							return None
						else:
							return token_list



					def 			_lowercase	(   self	:    Any				,						__A	:    List[Any]			):






						if not isinstance(A_				,						A_			):
							raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_			)}'''			)

						snake_case__					: str  =    self.trie.next_tokens(self.current_seq			)

						return token_id in next_tokens



					def 			_lowercase	(   self	:    List[str]				,						__A	:    List[str]			):






						if not isinstance(A_				,						A_			):
							raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_			)}'''			)

						snake_case__					: Dict  =    False
						snake_case__					: Optional[Any]  =    False
						snake_case__					: Any  =    False

						if self.does_advance(A_			):
							self.current_seq.append(A_			)
							snake_case__					: Optional[Any]  =    True
						else:
							snake_case__					: Any  =    True
							self.reset()

						snake_case__					: str  =    self.trie.reached_leaf(self.current_seq			)
						snake_case__					: Dict  =    completed

						return stepped, completed, reset



					def 			_lowercase	(   self	:    str			):
						snake_case__					: List[Any]  =    False
						snake_case__					: Optional[Any]  =    []



					def 			_lowercase	(   self	:    Any			):






						if self.completed:
							# since this can be completed without reaching max height
							return 0
						else:
							return self.seqlen - len(self.current_seq			)






					def 			_lowercase	(   self	:    List[Any]				,						__A	:    Optional[int]=False			):
						snake_case__					: Tuple  =    DisjunctiveConstraint(self.token_ids			)

						if stateful:
							snake_case__					: Dict  =    self.seqlen
							snake_case__					: Tuple  =    self.current_seq
							snake_case__					: List[Any]  =    self.completed

						return new_constraint



class 				SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(   self	:    Optional[int]				,						__A	:    List[str]			):
						snake_case__					: List[str]  =    constraints

						# max # of steps required to fulfill a given constraint
						snake_case__					: Any  =    max([c.seqlen for c in constraints]			)
						snake_case__					: Optional[int]  =    len(A_			)
						snake_case__					: Any  =    False

						self.init_state()



					def 			_lowercase	(   self	:    Optional[int]			):
						snake_case__					: Optional[Any]  =    []
						snake_case__					: Tuple  =    None
						snake_case__					: Any  =    [constraint.copy(stateful=A_			) for constraint in self.constraints]



					def 			_lowercase	(   self	:    Any			):
						snake_case__					: Dict  =    0
						if self.inprogress_constraint:
							# extra points for having a constraint mid-fulfilled
							add += self.max_seqlen - self.inprogress_constraint.remaining()

						return (len(self.complete_constraints			) * self.max_seqlen) + add



					def 			_lowercase	(   self	:    Union[str, Any]			):
						snake_case__					: int  =    []
						if self.inprogress_constraint is None:
							for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
								snake_case__					: int  =    constraint.advance()
								if isinstance(A_				,						A_			):
									token_list.append(A_			)
								elif isinstance(A_				,						A_			):
									token_list.extend(A_			)
						else:
							snake_case__					: List[str]  =    self.inprogress_constraint.advance()
							if isinstance(A_				,						A_			):
								token_list.append(A_			)
							elif isinstance(A_				,						A_			):
								token_list.extend(A_			)

						if len(A_			) == 0:
							return None
						else:
							return token_list



					def 			_lowercase	(   self	:    int				,						__A	:    List[Any]			):
						self.init_state()

						if token_ids is not None:
							for token in token_ids:
								# completes or steps **one** constraint
								snake_case__,		snake_case__					: List[str]  =    self.add(A_			)

								# the entire list of constraints are fulfilled
								if self.completed:
									break



					def 			_lowercase	(   self	:    Any				,						__A	:    str			):






						if not isinstance(A_				,						A_			):
							raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.'''			)

						snake_case__,		snake_case__					: int  =    False, False

						if self.completed:
							snake_case__					: List[str]  =    True
							snake_case__					: Any  =    False
							return complete, stepped

						if self.inprogress_constraint is not None:
							# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
							# job, simply update the state

							snake_case__,		snake_case__,		snake_case__					: Optional[Any]  =    self.inprogress_constraint.update(A_			)
							if reset:
								# 1. If the next token breaks the progress, then we must restart.
								#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

								#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
								#     constraint, not the full list of constraints.

								self.pending_constraints.append(self.inprogress_constraint.copy(stateful=A_			)			)
								snake_case__					: List[str]  =    None

							if complete:
								# 2. If the next token completes the constraint, move it to completed list, set
								#     inprogress to None. If there are no pending constraints either, then this full list of constraints
								#     is complete.

								self.complete_constraints.append(self.inprogress_constraint			)
								snake_case__					: str  =    None

								if len(self.pending_constraints			) == 0:
									# we're done!
									snake_case__					: Any  =    True

						else:
							# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
							# of constraints?

							for cidx, pending_constraint in enumerate(self.pending_constraints			):
								if pending_constraint.does_advance(A_			):
									snake_case__,		snake_case__,		snake_case__					: List[Any]  =    pending_constraint.update(A_			)

									if not stepped:
										raise Exception(
										    "`constraint.update(token_id)` is not yielding incremental progress, "
										    "even though `constraint.does_advance(token_id)` is true."			)

									if complete:
										self.complete_constraints.append(A_			)
										snake_case__					: str  =    None

									if not complete and stepped:
										snake_case__					: Union[str, Any]  =    pending_constraint

									if complete or stepped:
										# If we made any progress at all, then it's at least not a "pending constraint".

										snake_case__					: Optional[Any]  =    (
										    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
										)

										if len(self.pending_constraints			) == 0 and self.inprogress_constraint is None:
											# If there's no longer any pending after this and no inprogress either, then we must be
											# complete.

											snake_case__					: str  =    True

										break  # prevent accidentally stepping through multiple constraints with just one token.

						return complete, stepped






					def 			_lowercase	(   self	:    int				,						__A	:    str=True			):
						snake_case__					: Optional[int]  =    ConstraintListState(self.constraints			)  # we actually never though self.constraints objects
						# throughout this process. So it's at initialization state.

						if stateful:
							snake_case__					: int  =    [
							    constraint.copy(stateful=A_			) for constraint in self.complete_constraints
							]
							if self.inprogress_constraint is not None:
								snake_case__					: Tuple  =    self.inprogress_constraint.copy(stateful=A_			)
							snake_case__					: Tuple  =    [constraint.copy() for constraint in self.pending_constraints]

						return new_state





'''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


lowerCAmelCase					:    Dict           = logging.get_logger(__name__)

# General docstring
lowerCAmelCase					:    str           = 'RegNetConfig'

# Base docstring
lowerCAmelCase					:    str           = 'facebook/regnet-y-040'
lowerCAmelCase					:    Dict           = [1, 10_88, 7, 7]

# Image classification docstring
lowerCAmelCase					:    Dict           = 'facebook/regnet-y-040'
lowerCAmelCase					:    int           = 'tabby, tabby cat'

lowerCAmelCase					:    int           = [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]


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







				def __init__(       self						,				A_						,				A_ = 3						,				A_ = 1						,				A_ = 1						,				A_ = "relu"						,				**A_						,				)->		str:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.convolution(self.padding(A_     )     )
											UpperCamelCase   			=     self.normalization(A_     )
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     config.num_channels
											UpperCamelCase   			=     TFRegNetConvLayer(
											    out_channels=config.embedding_size						,				kernel_size=3						,				stride=2						,				activation=config.hidden_act						,				name='embedder'						,				)







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     shape_list(A_     )[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)
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 2, 3, 1)     )
											UpperCamelCase   			=     self.embedder(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_ = 2						,				**A_     )->		List[Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False     )->		tf.Tensor:







											'''simple docstring'''






											return self.normalization(self.convolution(A_     )						,				training=A_     )


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







				def __init__(       self						,				A_						,				A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.pooler(A_     )
											for layer_module in self.attention:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     hidden_state * pooled
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 1						,				**A_     )->		Dict:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     in_channels != out_channels or stride != 1
											UpperCamelCase   			=     max(1						,				out_channels // config.groups_width     )
											UpperCamelCase   			=     (
											    TFRegNetShortCut(A_						,				stride=A_						,				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.
											UpperCamelCase   			=     [
											    TFRegNetConvLayer(A_						,				kernel_size=1						,				activation=config.hidden_act						,				name='layer.0'     ),
											    TFRegNetConvLayer(
											        A_						,				stride=A_						,				groups=A_						,				activation=config.hidden_act						,				name='layer.1'     ),
											    TFRegNetConvLayer(A_						,				kernel_size=1						,				activation=A_						,				name='layer.2'     ),
											]
											UpperCamelCase   			=     ACTaFN[config.hidden_act]







				def 						UpperCAmelCase_    (       self						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     hidden_state
											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     self.shortcut(A_     )
											hidden_state += residual
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 1						,				**A_     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     hidden_state
											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     self.shortcut(A_     )
											hidden_state += residual
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 2						,				A_ = 2						,				**A_     )->		Dict:







											'''simple docstring'''






											super().__init__(**A_     )

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







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				**A_     )->		str:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False						,				A_ = True     )->		TFBaseModelOutputWithNoAttention:







											'''simple docstring'''






											UpperCamelCase   			=     () if output_hidden_states else None

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

																		UpperCamelCase   			=     stage_module(A_     )

											if output_hidden_states:
																		UpperCamelCase   			=     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=A_						,				hidden_states=A_     )


@keras_serializable
class 			SCREAMING_SNAKE_CASE__      (     tf.keras.layers.Layer):
				lowerCAmelCase_     			=							RegNetConfig







				def __init__(       self						,				A_						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     config
											UpperCamelCase   			=     TFRegNetEmbeddings(A_						,				name='embedder'     )
											UpperCamelCase   			=     TFRegNetEncoder(A_						,				name='encoder'     )
											UpperCamelCase   			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_						,				name='pooler'     )







				@unpack_inputs
				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = None						,				A_ = False						,				)->		TFBaseModelOutputWithPoolingAndNoAttention:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

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

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

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

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

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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							RegNetConfig
				lowerCAmelCase_     			=							"""regnet"""
				lowerCAmelCase_     			=							"""pixel_values"""







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






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


lowerCAmelCase					:    str           = 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'

lowerCAmelCase					:    List[str]           = 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_):







				def __init__(       self						,				A_						,				*A_						,				**A_     )->		List[Any]:







											'''simple docstring'''






											super().__init__(A_						,				*A_						,				**A_     )
											UpperCamelCase   			=     TFRegNetMainLayer(A_						,				name='regnet'     )







				@unpack_inputs
				@add_start_docstrings_to_model_forward(A_     )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC						,				output_type=A_						,				config_class=_CONFIG_FOR_DOC						,				modality='vision'						,				expected_output=_EXPECTED_OUTPUT_SHAPE						,				)
				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = None						,				A_=False						,				)->		Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     self.regnet(
											    pixel_values=A_						,				output_hidden_states=A_						,				return_dict=A_						,				training=A_						,				)
											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_):







				def __init__(       self						,				A_						,				*A_						,				**A_     )->		str:







											'''simple docstring'''






											super().__init__(A_						,				*A_						,				**A_     )
											UpperCamelCase   			=     config.num_labels
											UpperCamelCase   			=     TFRegNetMainLayer(A_						,				name='regnet'     )
											# classification head
											UpperCamelCase   			=     [
											    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(A_     )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT						,				output_type=A_						,				config_class=_CONFIG_FOR_DOC						,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,				)
				def 						UpperCAmelCase_    (       self						,				A_ = None						,				A_ = None						,				A_ = None						,				A_ = None						,				A_=False						,				)->		Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

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

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

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

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

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

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




