"""simple docstring"""




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

class       a_      (     _SCREAMING_SNAKE_CASE			):




					def __init__(	self						:     Optional[int]     ):
							self.test()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   False
							while not completed:
									if counter == 1:
											self.reset()
									lowerCAmelCase__        =   self.advance()
									if not self.does_advance(A_     ):
											raise Exception(
											    """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."""     )

									lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self.update(A_     )
									counter += 1

									if counter > 10000:
											raise Exception("""update() does not fulfill the constraint."""     )

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




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




					@abstractmethod
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     str     ):
							raise NotImplementedError(
							    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""     )




					@abstractmethod
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     int     ):
							raise NotImplementedError(
							    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""     )




					@abstractmethod
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							raise NotImplementedError(
							    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""     )




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




					@abstractmethod
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     int=False     ):
							raise NotImplementedError(
							    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""     )

class       a_      (     _SCREAMING_SNAKE_CASE			):




					def __init__(	self						:     List[str]  ,						snake_case__						:     Optional[Any]     ):
							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}."""     )

							lowerCAmelCase__        =   token_ids

							lowerCAmelCase__        =   len(self.token_ids     )
							lowerCAmelCase__        =   -1  # the index of the currently fulfilled step
							lowerCAmelCase__        =   False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							if self.completed:
									return None
							return self.token_ids[self.fulfilled_idx + 1]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Union[str, Any]     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Optional[int]     ):
							if not isinstance(A_  ,						A_     ):
									raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(A_     )}"""     )

							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   False

							if self.does_advance(A_     ):
									self.fulfilled_idx += 1
									lowerCAmelCase__        =   True
									if self.fulfilled_idx == (self.seqlen - 1):
											lowerCAmelCase__        =   True
									lowerCAmelCase__        =   completed
							else:
									# failed to make progress.
									lowerCAmelCase__        =   True
									self.reset()
							return stepped, completed, reset




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return self.seqlen - (self.fulfilled_idx + 1)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]=False     ):
							lowerCAmelCase__        =   PhrasalConstraint(self.token_ids     )

							if stateful:
									lowerCAmelCase__        =   self.seqlen
									lowerCAmelCase__        =   self.fulfilled_idx
									lowerCAmelCase__        =   self.completed

							return new_constraint

class       a_      :




					def __init__(	self						:     List[str]  ,						snake_case__						:     List[str]  ,						snake_case__						:     Optional[int]=True     ):
							lowerCAmelCase__        =   max([len(A_     ) for one in nested_token_ids]     )

							lowerCAmelCase__        =   {}
							for token_ids in nested_token_ids:
									lowerCAmelCase__        =   root
									for tidx, token_id in enumerate(A_     ):
											if token_id not in level:
													lowerCAmelCase__        =   {}

											lowerCAmelCase__        =   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}."""     )

							lowerCAmelCase__        =   root




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.trie

							for current_token in current_seq:
									lowerCAmelCase__        =   start[current_token]

							lowerCAmelCase__        =   list(start.keys()     )

							return next_tokens




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   self.next_tokens(A_     )

							return len(A_     ) == 0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   list(root.values()     )
							if len(A_     ) == 0:
									return 1
							else:
									return sum([self.count_leaves(A_     ) for nn in next_nodes]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   self.count_leaves(A_     )
							return len(A_     ) != leaf_count

class       a_      (     _SCREAMING_SNAKE_CASE			):




					def __init__(	self						:     int  ,						snake_case__						:     str     ):
							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}."""     )

							lowerCAmelCase__        =   DisjunctiveTrie(A_     )
							lowerCAmelCase__        =   nested_token_ids

							lowerCAmelCase__        =   self.trie.max_height
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.trie.next_tokens(self.current_seq     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     int     ):
							if not isinstance(A_  ,						A_     ):
									raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_     )}"""     )

							lowerCAmelCase__        =   self.trie.next_tokens(self.current_seq     )

							return token_id in next_tokens




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     int     ):
							if not isinstance(A_  ,						A_     ):
									raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_     )}"""     )

							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   False

							if self.does_advance(A_     ):
									self.current_seq.append(A_     )
									lowerCAmelCase__        =   True
							else:
									lowerCAmelCase__        =   True
									self.reset()

							lowerCAmelCase__        =   self.trie.reached_leaf(self.current_seq     )
							lowerCAmelCase__        =   completed

							return stepped, completed, reset




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   []




					def 						_SCREAMING_SNAKE_CASE						(	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 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Dict=False     ):
							lowerCAmelCase__        =   DisjunctiveConstraint(self.token_ids     )

							if stateful:
									lowerCAmelCase__        =   self.seqlen
									lowerCAmelCase__        =   self.current_seq
									lowerCAmelCase__        =   self.completed

							return new_constraint

class       a_      :




					def __init__(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   constraints

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

							self.init_state()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   [constraint.copy(stateful=A_     ) for constraint in self.constraints]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   []
							if self.inprogress_constraint is None:
									for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
											lowerCAmelCase__        =   constraint.advance()
											if isinstance(A_  ,						A_     ):
													token_list.append(A_     )
											elif isinstance(A_  ,						A_     ):
													token_list.extend(A_     )
							else:
									lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Tuple     ):
							self.init_state()

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     str     ):
							if not isinstance(A_  ,						A_     ):
									raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`."""     )

							lowerCAmelCase__   ,					lowerCAmelCase__        =   False, False

							if self.completed:
									lowerCAmelCase__        =   True
									lowerCAmelCase__        =   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

									lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   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_     )     )
											lowerCAmelCase__        =   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     )
											lowerCAmelCase__        =   None

											if len(self.pending_constraints     ) == 0:
													# we're done!
													lowerCAmelCase__        =   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_     ):
													lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   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_     )
															lowerCAmelCase__        =   None

													if not complete and stepped:
															lowerCAmelCase__        =   pending_constraint

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

															lowerCAmelCase__        =   (
															    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.

																	lowerCAmelCase__        =   True

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

							return complete, stepped




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Tuple=True     ):
							lowerCAmelCase__        =   ConstraintListState(self.constraints     )  # we actually never though self.constraints objects
							# throughout this process. So it's at initialization state.

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

							return new_state





"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFDeiTForImageClassification,
			    TFDeiTForImageClassificationWithTeacher,
			    TFDeiTForMaskedImageModeling,
			    TFDeiTModel,
			)
			from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Any=13  ,						snake_case__						:     int=30  ,						snake_case__						:     int=2  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     Dict=True  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     List[Any]=32  ,						snake_case__						:     List[str]=2  ,						snake_case__						:     Optional[Any]=4  ,						snake_case__						:     Optional[int]=37  ,						snake_case__						:     Tuple="gelu"  ,						snake_case__						:     str=0.1  ,						snake_case__						:     Any=0.1  ,						snake_case__						:     int=10  ,						snake_case__						:     Dict=0.02  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     str=None  ,						snake_case__						:     List[Any]=2  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   image_size
							lowerCAmelCase__        =   patch_size
							lowerCAmelCase__        =   num_channels
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   type_sequence_label_size
							lowerCAmelCase__        =   initializer_range
							lowerCAmelCase__        =   scope
							lowerCAmelCase__        =   encoder_stride

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							lowerCAmelCase__        =   (image_size // patch_size) ** 2
							lowerCAmelCase__        =   num_patches + 2




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

							lowerCAmelCase__        =   None
							if self.use_labels:
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.type_sequence_label_size     )

							lowerCAmelCase__        =   self.get_config()

							return config, pixel_values, labels




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return DeiTConfig(
							    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						hidden_size=self.hidden_size  ,						num_hidden_layers=self.num_hidden_layers  ,						num_attention_heads=self.num_attention_heads  ,						intermediate_size=self.intermediate_size  ,						hidden_act=self.hidden_act  ,						hidden_dropout_prob=self.hidden_dropout_prob  ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,						is_decoder=snake_case__  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     List[str]     ):
							lowerCAmelCase__        =   TFDeiTModel(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(config=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(
							    result.reconstruction.shape  ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForMaskedImageModeling(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.reconstruction.shape  ,						(self.batch_size, 1, self.image_size, self.image_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   self.type_sequence_label_size
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )

							# test greyscale images
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   TFDeiTForImageClassification(snake_case__     )

							lowerCAmelCase__        =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							lowerCAmelCase__        =   model(snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   config_and_inputs
							lowerCAmelCase__        =   {"""pixel_values""": pixel_values}
							return config, inputs_dict

@require_tf
class       a_      (     __UpperCamelCase ,		__UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    (
					    (
					        TFDeiTModel,
					        TFDeiTForImageClassification,
					        TFDeiTForImageClassificationWithTeacher,
					        TFDeiTForMaskedImageModeling,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase_ :	Any			=    (
					    {
					        "feature-extraction": TFDeiTModel,
					        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
					    }
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	Optional[int]			=    False
					UpperCamelCase_ :	int			=    False




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   TFDeiTModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						has_text_modality=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DeiT does not use inputs_embeds"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )
									self.assertIsInstance(model.get_input_embeddings()  ,						(tf.keras.layers.Layer)     )
									lowerCAmelCase__        =   model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case__  ,						tf.keras.layers.Dense     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )
									lowerCAmelCase__        =   inspect.signature(model.call     )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowerCAmelCase__        =   [*signature.parameters.keys()]

									lowerCAmelCase__        =   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						snake_case__						:     Union[str, Any]=False     ):
							lowerCAmelCase__        =   super()._prepare_for_class(snake_case__  ,						snake_case__  ,						return_labels=snake_case__     )

							if return_labels:
									if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call     ).parameters:
											del inputs_dict["labels"]

							return inputs_dict




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   TFDeiTModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
		return image






@require_tf
@require_vision
class       a_      (     unittest.TestCase			):




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							return (
							    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )
							    if is_vision_available()
							    else None
							)




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""     )

							lowerCAmelCase__        =   self.default_image_processor
							lowerCAmelCase__        =   prepare_img()
							lowerCAmelCase__        =   image_processor(images=snake_case__  ,						return_tensors="""tf"""     )

							# forward pass
							lowerCAmelCase__        =   model(**snake_case__     )

							# verify the logits
							lowerCAmelCase__        =   tf.TensorShape((1, 1000)     )
							self.assertEqual(outputs.logits.shape  ,						snake_case__     )

							lowerCAmelCase__        =   tf.constant([-1.0266, 0.1912, -1.2861]     )

							self.assertTrue(np.allclose(outputs.logits[0, :3]  ,						snake_case__  ,						atol=1E-4     )     )




