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 					lowercase__						:


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Union[str, Any]=13    ,		UpperCamelCase__	:				List[Any]=30    ,		UpperCamelCase__	:				Dict=2    ,		UpperCamelCase__	:				Any=3    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Optional[int]=32    ,		UpperCamelCase__	:				List[str]=2    ,		UpperCamelCase__	:				Optional[Any]=4    ,		UpperCamelCase__	:				List[str]=37    ,		UpperCamelCase__	:				Optional[int]="gelu"    ,		UpperCamelCase__	:				List[Any]=0.1    ,		UpperCamelCase__	:				Dict=0.1    ,		UpperCamelCase__	:				List[Any]=10    ,		UpperCamelCase__	:				str=0.02    ,		UpperCamelCase__	:				Dict=3    ,		UpperCamelCase__	:				int=None    ,		UpperCamelCase__	:				Optional[Any]=2    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parent
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							batch_size
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image_size
												SCREAMING_SNAKE_CASE							:    Dict							=							patch_size
												SCREAMING_SNAKE_CASE							:    List[str]							=							num_channels
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							is_training
												SCREAMING_SNAKE_CASE							:    Dict							=							use_labels
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hidden_size
												SCREAMING_SNAKE_CASE							:    str							=							num_hidden_layers
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							num_attention_heads
												SCREAMING_SNAKE_CASE							:    str							=							intermediate_size
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Tuple							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    List[Any]							=							type_sequence_label_size
												SCREAMING_SNAKE_CASE							:    int							=							initializer_range
												SCREAMING_SNAKE_CASE							:    Any							=							scope
												SCREAMING_SNAKE_CASE							:    List[str]							=							encoder_stride

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


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

												SCREAMING_SNAKE_CASE							:    Dict							=							None
												if self.use_labels:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_config()

												return config, pixel_values, labels


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												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=UpperCamelCase__    ,		initializer_range=self.initializer_range    ,		encoder_stride=self.encoder_stride    ,		)


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							TFDeiTModel(config=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__						)
												self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							TFDeiTForMaskedImageModeling(config=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							model(UpperCamelCase__						)
												self.parent.assertEqual(
												    result.reconstruction.shape    ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

												# test greyscale images
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							1
												SCREAMING_SNAKE_CASE							:    Dict							=							TFDeiTForMaskedImageModeling(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model(UpperCamelCase__						)
												self.parent.assertEqual(result.reconstruction.shape    ,		(self.batch_size, 1, self.image_size, self.image_size)						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self.type_sequence_label_size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFDeiTForImageClassification(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						)
												self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)						)

												# test greyscale images
												SCREAMING_SNAKE_CASE							:    str							=							1
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TFDeiTForImageClassification(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    str							=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						)
												self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.type_sequence_label_size)						)



						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							config_and_inputs
												SCREAMING_SNAKE_CASE							:    Tuple							=							{'''pixel_values''': pixel_values}
												return config, inputs_dict






@require_tf
class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		(
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCamelCase_     =		(
						    {
						        """feature-extraction""": TFDeiTModel,
						        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCamelCase_     =		False
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TFDeiTModelTester(self						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							ConfigTester(self    ,		config_class=UpperCamelCase__    ,		has_text_modality=UpperCamelCase__    ,		hidden_size=37						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												self.config_tester.run_common_tests()


						@unittest.skip(reason='''DeiT does not use inputs_embeds'''						)
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model_class(UpperCamelCase__						)
																		self.assertIsInstance(model.get_input_embeddings()    ,		(tf.keras.layers.Layer)						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(UpperCamelCase__    ,		tf.keras.layers.Dense						)						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							['''pixel_values''']
																		self.assertListEqual(arg_names[:1]    ,		UpperCamelCase__						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase__						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__						)


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				Dict    ,		UpperCamelCase__	:				str=False						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							super()._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__    ,		return_labels=UpperCamelCase__						)

												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        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							TFDeiTModel.from_pretrained(UpperCamelCase__						)
																		self.assertIsNotNone(UpperCamelCase__						)






def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
						return image





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


						@cached_property
						def        __A       (       self	:				str						):





												'''simple docstring'''





												return (
												    DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''						)
												    if is_vision_available()
												    else None
												)



						@slow
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''						)

												SCREAMING_SNAKE_CASE							:    str							=							self.default_image_processor
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							prepare_img()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							image_processor(images=UpperCamelCase__    ,		return_tensors='''tf'''						)

												# forward pass
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							model(**UpperCamelCase__						)

												# verify the logits
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tf.TensorShape((1, 1000)						)
												self.assertEqual(outputs.logits.shape    ,		UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Any							=							tf.constant([-1.0266, 0.1912, -1.2861]						)

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






import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





