
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__A				=logging.get_logger(__name__)

__A				={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__A				={
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__A				={
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

__A				={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
__A				={
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
__A				={
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


__A				={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__A				={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__A				={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = DPRContextEncoderTokenizer




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = DPRQuestionEncoderTokenizer



__A				=collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__A				=collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__A				=R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''




@add_start_docstrings(snake_case_						)
class     _SCREAMING_SNAKE_CASE     :



	def __call__(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = False						,  lowercase = False						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  **lowercase						,  )				->       BatchEncoding:
					if titles is None and texts is None:
									return super().__call__(
									    lowercase						,  padding=lowercase						,  truncation=lowercase						,  max_length=lowercase						,  return_tensors=lowercase						,  return_attention_mask=lowercase						,  **lowercase						,  )
					elif titles is None or texts is None:
									lowerCamelCase_						=			titles if texts is None else texts
									return super().__call__(
									    lowercase						,  lowercase						,  padding=lowercase						,  truncation=lowercase						,  max_length=lowercase						,  return_tensors=lowercase						,  return_attention_mask=lowercase						,  **lowercase						,  )
					lowerCamelCase_						=			titles if not isinstance(lowercase						,  lowercase ) else [titles]
					lowerCamelCase_						=			texts if not isinstance(lowercase						,  lowercase ) else [texts]
					lowerCamelCase_						=			len(lowercase )
					lowerCamelCase_						=			questions if not isinstance(lowercase						,  lowercase ) else [questions] * n_passages
					assert len(lowercase ) == len(
					    lowercase ), f'There should be as many titles than texts but got {len(lowercase )} titles and {len(lowercase )} texts.'
					lowerCamelCase_						=			super().__call__(lowercase						,  lowercase						,  padding=lowercase						,  truncation=lowercase )["input_ids"]
					lowerCamelCase_						=			super().__call__(lowercase						,  add_special_tokens=lowercase						,  padding=lowercase						,  truncation=lowercase )["input_ids"]
					lowerCamelCase_						=			{
					    "input_ids": [
					        (encoded_question_and_title + encoded_text)[:max_length]
					        if max_length is not None and truncation
					        else encoded_question_and_title + encoded_text
					        for encoded_question_and_title, encoded_text in zip(lowercase						,  lowercase )
					    ]
					}
					if return_attention_mask is not False:
									lowerCamelCase_						=			[]
									for input_ids in encoded_inputs["input_ids"]:
													attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
									lowerCamelCase_						=			attention_mask
					return self.pad(lowercase						,  padding=lowercase						,  max_length=lowercase						,  return_tensors=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = 16						,  lowercase = 64						,  lowercase = 4						,  )				->       List[DPRSpanPrediction]:
					lowerCamelCase_						=			reader_input["input_ids"]
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			reader_output[:3]
					lowerCamelCase_						=			len(lowercase )
					lowerCamelCase_						=			sorted(range(lowercase )						,  reverse=lowercase						,  key=relevance_logits.__getitem__ )
					lowerCamelCase_						=			[]
					for doc_id in sorted_docs:
									lowerCamelCase_						=			list(input_ids[doc_id] )
									# assuming question & title information is at the beginning of the sequence
									lowerCamelCase_						=			sequence_ids.index(self.sep_token_id						,  2 ) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
													lowerCamelCase_						=			sequence_ids.index(self.pad_token_id )
									else:
													lowerCamelCase_						=			len(lowercase )

									lowerCamelCase_						=			self._get_best_spans(
									    start_logits=start_logits[doc_id][passage_offset:sequence_len]						,  end_logits=end_logits[doc_id][passage_offset:sequence_len]						,  max_answer_length=lowercase						,  top_spans=lowercase						,  )
									for start_index, end_index in best_spans:
													start_index += passage_offset
													end_index += passage_offset
													nbest_spans_predictions.append(
													    DPRSpanPrediction(
													        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]						,  relevance_score=relevance_logits[doc_id]						,  doc_id=lowercase						,  start_index=lowercase						,  end_index=lowercase						,  text=self.decode(sequence_ids[start_index : end_index + 1] )						,  ) )
									if len(lowercase ) >= num_spans:
													break
					return nbest_spans_predictions[:num_spans]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[DPRSpanPrediction]:
					lowerCamelCase_						=			[]
					for start_index, start_score in enumerate(lowercase ):
									for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
													scores.append(((start_index, start_index + answer_length), start_score + end_score) )
					lowerCamelCase_						=			sorted(lowercase						,  key=lambda lowercase : x[1]						,  reverse=lowercase )
					lowerCamelCase_						=			[]
					for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
									lowerCamelCase_						=			end_index - start_index + 1
									assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
									if any(
									    start_index <= prev_start_index <= prev_end_index <= end_index
									    or prev_start_index <= start_index <= end_index <= prev_end_index
									    for (prev_start_index, prev_end_index) in chosen_span_intervals ):
													continue
									chosen_span_intervals.append((start_index, end_index) )

									if len(lowercase ) == top_spans:
													break
					return chosen_span_intervals




@add_end_docstrings(snake_case_						)
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = READER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = READER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = DPRReaderTokenizer







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class       a							:



		def __init__(     self     :List[Any]   ,__lowercase     :List[Any]   ,__lowercase     :Union[str, Any]=1_3   ,__lowercase     :str=3_0   ,__lowercase     :Optional[Any]=2   ,__lowercase     :int=3   ,__lowercase     :List[Any]=True   ,__lowercase     :Tuple=True   ,__lowercase     :List[Any]=3_2   ,__lowercase     :str=2   ,__lowercase     :Union[str, Any]=4   ,__lowercase     :Dict=3_7   ,__lowercase     :List[Any]="gelu"   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :str=0.1   ,__lowercase     :Union[str, Any]=1_0   ,__lowercase     :Optional[Any]=0.02   ,__lowercase     :Union[str, Any]=3   ,__lowercase     :Any=0.6   ,__lowercase     :List[str]=None   ,):
						snake_case__	:      str     		=	parent
						snake_case__	:      int     		=	batch_size
						snake_case__	:      Dict     		=	image_size
						snake_case__	:      List[str]     		=	patch_size
						snake_case__	:      str     		=	num_channels
						snake_case__	:      int     		=	is_training
						snake_case__	:      List[str]     		=	use_labels
						snake_case__	:      List[str]     		=	hidden_size
						snake_case__	:      List[Any]     		=	num_hidden_layers
						snake_case__	:      str     		=	num_attention_heads
						snake_case__	:      Any     		=	intermediate_size
						snake_case__	:      Optional[Any]     		=	hidden_act
						snake_case__	:      List[str]     		=	hidden_dropout_prob
						snake_case__	:      Union[str, Any]     		=	attention_probs_dropout_prob
						snake_case__	:      Optional[Any]     		=	type_sequence_label_size
						snake_case__	:      List[str]     		=	initializer_range
						snake_case__	:      Optional[Any]     		=	mask_ratio
						snake_case__	:      List[str]     		=	scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						snake_case__	:      str     		=	(image_size // patch_size) ** 2
						snake_case__	:      List[str]     		=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Optional[Any]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						snake_case__	:      Any     		=	None
						if self.use_labels:
										snake_case__	:      Tuple     		=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size  )

						snake_case__	:      List[Any]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						return ViTMAEConfig(
						    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   ,decoder_hidden_size=self.hidden_size   ,decoder_num_hidden_layers=self.num_hidden_layers   ,decoder_num_attention_heads=self.num_attention_heads   ,decoder_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=__lowercase   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :Union[str, Any]   ,__lowercase     :Dict   ,__lowercase     :List[str]  ):
						snake_case__	:      Optional[int]     		=	TFViTMAEModel(config=__lowercase  )
						snake_case__	:      int     		=	model(__lowercase   ,training=__lowercase  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)  )



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Optional[int]   ,__lowercase     :int  ):
						snake_case__	:      Dict     		=	TFViTMAEForPreTraining(__lowercase  )
						snake_case__	:      Optional[int]     		=	model(__lowercase   ,training=__lowercase  )
						# expected sequence length = num_patches
						snake_case__	:      Optional[Any]     		=	(self.image_size // self.patch_size) ** 2
						snake_case__	:      int     		=	self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )

						# test greyscale images
						snake_case__	:      Tuple     		=	1
						snake_case__	:      List[Any]     		=	TFViTMAEForPreTraining(__lowercase  )

						snake_case__	:      Any     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						snake_case__	:      Any     		=	model(__lowercase   ,training=__lowercase  )
						snake_case__	:      Any     		=	self.patch_size**2
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	self.prepare_config_and_inputs()
						((snake_case__)		, (snake_case__)		, (snake_case__))	:      Optional[Any]     		=	config_and_inputs
						snake_case__	:      List[Any]     		=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_tf
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		__lowerCAmelCase	:    Union[str, Any]											=					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

		__lowerCAmelCase	:    Tuple											=					False
		__lowerCAmelCase	:    List[Any]											=					False
		__lowerCAmelCase	:    Dict											=					False
		__lowerCAmelCase	:    List[str]											=					False



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Union[str, Any]     		=	TFViTMAEModelTester(self  )
						snake_case__	:      Dict     		=	ConfigTester(self   ,config_class=__lowercase   ,has_text_modality=__lowercase   ,hidden_size=3_7  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''  )
		def 	__lowerCamelCase			(     self     :Dict  ):
						pass



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__		, snake_case__	:      int     		=	self.model_tester.prepare_config_and_inputs_for_common()

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



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__		, snake_case__	:      str     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

										snake_case__	:      Dict     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__lowercase  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Any     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Optional[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      List[str]     		=	model_class(__lowercase  )
										snake_case__	:      Union[str, Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

										snake_case__	:      Optional[int]     		=	copy.deepcopy(self._prepare_for_class(__lowercase   ,__lowercase  )  )
										snake_case__	:      List[Any]     		=	model(**__lowercase   ,noise=__lowercase  )
										snake_case__	:      Optional[Any]     		=	outputs_dict[0].numpy()
										snake_case__	:      Optional[Any]     		=	outputs_keywords[0].numpy()

										self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )   ,1e-6  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      List[str]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						def prepare_numpy_arrays(__lowercase     :Dict  ):
										snake_case__	:      Any     		=	{}
										for k, v in inputs_dict.items():
														if tf.is_tensor(__lowercase  ):
																		snake_case__	:      Dict     		=	v.numpy()
														else:
																		snake_case__	:      str     		=	np.array(__lowercase  )

										return inputs_np_dict

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )

										snake_case__	:      List[Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      Dict     		=	prepare_numpy_arrays(__lowercase  )

										snake_case__	:      Tuple     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      Dict     		=	model(**__lowercase   ,noise=__lowercase  )
										self.assert_outputs_same(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[Any]   ,__lowercase     :List[str]  ):
						# make masks reproducible
						np.random.seed(2  )

						snake_case__	:      Union[str, Any]     		=	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.constant(__lowercase  )

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						snake_case__	:      Optional[Any]     		=	tf_noise

						super().check_pt_tf_models(__lowercase   ,__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      List[str]     		=	{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__  ),)
						    for module_member_name in dir(__lowercase  )
						    if module_member_name.endswith('''MainLayer'''  )
						    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
						    and module_member_name[: -len('''MainLayer'''  )] == model_class.__name__[: -len('''Model'''  )]
						    for module_member in (getattr(__lowercase   ,__lowercase  ),)
						    if isinstance(__lowercase   ,__lowercase  )
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(__lowercase   ,'''_keras_serializable'''   ,__lowercase  )
						}

						snake_case__	:      Optional[Any]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.convert_to_tensor(__lowercase  )
						inputs_dict.update({'''noise''': noise}  )

						for main_layer_class in tf_main_layer_classes:
										snake_case__	:      List[Any]     		=	main_layer_class(__lowercase  )

										snake_case__	:      Union[str, Any]     		=	{
										    name: tf.keras.Input(tensor.shape[1:]   ,dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
										}

										snake_case__	:      Optional[Any]     		=	tf.keras.Model(__lowercase   ,outputs=main_layer(__lowercase  )  )
										snake_case__	:      List[str]     		=	model(__lowercase  )

										with tempfile.TemporaryDirectory() as tmpdirname:
														snake_case__	:      List[str]     		=	os.path.join(__lowercase   ,'''keras_model.h5'''  )
														model.save(__lowercase  )
														snake_case__	:      List[str]     		=	tf.keras.models.load_model(
														    __lowercase   ,custom_objects={main_layer_class.__name__: main_layer_class}  )
														assert isinstance(__lowercase   ,tf.keras.Model  )
														snake_case__	:      Union[str, Any]     		=	model(__lowercase  )
														self.assert_outputs_same(__lowercase   ,__lowercase  )



		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      str     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Tuple     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      Optional[int]     		=	model_class(__lowercase  )
										snake_case__	:      str     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )

										if model_class.__name__ == "TFViTMAEModel":
														snake_case__	:      List[Any]     		=	outputs.last_hidden_state.numpy()
														snake_case__	:      List[Any]     		=	0
										else:
														snake_case__	:      Any     		=	outputs.logits.numpy()
														snake_case__	:      Tuple     		=	0

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(__lowercase   ,saved_model=__lowercase  )
														snake_case__	:      Optional[Any]     		=	model_class.from_pretrained(__lowercase  )
														snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

														if model_class.__name__ == "TFViTMAEModel":
																		snake_case__	:      Dict     		=	after_outputs['''last_hidden_state'''].numpy()
																		snake_case__	:      List[Any]     		=	0
														else:
																		snake_case__	:      Any     		=	after_outputs['''logits'''].numpy()
																		snake_case__	:      Optional[Any]     		=	0

														snake_case__	:      int     		=	np.amax(np.abs(out_a - out_a  )  )
														self.assertLessEqual(__lowercase   ,1e-5  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      List[str]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Dict     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[str]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      int     		=	model_class(__lowercase  )
										snake_case__	:      int     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      int     		=	model.get_config()
										# make sure that returned config is jsonifiable, which is required by keras
										json.dumps(__lowercase  )
										snake_case__	:      Any     		=	model_class.from_config(model.get_config()  )
										# make sure it also accepts a normal config
										snake_case__	:      Optional[int]     		=	model_class.from_config(model.config  )
										snake_case__	:      Tuple     		=	new_model(__lowercase  )  # Build model
										new_model.set_weights(model.get_weights()  )
										snake_case__	:      List[Any]     		=	new_model(__lowercase   ,noise=__lowercase  )

										self.assert_outputs_same(__lowercase   ,__lowercase  )



		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''  )
		def 	__lowerCamelCase			(     self     :List[Any]  ):
						pass



		@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''  )
		def 	__lowerCamelCase			(     self     :Tuple  ):
						pass






		@slow
		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[Any]     		=	TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
						self.assertIsNotNone(__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      str     		=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
				return image





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



		@cached_property
		def 	__lowerCamelCase			(     self     :List[str]  ):
						return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''  ) if is_vision_available() else None






		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make random mask reproducible across the PT and TF model
						np.random.seed(2  )

						snake_case__	:      Tuple     		=	TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  )

						snake_case__	:      int     		=	self.default_image_processor
						snake_case__	:      int     		=	prepare_img()
						snake_case__	:      Tuple     		=	image_processor(images=__lowercase   ,return_tensors='''tf'''  )

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						snake_case__	:      str     		=	ViTMAEConfig()
						snake_case__	:      List[str]     		=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
						snake_case__	:      Dict     		=	np.random.uniform(size=(1, num_patches)  )

						# forward pass
						snake_case__	:      List[str]     		=	model(**__lowercase   ,noise=__lowercase  )

						# verify the logits
						snake_case__	:      Optional[Any]     		=	tf.convert_to_tensor([1, 1_9_6, 7_6_8]  )
						self.assertEqual(outputs.logits.shape   ,__lowercase  )

						snake_case__	:      Tuple     		=	tf.convert_to_tensor(
						    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

						tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,__lowercase   ,atol=1e-4  )

