



'''simple docstring'''





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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE__      					=     {
    "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"
        ),
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__      					=     {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :str						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Tuple						   =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :int						   =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :int						   =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__  :Tuple						   =					DPRContextEncoderTokenizer





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :List[str]						   =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :Any						   =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :List[str]						   =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__  :Union[str, Any]						   =					DPRQuestionEncoderTokenizer





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

SCREAMING_SNAKE_CASE__      					=     collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE__      					=     R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "


@add_start_docstrings(UpperCamelCase   )
class 				snake_case :



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



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

               lowercase__		       =						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=UpperCAmelCase_     ,top_spans=UpperCAmelCase_     ,)
               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=UpperCAmelCase_     ,start_index=UpperCAmelCase_     ,end_index=UpperCAmelCase_     ,text=self.decode(sequence_ids[start_index : end_index + 1]      )     ,)      )
               if len(UpperCAmelCase_      ) >= num_spans:
                   break
           return nbest_spans_predictions[:num_spans]







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							List[DPRSpanPrediction]:
           lowercase__		       =						[]
           for start_index, start_score in enumerate(UpperCAmelCase_      ):
               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)      )
           lowercase__		       =						sorted(UpperCAmelCase_     ,key=lambda UpperCAmelCase_      : x[1]     ,reverse=UpperCAmelCase_      )
           lowercase__		       =						[]
           for (start_index, end_index), score in scores:
               assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
               lowercase__		       =						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(UpperCAmelCase_      ) == top_spans:
                   break
           return chosen_span_intervals





@add_end_docstrings(UpperCamelCase   )
class 				snake_case (UpperCamelCase		,      UpperCamelCase   ):
       lowerCAmelCase__  :int						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :str						   =					READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :int						   =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :Union[str, Any]						   =					READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__  :Optional[Any]						   =					["input_ids", "attention_mask"]
       lowerCAmelCase__  :Optional[Any]						   =					DPRReaderTokenizer









'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
 import jax

 from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor


class 				snake_case (unittest.TestCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=100     ,UpperCAmelCase_=13     ,UpperCAmelCase_=30     ,UpperCAmelCase_=2     ,UpperCAmelCase_=3     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_=32     ,UpperCAmelCase_=5     ,UpperCAmelCase_=4     ,UpperCAmelCase_=37     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=10     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=3     ,)  ->							List[Any]:
           lowercase__		       =						parent
           lowercase__		       =						vocab_size
           lowercase__		       =						batch_size
           lowercase__		       =						image_size
           lowercase__		       =						patch_size
           lowercase__		       =						num_channels
           lowercase__		       =						is_training
           lowercase__		       =						use_labels
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						type_sequence_label_size
           lowercase__		       =						initializer_range

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowercase__		       =						(image_size // patch_size) ** 2
           lowercase__		       =						num_patches + 1



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

           lowercase__		       =						BeitConfig(
               vocab_size=self.vocab_size     ,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     ,)

           return config, pixel_values, labels



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						FlaxBeitModel(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						FlaxBeitForImageClassification(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FlaxBeitForImageClassification(UpperCAmelCase_      )

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_      )







       def 							_a      (				self      )  ->							int:
           lowercase__		       =						self.prepare_config_and_inputs()
           (
               (
               lowercase__
           )       ,       (
               lowercase__
           )       ,       (
               lowercase__
           )       ,       
           )		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_flax
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[int]						   =					(
           (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
       )



       def 							_a      (				self      )  ->							None:
           lowercase__		       =						FlaxBeitModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,has_text_modality=UpperCAmelCase_     ,hidden_size=37      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           self.config_tester.run_common_tests()



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                   lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_      )
                   lowercase__		       =						model_class(UpperCAmelCase_      )

                   @jax.jit
                   def model_jitted(UpperCAmelCase_     ,**UpperCAmelCase_      ):
                       return model(pixel_values=UpperCAmelCase_     ,**UpperCAmelCase_      )

                   with self.subTest("JIT Enabled"      ):
                       lowercase__		       =						model_jitted(**UpperCAmelCase_      ).to_tuple()

                   with self.subTest("JIT Disabled"      ):
                       with jax.disable_jit():
                           lowercase__		       =						model_jitted(**UpperCAmelCase_      ).to_tuple()

                   self.assertEqual(len(UpperCAmelCase_      )     ,len(UpperCAmelCase_      )      )
                   for jitted_output, output in zip(UpperCAmelCase_     ,UpperCAmelCase_      ):
                       self.assertEqual(jitted_output.shape     ,output.shape      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )







       @slow
       def 							_a      (				self      )  ->							Tuple:
           for model_class_name in self.all_model_classes:
               lowercase__		       =						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"      )
               lowercase__		       =						model(np.ones((1, 3, 224, 224)      )      )
               self.assertIsNotNone(UpperCAmelCase_      )





def        lowerCamelCase  (				):





    '''simple docstring'''


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



@require_vision
@require_flax
class 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Optional[int]:
           return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      ).pixel_values

           # prepare bool_masked_pos
           lowercase__		       =						np.ones((1, 196)     ,dtype=UpperCAmelCase_      )

           # forward pass
           lowercase__		       =						model(pixel_values=UpperCAmelCase_     ,bool_masked_pos=UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 196, 8_192)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array(
               [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      )

           self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]     ,UpperCAmelCase_     ,atol=1E-2      )      )



       @slow
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      )

           # forward pass
           lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 1_000)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array([-1.23_85, -1.09_87, -1.01_08]      )

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

           lowercase__		       =						281
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )







       @slow
       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      )

           # forward pass
           lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 21_841)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array([1.68_81, -0.27_87, 0.59_01]      )

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

           lowercase__		       =						2_396
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )




