

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 import BertTokenizer


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


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							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__A						  =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__A						  =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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'\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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(__snake_case							)
class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = False ,				__UpperCamelCase = False ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				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:
					snake_case_      		= 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 ,				)
				snake_case_      		= titles if not isinstance(__UpperCamelCase ,				__UpperCamelCase							) else [titles]
				snake_case_      		= texts if not isinstance(__UpperCamelCase ,				__UpperCamelCase							) else [texts]
				snake_case_      		= len(__UpperCamelCase							)
				snake_case_      		= questions if not isinstance(__UpperCamelCase ,				__UpperCamelCase							) else [questions] * n_passages
				if len(__UpperCamelCase							) != len(__UpperCamelCase							):
					raise ValueError(
					    f"""There should be as many titles than texts but got {len(__UpperCamelCase							)} titles and {len(__UpperCamelCase							)} texts."""							)
				snake_case_      		= super().__call__(__UpperCamelCase ,				__UpperCamelCase ,				padding=__UpperCamelCase ,				truncation=__UpperCamelCase							)['input_ids']
				snake_case_      		= super().__call__(__UpperCamelCase ,				add_special_tokens=__UpperCamelCase ,				padding=__UpperCamelCase ,				truncation=__UpperCamelCase							)['input_ids']
				snake_case_      		= {
				    '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:
					snake_case_      		= []
					for input_ids in encoded_inputs["input_ids"]:
						attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
					snake_case_      		= attention_mask
				return self.pad(__UpperCamelCase ,				padding=__UpperCamelCase ,				max_length=__UpperCamelCase ,				return_tensors=__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 16 ,				__UpperCamelCase = 64 ,				__UpperCamelCase = 4 ,				):

				"""simple docstring"""


				snake_case_      		= reader_input['input_ids']
				snake_case_		,       snake_case_		,       snake_case_      		= reader_output[:3]
				snake_case_      		= len(__UpperCamelCase							)
				snake_case_      		= sorted(range(__UpperCamelCase							) ,				reverse=__UpperCamelCase ,				key=relevance_logits.__getitem__							)
				snake_case_      		= []
				for doc_id in sorted_docs:
					snake_case_      		= list(input_ids[doc_id]							)
					# assuming question & title information is at the beginning of the sequence
					snake_case_      		= sequence_ids.index(self.sep_token_id ,				2							) + 1  # second sep id
					if sequence_ids[-1] == self.pad_token_id:
						snake_case_      		= sequence_ids.index(self.pad_token_id							)
					else:
						snake_case_      		= len(__UpperCamelCase							)

					snake_case_      		= 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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= []
				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)							)
				snake_case_      		= sorted(__UpperCamelCase ,				key=lambda __UpperCamelCase							: x[1] ,				reverse=__UpperCamelCase							)
				snake_case_      		= []
				for (start_index, end_index), score in scores:
					if start_index > end_index:
						raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
					snake_case_      		= end_index - start_index + 1
					if length > max_answer_length:
						raise ValueError(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(__snake_case							)
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  READER_PRETRAINED_VOCAB_FILES_MAP
			__A						  =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A						  =  READER_PRETRAINED_INIT_CONFIGURATION
			__A						  =  ["""input_ids""", """attention_mask"""]



import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
		import torch

		from transformers import (
		    MraForMaskedLM,
		    MraForMultipleChoice,
		    MraForQuestionAnswering,
		    MraForSequenceClassification,
		    MraForTokenClassification,
		    MraModel,
		)
		from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=2 ,				__UpperCamelCase=8 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=99 ,				__UpperCamelCase=16 ,				__UpperCamelCase=5 ,				__UpperCamelCase=2 ,				__UpperCamelCase=36 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=16 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=3 ,				__UpperCamelCase=4 ,				__UpperCamelCase=None ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= seq_length
				snake_case_      		= is_training
				snake_case_      		= use_input_mask
				snake_case_      		= use_token_type_ids
				snake_case_      		= use_labels
				snake_case_      		= vocab_size
				snake_case_      		= hidden_size
				snake_case_      		= num_hidden_layers
				snake_case_      		= num_attention_heads
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= max_position_embeddings
				snake_case_      		= type_vocab_size
				snake_case_      		= type_sequence_label_size
				snake_case_      		= initializer_range
				snake_case_      		= num_labels
				snake_case_      		= num_choices
				snake_case_      		= scope


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)

				snake_case_      		= None
				if self.use_input_mask:
					snake_case_      		= random_attention_mask([self.batch_size, self.seq_length]							)

				snake_case_      		= None
				if self.use_token_type_ids:
					snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size							)

				snake_case_      		= None
				snake_case_      		= None
				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)
					snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels							)
					snake_case_      		= ids_tensor([self.batch_size] ,				self.num_choices							)

				snake_case_      		= self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return MraConfig(
				    vocab_size=self.vocab_size ,				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 ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				is_decoder=__UpperCamelCase ,				initializer_range=self.initializer_range ,				)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_config()
				snake_case_      		= 3_00
				return config


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				(
				    (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       
				)      		= self.prepare_config_and_inputs()

				snake_case_      		= True
				snake_case_      		= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
				snake_case_      		= ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2							)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MraModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase ,				token_type_ids=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= True
				snake_case_      		= MraModel(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				encoder_attention_mask=__UpperCamelCase ,				)
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				encoder_hidden_states=__UpperCamelCase ,				)
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MraForMaskedLM(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= MraForQuestionAnswering(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				start_positions=__UpperCamelCase ,				end_positions=__UpperCamelCase ,				)
				self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)							)
				self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MraForSequenceClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_labels
				snake_case_      		= MraForTokenClassification(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.num_choices
				snake_case_      		= MraForMultipleChoice(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= input_ids.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
				snake_case_      		= token_type_ids.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
				snake_case_      		= input_mask.unsqueeze(1							).expand(-1 ,				self.num_choices ,				-1							).contiguous()
				snake_case_      		= model(
				    __UpperCamelCase ,				attention_mask=__UpperCamelCase ,				token_type_ids=__UpperCamelCase ,				labels=__UpperCamelCase ,				)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				(
				    (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       (
				    snake_case_
				)		,       
				)      		= config_and_inputs
				snake_case_      		= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False

			__A						  =  ()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				hidden_size=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					snake_case_      		= type
					self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= MraModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







			@unittest.skip(reason='MRA does not output attentions'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return







@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraModel.from_pretrained('uw-madison/mra-base-512-4'							)
				snake_case_      		= torch.arange(2_56							).unsqueeze(0							)

				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)[0]

				snake_case_      		= torch.Size((1, 2_56, 7_68)							)
				self.assertEqual(output.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'							)
				snake_case_      		= torch.arange(2_56							).unsqueeze(0							)

				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)[0]

				snake_case_      		= 5_02_65

				snake_case_      		= torch.Size((1, 2_56, vocab_size)							)
				self.assertEqual(output.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'							)
				snake_case_      		= torch.arange(40_96							).unsqueeze(0							)

				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)[0]

				snake_case_      		= 5_02_65

				snake_case_      		= torch.Size((1, 40_96, vocab_size)							)
				self.assertEqual(output.shape ,				__UpperCamelCase							)

				snake_case_      		= torch.tensor(
				    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]							)

				self.assertTrue(torch.allclose(output[:, :3, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)
