

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': 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 				UpperCAmelCase_		(			__UpperCAmelCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    VOCAB_FILES_NAMES
							UpperCAmelCase__       :						Dict	    =    CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__       :						Union[str, Any]	    =    CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__       :						Dict	    =    CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase__       :						int	    =    DPRContextEncoderTokenizer



class 				UpperCAmelCase_		(			__UpperCAmelCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    VOCAB_FILES_NAMES
							UpperCAmelCase__       :						str	    =    QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__       :						List[Any]	    =    QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__       :						Optional[int]	    =    QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase__       :						int	    =    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'\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 				UpperCAmelCase_		:
							"""simple docstring"""







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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = 1_6       ,							_a = 6_4       ,							_a = 4       ,							)    ->    List[str]:
												_a	:    Any         =		reader_input['''input_ids''']
												_a      ,					_a      ,					_a	:    int         =		reader_output[:3]
												_a	:    Tuple         =		len(lowerCamelCase__       )
												_a	:    Dict         =		sorted(range(lowerCamelCase__       )       ,							reverse=lowerCamelCase__       ,							key=relevance_logits.__getitem__       )
												_a	:    Union[str, Any]         =		[]
												for doc_id in sorted_docs:
																	_a	:    int         =		list(input_ids[doc_id]       )
																	# assuming question & title information is at the beginning of the sequence
																	_a	:    Optional[int]         =		sequence_ids.index(self.sep_token_id       ,							2       ) + 1  # second sep id
																	if sequence_ids[-1] == self.pad_token_id:
																						_a	:    List[Any]         =		sequence_ids.index(self.pad_token_id       )
																	else:
																						_a	:    Any         =		len(lowerCamelCase__       )

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




							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							)    ->    Any:
												_a	:    int         =		[]
												for start_index, start_score in enumerate(lowerCamelCase__       ):
																	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)       )
												_a	:    Optional[int]         =		sorted(lowerCamelCase__       ,							key=lambda _a       : x[1]       ,							reverse=lowerCamelCase__       )
												_a	:    Any         =		[]
												for (start_index, end_index), score in scores:
																	assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
																	_a	:    Dict         =		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(lowerCamelCase__       ) == top_spans:
																						break
												return chosen_span_intervals



@add_end_docstrings(__UpperCAmelCase   )
class 				UpperCAmelCase_		(			__UpperCAmelCase				,       __UpperCAmelCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    VOCAB_FILES_NAMES
							UpperCAmelCase__       :						Any	    =    READER_PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__       :						Optional[Any]	    =    READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__       :						str	    =    READER_PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase__       :						str	    =    ["input_ids", "attention_mask"]
							UpperCAmelCase__       :						Union[str, Any]	    =    DPRReaderTokenizer




"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



