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


__UpperCamelCase      :      List[str]     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Optional[Any]     						=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase      :      int     						=						{
    "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"
        ),
    },
}
__UpperCamelCase      :      List[Any]     						=						{
    "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"
        ),
    },
}
__UpperCamelCase      :      int     						=						{
    "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"
        ),
    },
}

__UpperCamelCase      :      str     						=						{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__UpperCamelCase      :      Dict     						=						{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__UpperCamelCase      :      Optional[Any]     						=						{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


__UpperCamelCase      :      int     						=						{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCamelCase      :      int     						=						{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__UpperCamelCase      :      Union[str, Any]     						=						{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class        __UpperCamelCase    (     __lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       VOCAB_FILES_NAMES
	__snake_case	:Any   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:List[str]   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:List[str]   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__snake_case	:Tuple   =       DPRContextEncoderTokenizer


class        __UpperCamelCase    (     __lowerCAmelCase   ):
	__snake_case	:List[str]   =       VOCAB_FILES_NAMES
	__snake_case	:Optional[int]   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:Union[str, Any]   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:Dict   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__snake_case	:Tuple   =       DPRQuestionEncoderTokenizer

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

__UpperCamelCase      :      Dict     						=						collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__UpperCamelCase      :      Union[str, Any]     						=						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(__lowerCAmelCase   )
class        __UpperCamelCase    :







	def __call__(  self      :							Any       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[str] = None       ,				_lowerCAmelCase      :							Optional[str] = None       ,				_lowerCAmelCase      :							Union[bool, str] = False       ,				_lowerCAmelCase      :							Union[bool, str] = False       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Optional[Union[str, TensorType]] = None       ,				_lowerCAmelCase      :							Optional[bool] = None       ,				**_lowerCAmelCase      :							Optional[Any]       ,				)		-> BatchEncoding:
		"""simple docstring"""







		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:
			__lowercase             =  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__       ,				)
		__lowercase             =  titles if not isinstance(lowerCamelCase__       ,				lowerCamelCase__			) else [titles]
		__lowercase             =  texts if not isinstance(lowerCamelCase__       ,				lowerCamelCase__			) else [texts]
		__lowercase             =  len(lowerCamelCase__			)
		__lowercase             =  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.'
		__lowercase             =  super().__call__(lowerCamelCase__       ,				lowerCamelCase__       ,				padding=lowerCamelCase__       ,				truncation=lowerCamelCase__			)['''input_ids''']
		__lowercase             =  super().__call__(lowerCamelCase__       ,				add_special_tokens=lowerCamelCase__       ,				padding=lowerCamelCase__       ,				truncation=lowerCamelCase__			)['''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(lowerCamelCase__       ,				lowerCamelCase__			)
		    ]
		}
		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(lowerCamelCase__       ,				padding=lowerCamelCase__       ,				max_length=lowerCamelCase__       ,				return_tensors=lowerCamelCase__			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							BatchEncoding       ,				_lowerCAmelCase      :							DPRReaderOutput       ,				_lowerCAmelCase      :							int = 16       ,				_lowerCAmelCase      :							int = 64       ,				_lowerCAmelCase      :							int = 4       ,				)		-> List[DPRSpanPrediction]:
		"""simple docstring"""







		__lowercase             =  reader_input['''input_ids''']
		__lowercase             =  reader_output[:3]
		__lowercase             =  len(lowerCamelCase__			)
		__lowercase             =  sorted(range(lowerCamelCase__			)       ,				reverse=lowerCamelCase__       ,				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(lowerCamelCase__			)

			__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=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  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				)		-> List[DPRSpanPrediction]:
		"""simple docstring"""







		__lowercase             =  []
		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)			)
		__lowercase             =  sorted(lowerCamelCase__       ,				key=lambda _lowerCAmelCase			: x[1]       ,				reverse=lowerCamelCase__			)
		__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(lowerCamelCase__			) == top_spans:
				break
		return chosen_span_intervals


@add_end_docstrings(__lowerCAmelCase   )
class        __UpperCamelCase    (     __lowerCAmelCase   ,			__lowerCAmelCase   ):
	__snake_case	:Tuple   =       VOCAB_FILES_NAMES
	__snake_case	:Union[str, Any]   =       READER_PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:List[str]   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:Optional[Any]   =       READER_PRETRAINED_INIT_CONFIGURATION
	__snake_case	:List[str]   =       ["input_ids", "attention_mask"]
	__snake_case	:Optional[Any]   =       DPRReaderTokenizer









import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCamelCase      :      Optional[Any]     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()
	__lowercase             =  []

	def parse_line(lowerCamelCase					):
		for line in fp:
			if isinstance(lowerCamelCase				,  lowerCamelCase					):
				__lowercase             =  line.decode("""UTF-8"""					)
			if "warnings summary (final)" in line:
				continue
			# This means we are outside the body of a warning
			elif not line.startswith(""" """					):
				# process a single warning and move it to `selected_warnings`.
				if len(lowerCamelCase					) > 0:
					__lowercase             =  """\n""".join(lowerCamelCase					)
					# Only keep the warnings specified in `targets`
					if any(F': {x}: ' in warning for x in targets					):
						selected_warnings.add(lowerCamelCase					)
					buffer.clear()
				continue
			else:
				__lowercase             =  line.strip()
				buffer.append(lowerCamelCase					)

	if from_gh:
		for filename in os.listdir(lowerCamelCase					):
			__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
			if not os.path.isdir(lowerCamelCase					):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(lowerCamelCase					) as fp:
					parse_line(lowerCamelCase					)
	else:
		try:
			with zipfile.ZipFile(lowerCamelCase					) as z:
				for filename in z.namelist():
					if not os.path.isdir(lowerCamelCase					):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(lowerCamelCase					) as fp:
							parse_line(lowerCamelCase					)
		except Exception:
			logger.warning(
			    F'{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'					)

	return selected_warnings





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  set()

	__lowercase             =  [os.path.join(lowerCamelCase				,  lowerCamelCase					) for p in os.listdir(lowerCamelCase					) if (p.endswith(""".zip"""					) or from_gh)]
	for p in paths:
		selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase				,  lowerCamelCase					)					)

	return selected_warnings


if __name__ == "__main__":





						def 				snake_case  (     lowerCamelCase					):




							'''simple docstring'''







							return values.split(""","""					)

						__UpperCamelCase      :      Union[str, Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
						parser.add_argument(
						    """--output_dir""",
						    type=str,
						    required=True,
						    help="""Where to store the downloaded artifacts and other result files.""",
						)
						parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
						# optional parameters
						parser.add_argument(
						    """--targets""",
						    default="""DeprecationWarning,UserWarning,FutureWarning""",
						    type=list_str,
						    help="""Comma-separated list of target warning(s) which we want to extract.""",
						)
						parser.add_argument(
						    """--from_gh""",
						    action="""store_true""",
						    help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
						)

						__UpperCamelCase      :      List[str]     						=						parser.parse_args()

						__UpperCamelCase      :      Union[str, Any]     						=						args.from_gh
						if from_gh:
												# The artifacts have to be downloaded using `actions/download-artifact@v3`
												pass
						else:
												os.makedirs(args.output_dir, exist_ok=True)

												# get download links
												__UpperCamelCase      :      Any     						=						get_artifacts_links(args.workflow_run_id, token=args.token)
												with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
																		json.dump(artifacts, fp, ensure_ascii=False, indent=4)

												# download artifacts
												for idx, (name, url) in enumerate(artifacts.items()):
																		print(name)
																		print(url)
																		print("""=""" * 80)
																		download_artifact(name, url, args.output_dir, args.token)
																		# Be gentle to GitHub
																		time.sleep(1)

    # extract warnings from artifacts
						__UpperCamelCase      :      Union[str, Any]     						=						extract_warnings(args.output_dir, args.targets)
						__UpperCamelCase      :      Any     						=						sorted(selected_warnings)
						with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
												json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

