






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

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

UpperCamelCase__    :   Tuple    		=       {
    "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[str]    		=       {
    "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__    :   List[Any]    		=       {
    "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__    :   Optional[Any]    		=       {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCamelCase__    :   Any    		=       {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCamelCase__    :   int    		=       {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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







class 					lowerCamelCase_  (						__snake_case  ):
							SCREAMING_SNAKE_CASE_      =      VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_      =      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_      =      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_      =      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE_      =      DPRContextEncoderTokenizer







class 					lowerCamelCase_  (						__snake_case  ):
							SCREAMING_SNAKE_CASE_      =      VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_      =      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_      =      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_      =      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE_      =      DPRQuestionEncoderTokenizer


UpperCamelCase__    :   List[str]    		=       collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

UpperCamelCase__    :   Optional[int]    		=       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(__snake_case  )
class 					lowerCamelCase_  :
							def __call__(    self		:       Union[str, Any]				,__lowerCamelCase		:       Optional[int]				,__lowerCamelCase		:       Dict = None				,__lowerCamelCase		:       Union[str, Any] = None				,__lowerCamelCase		:       Optional[Any] = False				,__lowerCamelCase		:       Dict = False				,__lowerCamelCase		:       Optional[int] = None				,__lowerCamelCase		:       List[str] = None				,__lowerCamelCase		:       Any = None				,**__lowerCamelCase		:       Any				,):





								'''simple docstring'''



								if titles is None and texts is None:
									return super().__call__(
									    snake_case_				,padding=snake_case_				,truncation=snake_case_				,max_length=snake_case_				,return_tensors=snake_case_				,return_attention_mask=snake_case_				,**snake_case_				,)
								elif titles is None or texts is None:
									a												=      titles if texts is None else texts
									return super().__call__(
									    snake_case_				,snake_case_				,padding=snake_case_				,truncation=snake_case_				,max_length=snake_case_				,return_tensors=snake_case_				,return_attention_mask=snake_case_				,**snake_case_				,)
								a												=      titles if not isinstance(snake_case_				,snake_case_       ) else [titles]
								a												=      texts if not isinstance(snake_case_				,snake_case_       ) else [texts]
								a												=      len(snake_case_       )
								a												=      questions if not isinstance(snake_case_				,snake_case_       ) else [questions] * n_passages
								assert len(snake_case_       ) == len(
								    snake_case_       ), F"""There should be as many titles than texts but got {len(snake_case_       )} titles and {len(snake_case_       )} texts."""
								a												=      super().__call__(snake_case_				,snake_case_				,padding=snake_case_				,truncation=snake_case_       )['''input_ids''']
								a												=      super().__call__(snake_case_				,add_special_tokens=snake_case_				,padding=snake_case_				,truncation=snake_case_       )['''input_ids''']
								a												=      {
								    '''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(snake_case_				,snake_case_       )
								    ]
								}
								if return_attention_mask is not False:
									a												=      []
									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												=      attention_mask
								return self.pad(snake_case_				,padding=snake_case_				,max_length=snake_case_				,return_tensors=snake_case_       )
							def 			SCREAMING_SNAKE_CASE_			(    self		:       int				,__lowerCamelCase		:       Tuple				,__lowerCamelCase		:       List[str]				,__lowerCamelCase		:       Optional[Any] = 16				,__lowerCamelCase		:       Optional[Any] = 64				,__lowerCamelCase		:       Optional[Any] = 4				,):





								'''simple docstring'''



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

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




							def 			SCREAMING_SNAKE_CASE_			(    self		:       List[str]				,__lowerCamelCase		:       Union[str, Any]				,__lowerCamelCase		:       str				,__lowerCamelCase		:       Union[str, Any]				,__lowerCamelCase		:       Dict				,):





								'''simple docstring'''



								a												=      []
								for start_index, start_score in enumerate(snake_case_       ):
									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												=      sorted(snake_case_				,key=lambda __lowerCamelCase       : x[1]				,reverse=snake_case_       )
								a												=      []
								for (start_index, end_index), score in scores:
									assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
									a												=      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(snake_case_       ) == top_spans:
										break
								return chosen_span_intervals







@add_end_docstrings(__snake_case  )
class 					lowerCamelCase_  (						__snake_case			,    __snake_case  ):
							SCREAMING_SNAKE_CASE_      =      VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE_      =      READER_PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE_      =      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE_      =      READER_PRETRAINED_INIT_CONFIGURATION
							SCREAMING_SNAKE_CASE_      =      ['input_ids', 'attention_mask']
							SCREAMING_SNAKE_CASE_      =      DPRReaderTokenizer












import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							UNetaDModel(
             block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,     )
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'				,     return_dict=snake_case_							)[0]

         _A     =							image[0, -3:, -3:, -1]
         _A     =							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _A     =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2







@slow
@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=20				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





