
"""simple docstring"""




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


A__   :					List[Any]							=   logging.get_logger(__name__)

A__   :					Union[str, Any]							=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__   :					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'
        ),
    },
}
A__   :					Optional[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'
        ),
    },
}
A__   :					Union[str, 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'
        ),
    },
}

A__   :					Tuple							=   {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
A__   :					List[Any]							=   {
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
A__   :					Union[str, Any]							=   {
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


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


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_   =		DPRContextEncoderTokenizer


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_   =		DPRQuestionEncoderTokenizer

A__   :					Optional[int]							=   collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

A__   :					Optional[Any]							=   collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


A__   :					str							=   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(SCREAMING_SNAKE_CASE__ )
class 			__magic_name__       :
       def __call__(     self ,				A_ ,				A_ = None ,				A_ = None ,				A_ = False ,				A_ = False ,				A_ = None ,				A_ = None ,				A_ = None ,				**A_ ,				)     ->	BatchEncoding:





         """simple docstring"""




         if titles is None and texts is None:
           return super().__call__(
               A_ ,				padding=A_ ,				truncation=A_ ,				max_length=A_ ,				return_tensors=A_ ,				return_attention_mask=A_ ,				**A_ ,				)
         elif titles is None or texts is None:
           _lowercase:			Dict       =     titles if texts is None else texts
           return super().__call__(
               A_ ,				A_ ,				padding=A_ ,				truncation=A_ ,				max_length=A_ ,				return_tensors=A_ ,				return_attention_mask=A_ ,				**A_ ,				)
         _lowercase:			str       =     titles if not isinstance(A_ ,				A_       ) else [titles]
         _lowercase:			Optional[Any]       =     texts if not isinstance(A_ ,				A_       ) else [texts]
         _lowercase:			str       =     len(A_       )
         _lowercase:			List[Any]       =     questions if not isinstance(A_ ,				A_       ) else [questions] * n_passages
         assert len(A_       ) == len(
             A_       ), f'''There should be as many titles than texts but got {len(A_       )} titles and {len(A_       )} texts.'''
         _lowercase:			List[Any]       =     super().__call__(A_ ,				A_ ,				padding=A_ ,				truncation=A_       )['''input_ids''']
         _lowercase:			Optional[Any]       =     super().__call__(A_ ,				add_special_tokens=A_ ,				padding=A_ ,				truncation=A_       )['''input_ids''']
         _lowercase:			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(A_ ,				A_       )
             ]
         }
         if return_attention_mask is not False:
           _lowercase:			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]       )
           _lowercase:			Optional[Any]       =     attention_mask
         return self.pad(A_ ,				padding=A_ ,				max_length=A_ ,				return_tensors=A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ = 16 ,				A_ = 64 ,				A_ = 4 ,				)     ->	List[DPRSpanPrediction]:





         """simple docstring"""




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

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



       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				)     ->	List[DPRSpanPrediction]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     []
         for start_index, start_score in enumerate(A_       ):
           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:			Tuple       =     sorted(A_ ,				key=lambda A_       : x[1] ,				reverse=A_       )
         _lowercase:			int       =     []
         for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
           _lowercase:			Optional[int]       =     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(A_       ) == top_spans:
             break
         return chosen_span_intervals


@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		READER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		READER_PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_   =		DPRReaderTokenizer


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
