




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	@property
	def 					lowerCamelCase__     (					self		:						List[str]	)	->       str:







						"""simple docstring"""







						torch.manual_seed(0	)
						__UpperCamelCase					:	List[Any]             =      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		:						str	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      self.dummy_uncond_unet
						__UpperCamelCase					:	Any             =      PNDMScheduler()

						__UpperCamelCase					:	Optional[int]             =      PNDMPipeline(unet=lowerCAmelCase					,     scheduler=lowerCAmelCase	)
						pndm.to(lowerCAmelCase	)
						pndm.set_progress_bar_config(disable=lowerCAmelCase	)

						__UpperCamelCase					:	Dict             =      torch.manual_seed(0	)
						__UpperCamelCase					:	Optional[Any]             =      pndm(generator=lowerCAmelCase					,     num_inference_steps=20					,     output_type="""numpy"""	).images

						__UpperCamelCase					:	Tuple             =      torch.manual_seed(0	)
						__UpperCamelCase					:	Union[str, Any]             =      pndm(generator=lowerCAmelCase					,     num_inference_steps=20					,     output_type="""numpy"""					,     return_dict=lowerCAmelCase	)[0]

						__UpperCamelCase					:	str             =      image[0, -3:, -3:, -1]
						__UpperCamelCase					:	str             =      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__UpperCamelCase					:	List[Any]             =      np.array([1.0, 1.0, 0.0, 1.0, 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    SCREAMING_SNAKE_CASE_				(       unittest.TestCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Any	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      """google/ddpm-cifar10-32"""

						__UpperCamelCase					:	Tuple             =      UNetaDModel.from_pretrained(lowerCAmelCase	)
						__UpperCamelCase					:	int             =      PNDMScheduler()

						__UpperCamelCase					:	Optional[Any]             =      PNDMPipeline(unet=lowerCAmelCase					,     scheduler=lowerCAmelCase	)
						pndm.to(lowerCAmelCase	)
						pndm.set_progress_bar_config(disable=lowerCAmelCase	)
						__UpperCamelCase					:	Dict             =      torch.manual_seed(0	)
						__UpperCamelCase					:	Optional[Any]             =      pndm(generator=lowerCAmelCase					,     output_type="""numpy"""	).images

						__UpperCamelCase					:	Union[str, Any]             =      image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__UpperCamelCase					:	str             =      np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]	)

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












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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Union[str, Any]      	=		VOCAB_FILES_NAMES
	__magic_name__   :       List[Any]      	=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       Optional[Any]      	=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Union[str, Any]      	=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__   :       Optional[Any]      	=		DPRContextEncoderTokenizer






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Tuple      	=		VOCAB_FILES_NAMES
	__magic_name__   :       Dict      	=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       Optional[Any]      	=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Tuple      	=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__   :       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'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''


@add_start_docstrings(_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __call__(					self		:						Tuple					,     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		:						Tuple					,     )	->       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:
											__UpperCamelCase					:	Optional[Any]             =      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					,     )
						__UpperCamelCase					:	Optional[Any]             =      titles if not isinstance(lowerCAmelCase					,     lowerCAmelCase	) else [titles]
						__UpperCamelCase					:	Union[str, Any]             =      texts if not isinstance(lowerCAmelCase					,     lowerCAmelCase	) else [texts]
						__UpperCamelCase					:	Optional[Any]             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	str             =      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.'''
						__UpperCamelCase					:	List[str]             =      super().__call__(lowerCAmelCase					,     lowerCAmelCase					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase	)["""input_ids"""]
						__UpperCamelCase					:	Tuple             =      super().__call__(lowerCAmelCase					,     add_special_tokens=lowerCAmelCase					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase	)["""input_ids"""]
						__UpperCamelCase					:	List[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:
											__UpperCamelCase					:	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]	)
											__UpperCamelCase					:	Optional[Any]             =      attention_mask
						return self.pad(lowerCAmelCase					,     padding=lowerCAmelCase					,     max_length=lowerCAmelCase					,     return_tensors=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						BatchEncoding					,     lowerCAmelCase		:						DPRReaderOutput					,     lowerCAmelCase		:						int = 16					,     lowerCAmelCase		:						int = 64					,     lowerCAmelCase		:						int = 4					,     )	->       List[DPRSpanPrediction]:







						"""simple docstring"""







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

											__UpperCamelCase					:	Union[str, 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 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int					,     )	->       List[DPRSpanPrediction]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      []
						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)	)
						__UpperCamelCase					:	Optional[Any]             =      sorted(lowerCAmelCase					,     key=lambda lowerCAmelCase	: x[1]					,     reverse=lowerCAmelCase	)
						__UpperCamelCase					:	str             =      []
						for (start_index, end_index), score in scores:
											assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
											__UpperCamelCase					:	Union[str, Any]             =      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    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Any      	=		VOCAB_FILES_NAMES
	__magic_name__   :       str      	=		READER_PRETRAINED_VOCAB_FILES_MAP
	__magic_name__   :       str      	=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__   :       Tuple      	=		READER_PRETRAINED_INIT_CONFIGURATION
	__magic_name__   :       Dict      	=		['input_ids', 'attention_mask']
	__magic_name__   :       Union[str, Any]      	=		DPRReaderTokenizer






