



"""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


_lowerCAmelCase    :int     =					logging.get_logger(__name__)

_lowerCAmelCase    :Optional[int]     =					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase    :Optional[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'
        ),
    },
}
_lowerCAmelCase    :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'
        ),
    },
}
_lowerCAmelCase    :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'
        ),
    },
}

_lowerCAmelCase    :Union[str, Any]     =					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
_lowerCAmelCase    :Dict     =					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
_lowerCAmelCase    :Union[str, Any]     =					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


_lowerCAmelCase    :Any     =					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_lowerCAmelCase    :int     =					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
_lowerCAmelCase    :Dict     =					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     a__									=CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     a__									=DPRContextEncoderTokenizer



class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     a__									=QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     a__									=DPRQuestionEncoderTokenizer



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

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


_lowerCAmelCase    :Optional[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(a      )
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __call__(					self       ,	A       ,	A = None       ,	A = None       ,	A = False       ,	A = False       ,	A = None       ,	A = None       ,	A = None       ,	**A       ,	)							->					BatchEncoding:
         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:
             _UpperCAmelCase							:  int											=    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       ,	)
         _UpperCAmelCase							:  Optional[Any]											=    titles if not isinstance(A       ,	A		) else [titles]
         _UpperCAmelCase							:  int											=    texts if not isinstance(A       ,	A		) else [texts]
         _UpperCAmelCase							:  List[str]											=    len(A		)
         _UpperCAmelCase							:  List[str]											=    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.'
         _UpperCAmelCase							:  Optional[Any]											=    super().__call__(A       ,	A       ,	padding=A       ,	truncation=A		)['''input_ids''']
         _UpperCAmelCase							:  Union[str, Any]											=    super().__call__(A       ,	add_special_tokens=A       ,	padding=A       ,	truncation=A		)['''input_ids''']
         _UpperCAmelCase							:  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:
             _UpperCAmelCase							:  Optional[int]											=    []
             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							:  str											=    attention_mask
         return self.pad(A       ,	padding=A       ,	max_length=A       ,	return_tensors=A		)





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

             _UpperCAmelCase							:  Tuple											=    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  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	)							->					List[DPRSpanPrediction]:
         _UpperCAmelCase							:  List[str]											=    []
         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)		)
         _UpperCAmelCase							:  int											=    sorted(A       ,	key=lambda A		: x[1]       ,	reverse=A		)
         _UpperCAmelCase							:  Dict											=    []
         for (start_index, end_index), score in scores:
             assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
             _UpperCAmelCase							:  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(A		) == top_spans:
                 break
         return chosen_span_intervals



@add_end_docstrings(a      )
class 							_UpperCAmelCase							( a		,a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=READER_PRETRAINED_VOCAB_FILES_MAP
     a__									=READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=READER_PRETRAINED_INIT_CONFIGURATION
     a__									=['''input_ids''', '''attention_mask''']
     a__									=DPRReaderTokenizer







"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


