



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__     = logging.get_logger(__name__)

UpperCAmelCase__     = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__     = {
    "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__     = {
    "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__     = {
    "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__     = {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCAmelCase__     = {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCAmelCase__     = {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


UpperCAmelCase__     = {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__     = {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__     = {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}







class     __lowerCAmelCase					(    A		):
			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     __lowerCAmelCase					(    A		):
			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





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

UpperCAmelCase__     = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase__     = 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     __lowerCAmelCase					:



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



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						BatchEncoding				,	A     :						DPRReaderOutput				,	A     :						int = 16				,	A     :						int = 64				,	A     :						int = 4				,	)     ->					List[DPRSpanPrediction]:



						"""simple docstring"""






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

									_UpperCAmelCase	 =     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     :						Optional[Any]				,	A     :						List[int]				,	A     :						List[int]				,	A     :						int				,	A     :						int				,	)     ->					List[DPRSpanPrediction]:



						"""simple docstring"""






						_UpperCAmelCase	 =     []
						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	 =     sorted(A				,	key=lambda A: x[1]				,	reverse=A)
						_UpperCAmelCase	 =     []
						for (start_index, end_index), score in scores:
									assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
									_UpperCAmelCase	 =     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     __lowerCAmelCase					(    A       ,		A		):
			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





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  IFInpaintingPipeline
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase           =  PipelineTesterMixin.required_optional_params - {'''latents'''}



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(A)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=A).manual_seed(A)

						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)
						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)

						_UpperCAmelCase	 =     {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}

						return inputs



			@unittest.skipIf(
			    torch_device != 'cuda' or not is_xformers_available()				,	reason='XFormers attention is only available with CUDA and `xformers` installed'				,	)
			def 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != 'cuda'				,	reason='float16 requires CUDA')
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1E-1)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2				,	)
