



"""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 import BertTokenizer


snake_case_    					=	logging.get_logger(__name__)

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

snake_case_    					=	{
    """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"""
        ),
    },
}
snake_case_    					=	{
    """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"""
        ),
    },
}
snake_case_    					=	{
    """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"""
        ),
    },
}

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


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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


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

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


snake_case_    					=	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)

    Returns:
        `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(SCREAMING_SNAKE_CASE_    )
class 				A_      :



						"""simple docstring"""






						def __call__( self			:Dict       ,  lowercase_			:Any       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Union[bool, str] = False       ,  lowercase_			:Union[bool, str] = False       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:Optional[Union[str, TensorType]] = None       ,  lowercase_			:Optional[bool] = None       ,  **lowercase_			:Optional[int]       ,  )    ->      BatchEncoding:
												if titles is None and texts is None:
																		return super().__call__(
																		    lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_       ,  return_attention_mask=lowercase_       ,  **lowercase_       ,  )
												elif titles is None or texts is None:
																		UpperCAmelCase					=			titles if texts is None else texts
																		return super().__call__(
																		    lowercase_       ,  lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_       ,  return_attention_mask=lowercase_       ,  **lowercase_       ,  )
												UpperCAmelCase					=			titles if not isinstance(lowercase_       ,  lowercase_ ) else [titles]
												UpperCAmelCase					=			texts if not isinstance(lowercase_       ,  lowercase_ ) else [texts]
												UpperCAmelCase					=			len(lowercase_ )
												UpperCAmelCase					=			questions if not isinstance(lowercase_       ,  lowercase_ ) else [questions] * n_passages
												if len(lowercase_ ) != len(lowercase_ ):
																		raise ValueError(
																		    f"""There should be as many titles than texts but got {len(lowercase_ )} titles and {len(lowercase_ )} texts.""" )
												UpperCAmelCase					=			super().__call__(lowercase_       ,  lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_ )['input_ids']
												UpperCAmelCase					=			super().__call__(lowercase_       ,  add_special_tokens=lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_ )['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(lowercase_       ,  lowercase_ )
												    ]
												}
												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(lowercase_       ,  padding=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:BatchEncoding       ,  lowercase_			:DPRReaderOutput       ,  lowercase_			:int = 16       ,  lowercase_			:int = 64       ,  lowercase_			:int = 4       ,  )    ->      List[DPRSpanPrediction]:
												UpperCAmelCase					=			reader_input['input_ids']
												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			reader_output[:3]
												UpperCAmelCase					=			len(lowercase_ )
												UpperCAmelCase					=			sorted(range(lowercase_ )       ,  reverse=lowercase_       ,  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(lowercase_ )

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






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:List[int]       ,  lowercase_			:List[int]       ,  lowercase_			:int       ,  lowercase_			:int       ,  )    ->      List[DPRSpanPrediction]:
												UpperCAmelCase					=			[]
												for start_index, start_score in enumerate(lowercase_ ):
																		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(lowercase_       ,  key=lambda lowercase_ : x[1]       ,  reverse=lowercase_ )
												UpperCAmelCase					=			[]
												for (start_index, end_index), score in scores:
																		if start_index > end_index:
																								raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""" )
																		UpperCAmelCase					=			end_index - start_index + 1
																		if length > max_answer_length:
																								raise ValueError(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(lowercase_ ) == top_spans:
																								break
												return chosen_span_intervals







@add_end_docstrings(SCREAMING_SNAKE_CASE_    )
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__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"""]










"""simple docstring"""


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


snake_case_    					=	logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:CLIPSegForImageSegmentation       ,  lowercase_			:CLIPSegProcessor       ,  lowercase_			:AutoencoderKL       ,  lowercase_			:CLIPTextModel       ,  lowercase_			:CLIPTokenizer       ,  lowercase_			:UNetaDConditionModel       ,  lowercase_			:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,  lowercase_			:StableDiffusionSafetyChecker       ,  lowercase_			:CLIPImageProcessor       ,  )    ->      List[str]:
												super().__init__()

												if hasattr(scheduler.config       ,  'steps_offset' ) and scheduler.config.steps_offset != 1:
																		UpperCAmelCase					=			(
																		    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																		    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																		    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
																		    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
																		    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
																		    ' file'
																		)
																		deprecate('steps_offset!=1'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			1
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if hasattr(scheduler.config       ,  'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
																		UpperCAmelCase					=			(
																		    f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
																		    ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
																		    ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
																		    ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
																		    ' Hub, it would be very nice if you could open a Pull request for the'
																		    ' `scheduler/scheduler_config.json` file'
																		)
																		deprecate('skip_prk_steps not set'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			True
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if safety_checker is None:
																		logger.warning(
																		    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
																		    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
																		    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
																		    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
																		    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )

												self.register_modules(
												    segmentation_model=lowercase_       ,  segmentation_processor=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  unet=lowercase_       ,  scheduler=lowercase_       ,  safety_checker=lowercase_       ,  feature_extractor=lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Optional[Union[str, int]] = "auto" )    ->      int:
												if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		UpperCAmelCase					=			self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												self.enable_attention_slicing(lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      int:
												if is_accelerate_available():
																		from accelerate import cpu_offload
												else:
																		raise ImportError('Please install accelerate via `pip install accelerate`' )

												UpperCAmelCase					=			torch.device('cuda' )

												for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																		if cpu_offloaded_model is not None:
																								cpu_offload(lowercase_       ,  lowercase_ )






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      List[str]:
												if self.device != torch.device('meta' ) or not hasattr(self.unet       ,  '_hf_hook' ):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(lowercase_       ,  '_hf_hook' )
																		    and hasattr(module._hf_hook       ,  'execution_device' )
																		    and module._hf_hook.execution_device is not None
																		):
																								return torch.device(module._hf_hook.execution_device )
												return self.device






						@torch.no_grad()
						def __call__( self			:Optional[Any]       ,  lowercase_			:Union[str, List[str]]       ,  lowercase_			:Union[torch.FloatTensor, PIL.Image.Image]       ,  lowercase_			:str       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 50       ,  lowercase_			:float = 7.5       ,  lowercase_			:Optional[Union[str, List[str]]] = None       ,  lowercase_			:Optional[int] = 1       ,  lowercase_			:float = 0.0       ,  lowercase_			:Optional[torch.Generator] = None       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:Optional[str] = "pil"       ,  lowercase_			:bool = True       ,  lowercase_			:Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,  lowercase_			:int = 1       ,  **lowercase_			:int       ,  )    ->      int:
												UpperCAmelCase					=			self.segmentation_processor(
												    text=[text]       ,  images=[image]       ,  padding='max_length'       ,  return_tensors='pt' ).to(self.device )
												UpperCAmelCase					=			self.segmentation_model(**lowercase_ )
												UpperCAmelCase					=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
												UpperCAmelCase					=			self.numpy_to_pil(lowercase_ )[0].resize(image.size )

												# Run inpainting pipeline with the generated mask
												UpperCAmelCase					=			StableDiffusionInpaintPipeline(
												    vae=self.vae       ,  text_encoder=self.text_encoder       ,  tokenizer=self.tokenizer       ,  unet=self.unet       ,  scheduler=self.scheduler       ,  safety_checker=self.safety_checker       ,  feature_extractor=self.feature_extractor       ,  )
												return inpainting_pipeline(
												    prompt=lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  height=lowercase_       ,  width=lowercase_       ,  num_inference_steps=lowercase_       ,  guidance_scale=lowercase_       ,  negative_prompt=lowercase_       ,  num_images_per_prompt=lowercase_       ,  eta=lowercase_       ,  generator=lowercase_       ,  latents=lowercase_       ,  output_type=lowercase_       ,  return_dict=lowercase_       ,  callback=lowercase_       ,  callback_steps=lowercase_       ,  )





