
'''simple docstring'''
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from ..image_utils import load_image


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase__											=logging.get_logger(__name__)


lowercase__											=Dict[str, Any]
lowercase__											=List[Prediction]







@add_end_docstrings(__snake_case					)
class   a_ (      __snake_case					):




			def __init__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				super().__init__(*UpperCAmelCase					,   **UpperCAmelCase	)

				if self.framework == "tf":
					raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''	)

				requires_backends(self					,   """vision"""	)
				self.check_model_type(
				    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()	)	)




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				a_      				=						{}
				if "threshold" in kwargs:
					a_      				=						kwargs["""threshold"""]
				return {}, {}, postprocess_kwargs




			def __call__(       self					,   *UpperCAmelCase					,   **UpperCAmelCase	):
				return super().__call__(*UpperCAmelCase					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						load_image(UpperCAmelCase	)
				a_      				=						torch.IntTensor([[image.height, image.width]]	)
				a_      				=						self.image_processor(images=[image]					,   return_tensors="""pt"""	)
				if self.tokenizer is not None:
					a_      				=						self.tokenizer(text=inputs["""words"""]					,   boxes=inputs["""boxes"""]					,   return_tensors="""pt"""	)
				a_      				=						target_size
				return inputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						model_inputs.pop("""target_size"""	)
				a_      				=						self.model(**UpperCAmelCase	)
				a_      				=						outputs.__class__({"""target_size""": target_size, **outputs}	)
				if self.tokenizer is not None:
					a_      				=						model_inputs["""bbox"""]
				return model_outputs




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=0.9	):
				a_      				=						model_outputs["""target_size"""]
				if self.tokenizer is not None:
					# This is a LayoutLMForTokenClassification variant.
					# The OCR got the boxes and the model classified the words.
					a_      				=						target_size[0].tolist()

					def unnormalize(UpperCAmelCase	):
						return self._get_bounding_box(
						    torch.Tensor(
						        [
						            (width * bbox[0] / 10_00),
						            (height * bbox[1] / 10_00),
						            (width * bbox[2] / 10_00),
						            (height * bbox[3] / 10_00),
						        ]	)	)

					a_      				=						model_outputs["""logits"""].squeeze(0	).softmax(dim=-1	).max(dim=-1	)
					a_      				=						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
					a_      				=						[unnormalize(UpperCAmelCase	) for bbox in model_outputs["""bbox"""].squeeze(0	)]
					a_      				=						["""score""", """label""", """box"""]
					a_      				=						[dict(zip(UpperCAmelCase					,   UpperCAmelCase	)	) for vals in zip(scores.tolist()					,   UpperCAmelCase					,   UpperCAmelCase	) if vals[0] > threshold]
				else:
					# This is a regular ForObjectDetectionModel
					a_      				=						self.image_processor.post_process_object_detection(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						raw_annotations[0]
					a_      				=						raw_annotation["""scores"""]
					a_      				=						raw_annotation["""labels"""]
					a_      				=						raw_annotation["""boxes"""]

					a_      				=						scores.tolist()
					a_      				=						[self.model.config.idalabel[label.item()] for label in labels]
					a_      				=						[self._get_bounding_box(UpperCAmelCase	) for box in boxes]

					# {"scores": [...], ...} --> [{"score":x, ...}, ...]
					a_      				=						["""score""", """label""", """box"""]
					a_      				=						[
					    dict(zip(UpperCAmelCase					,   UpperCAmelCase	)	)
					    for vals in zip(raw_annotation["""scores"""]					,   raw_annotation["""labels"""]					,   raw_annotation["""boxes"""]	)
					]

				return annotation






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if self.framework != "pt":
					raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""	)
				a_      				=						box.int().tolist()
				a_      				=						{
				    """xmin""": xmin,
				    """ymin""": ymin,
				    """xmax""": xmax,
				    """ymax""": ymax,
				}
				return bbox








"""simple docstring"""







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase            =	logging.get_logger(__name__)


_lowerCAmelCase            =	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_lowerCAmelCase            =	{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_lowerCAmelCase            =	{'facebook/blenderbot_small-90M': 5_12}


def      UpperCamelCase       ( _A )     ->       Dict:
				lowercase    :      Union[str, Any]       = set()
				lowercase    :      Union[str, Any]       = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								lowercase    :      List[str]       = char

				lowercase    :      Optional[Any]       = set(_A )
				return pairs


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : str												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Any												=		["""input_ids""", """attention_mask"""]






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :str			, __magic_name__  :Union[str, Any]="__start__"			, __magic_name__  :Optional[int]="__end__"			, __magic_name__  :Tuple="__unk__"			, __magic_name__  :Optional[int]="__null__"			, **__magic_name__  :Tuple			, )      ->Tuple:
										super().__init__(unk_token=__magic_name__			, bos_token=__magic_name__			, eos_token=__magic_name__			, pad_token=__magic_name__			, **__magic_name__				)

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      int       = json.load(__magic_name__				)
										lowercase    :      int       = {v: k for k, v in self.encoder.items()}
										with open(__magic_name__			, encoding="""utf-8"""				) as merges_handle:
														lowercase    :      Any       = merges_handle.read().split("""\n"""				)[1:-1]
										lowercase    :      Union[str, Any]       = [tuple(merge.split()				) for merge in merges]
										lowercase    :      Any       = dict(zip(__magic_name__			, range(len(__magic_name__				)				)				)				)
										lowercase    :      Union[str, Any]       = {}






						@property
						def      __snake_case      (		self  :int				)      ->int:
										return len(self.encoder				)






						def      __snake_case      (		self  :Optional[Any]				)      ->Dict:
										return dict(self.encoder			, **self.added_tokens_encoder				)






						def      __snake_case      (		self  :List[str]			, __magic_name__  :str				)      ->str:
										if token in self.cache:
														return self.cache[token]
										lowercase    :      Dict       = re.sub("""([.,!?()])"""			, r""" \1"""			, __magic_name__				)
										lowercase    :      Tuple       = re.sub("""(')"""			, r""" \1 """			, __magic_name__				)
										lowercase    :      List[str]       = re.sub(r"""\s{2,}"""			, """ """			, __magic_name__				)
										if "\n" in token:
														lowercase    :      int       = token.replace("""\n"""			, """ __newln__"""				)

										lowercase    :      Tuple       = token.split(""" """				)
										lowercase    :      Dict       = []
										for token in tokens:
														if not len(__magic_name__				):
																		continue

														lowercase    :      List[Any]       = token.lower()
														lowercase    :      int       = tuple(__magic_name__				)
														lowercase    :      List[str]       = tuple(list(word[:-1]				) + [word[-1] + """</w>"""]				)
														lowercase    :      List[str]       = get_pairs(__magic_name__				)

														if not pairs:
																		words.append(__magic_name__				)
																		continue

														while True:
																		lowercase    :      Optional[Any]       = min(__magic_name__			, key=lambda __magic_name__				: self.bpe_ranks.get(__magic_name__			, float("""inf"""				)				)				)
																		if bigram not in self.bpe_ranks:
																						break
																		lowercase      ,      lowercase    :      List[str]       = bigram
																		lowercase    :      Dict       = []
																		lowercase    :      Optional[int]       = 0

																		while i < len(__magic_name__				):
																						try:
																										lowercase    :      Optional[Any]       = word.index(__magic_name__			, __magic_name__				)
																										new_word.extend(word[i:j]				)
																										lowercase    :      Union[str, Any]       = j
																						except ValueError:
																										new_word.extend(word[i:]				)
																										break

																						if word[i] == first and i < len(__magic_name__				) - 1 and word[i + 1] == second:
																										new_word.append(first + second				)
																										i += 2
																						else:
																										new_word.append(word[i]				)
																										i += 1
																		lowercase    :      str       = tuple(__magic_name__				)
																		lowercase    :      Optional[Any]       = new_word
																		if len(__magic_name__				) == 1:
																						break
																		else:
																						lowercase    :      List[Any]       = get_pairs(__magic_name__				)
														lowercase    :      int       = """@@ """.join(__magic_name__				)
														lowercase    :      int       = word[:-4]

														lowercase    :      List[str]       = word
														words.append(__magic_name__				)
										return " ".join(__magic_name__				)






						def      __snake_case      (		self  :List[str]			, __magic_name__  :str				)      ->List[str]:
										lowercase    :      str       = []

										lowercase    :      Dict       = re.findall(r"""\S+\n?"""			, __magic_name__				)

										for token in words:
														split_tokens.extend(list(self.bpe(__magic_name__				).split(""" """				)				)				)
										return split_tokens






						def      __snake_case      (		self  :int			, __magic_name__  :str				)      ->int:
										lowercase    :      Tuple       = token.lower()
										return self.encoder.get(__magic_name__			, self.encoder.get(self.unk_token				)				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :int				)      ->str:
										return self.decoder.get(__magic_name__			, self.unk_token				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :List[str]				)      ->str:
										lowercase    :      Tuple       = """ """.join(__magic_name__				).replace("""@@ """			, """"""				).strip()
										return out_string






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
										lowercase    :      List[str]       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

										with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as f:
														f.write(json.dumps(self.encoder			, indent=2			, sort_keys=__magic_name__			, ensure_ascii=__magic_name__				) + """\n"""				)

										lowercase    :      Any       = 0
										with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as writer:
														writer.write("""#version: 0.2\n"""				)
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			, key=lambda __magic_name__				: kv[1]				):
																		if index != token_index:
																						logger.warning(
																						    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																						    """ Please check that the tokenizer is not corrupted!"""				)
																						lowercase    :      Any       = token_index
																		writer.write(""" """.join(__magic_name__				) + """\n"""				)
																		index += 1

										return vocab_file, merge_file
