






"""simple docstring"""



from typing import List, Union

import numpy as np

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 PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

A_    =							logging.get_logger(__name__)






@add_end_docstrings(UpperCamelCase		)
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						List[Any]			,    *snake_case      :						str			,    **snake_case      :						List[str]							):
													'''simple docstring'''





													super().__init__(*snake_case			,    **snake_case							)
													requires_backends(self			,    """vision"""							)
													self.check_model_type(snake_case							)


							def __call__(			self      :						Dict			,    snake_case      :						Union[str, List[str], "Image.Image", List["Image.Image"]]			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





													return super().__call__(snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													return {}, {}, {}


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						List[Any]							):
													'''simple docstring'''





													A__						:  List[str]		  =  load_image(snake_case							)
													A__						:  str		  =  image.size
													A__						:  int		  =  self.image_processor(images=snake_case			,    return_tensors=self.framework							)
													return model_inputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  self.model(**snake_case							)
													return model_outputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Dict							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  model_outputs.predicted_depth
													A__						:  Tuple		  =  torch.nn.functional.interpolate(
													    predicted_depth.unsqueeze(1							)			,    size=self.image_size[::-1]			,    mode="""bicubic"""			,    align_corners=snake_case							)
													A__						:  List[Any]		  =  prediction.squeeze().cpu().numpy()
													A__						:  List[str]		  =  (output * 255 / np.max(snake_case							)).astype("""uint8"""							)
													A__						:  Dict		  =  Image.fromarray(snake_case							)
													A__						:  int		  =  {}
													A__						:  List[str]		  =  predicted_depth
													A__						:  Tuple		  =  depth
													return output_dict














"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A_    =							logging.get_logger(__name__)


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

A_    =							{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

A_    =							{'''facebook/blenderbot-3B''': 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _lowerCAmelCase		(							)		->Any:
						A__						:  Any		  =  (
						    list(range(ord("""!"""      ),						ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      ),						ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      ),						ord("""ÿ"""      ) + 1      )      )
						)
						A__						:  Optional[int]		  =  bs[:]
						A__						:  Union[str, Any]		  =  0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCAmelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						A__						:  Optional[int]		  =  [chr(UpperCAmelCase__      ) for n in cs]
						return dict(zip(UpperCAmelCase__,						UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->Tuple:
						A__						:  Any		  =  set()
						A__						:  Optional[int]		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  Dict		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						int			,    snake_case      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						str="replace"			,    snake_case      :						Tuple="<s>"			,    snake_case      :						Dict="</s>"			,    snake_case      :						Optional[Any]="</s>"			,    snake_case      :						int="<s>"			,    snake_case      :						Dict="<unk>"			,    snake_case      :						List[Any]="<pad>"			,    snake_case      :						Union[str, Any]="<mask>"			,    snake_case      :						Optional[Any]=False			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else bos_token
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else eos_token
													A__						:  List[str]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else sep_token
													A__						:  Tuple		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else cls_token
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else unk_token
													A__						:  int		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  str		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

													super().__init__(
													    errors=snake_case			,    bos_token=snake_case			,    eos_token=snake_case			,    unk_token=snake_case			,    sep_token=snake_case			,    cls_token=snake_case			,    pad_token=snake_case			,    mask_token=snake_case			,    add_prefix_space=snake_case			,    **snake_case			,    )

													with open(snake_case			,    encoding="""utf-8"""							) as vocab_handle:
																			A__						:  Dict		  =  json.load(snake_case							)
													A__						:  Tuple		  =  {v: k for k, v in self.encoder.items()}
													A__						:  Union[str, Any]		  =  errors  # how to handle errors in decoding
													A__						:  int		  =  bytes_to_unicode()
													A__						:  str		  =  {v: k for k, v in self.byte_encoder.items()}
													with open(snake_case			,    encoding="""utf-8"""							) as merges_handle:
																			A__						:  Tuple		  =  merges_handle.read().split("""\n"""							)[1:-1]
													A__						:  Optional[Any]		  =  [tuple(merge.split()							) for merge in bpe_merges]
													A__						:  List[str]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Union[str, Any]		  =  {}
													A__						:  int		  =  add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Optional[Any]		  =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													return dict(self.encoder			,    **self.added_tokens_encoder							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  Tuple		  =  tuple(snake_case							)
													A__						:  Union[str, Any]		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

													while True:
																			A__						:  Any		  =  min(snake_case			,    key=lambda snake_case							: self.bpe_ranks.get(snake_case			,    float("""inf"""							)							)							)
																			if bigram not in self.bpe_ranks:
																									break
																			A__  ,  A__						:  Optional[int]		  =  bigram
																			A__						:  Tuple		  =  []
																			A__						:  Dict		  =  0
																			while i < len(snake_case							):
																									try:
																															A__						:  int		  =  word.index(snake_case			,    snake_case							)
																									except ValueError:
																															new_word.extend(word[i:]							)
																															break
																									else:
																															new_word.extend(word[i:j]							)
																															A__						:  Optional[Any]		  =  j

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  Optional[int]		  =  tuple(snake_case							)
																			A__						:  str		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  List[str]		  =  get_pairs(snake_case							)
													A__						:  int		  =  """ """.join(snake_case							)
													A__						:  Optional[int]		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  Optional[Any]		  =  """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return self.encoder.get(snake_case			,    self.encoder.get(self.unk_token							)							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  """""".join(snake_case							)
													A__						:  Dict		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__						:  Any		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													A__						:  List[Any]		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

													A__						:  str		  =  0
													with open(snake_case			,    """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 snake_case							: 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!"""							)
																															A__						:  List[str]		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=snake_case			,    token_ids_a=snake_case			,    already_has_special_tokens=snake_case							)

													if token_ids_a is None:
																			return [1] + ([0] * len(snake_case							)) + [1]
													return [1] + ([0] * len(snake_case							)) + [1, 1] + ([0] * len(snake_case							)) + [1]


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  [self.sep_token_id]
													A__						:  Optional[Any]		  =  [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep							) * [0]
													return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Optional[int]			,    snake_case      :						List[Any]=False			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  int		  =  """ """ + text
													return (text, kwargs)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													return token_ids_a + [self.eos_token_id]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						"Conversation"							):
													'''simple docstring'''





													A__						:  str		  =  []
													for is_user, text in conversation.iter_texts():
																			if is_user:
																									# We need to space prefix as it's being done within blenderbot
																									inputs.append(""" """ + text							)
																			else:
																									# Generated responses should contain them already.
																									inputs.append(snake_case							)

													A__						:  Tuple		  =  """  """.join(snake_case							)
													A__						:  Dict		  =  self.encode(snake_case							)
													if len(snake_case							) > self.model_max_length:
																			A__						:  Optional[int]		  =  input_ids[-self.model_max_length :]
																			logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'							)
													return input_ids






