"""simple docstring"""





from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


__A	=					logging.get_logger(__name__)
@add_end_docstrings(__A		)
class 			_snake_case  (       __A		):






						def __init__(							self   :      Optional[Any]				,       **UpperCAmelCase   :      List[str]     ):
													super().__init__(**lowerCAmelCase_     )

													if self.framework != "pt":
																				raise ValueError(F"""The {self.__class__} is only available in PyTorch."""     )
													# No specific FOR_XXX available yet






						def __call__(							self   :      List[str]				,       UpperCAmelCase   :      Union[np.ndarray, bytes, str]				,       **UpperCAmelCase   :      List[str]     ):
													return super().__call__(lowerCAmelCase_				,       **lowerCAmelCase_     )






						def    lowerCamelCase__  (							self   :      str				,       **UpperCAmelCase   :      Any     ):
													__lowerCamelCase       :		Optional[Any]		 =  {}
													if "candidate_labels" in kwargs:
																				__lowerCamelCase       :		int		 =  kwargs["candidate_labels"]
													if "hypothesis_template" in kwargs:
																				__lowerCamelCase       :		List[str]		 =  kwargs["hypothesis_template"]

													return preprocess_params, {}, {}






						def    lowerCamelCase__  (							self   :      str				,       UpperCAmelCase   :      List[Any]				,       UpperCAmelCase   :      Any=None				,       UpperCAmelCase   :      List[str]="This is a sound of {}."     ):
													if isinstance(lowerCAmelCase_				,       lowerCAmelCase_     ):
																				if audio.startswith("http://"     ) or audio.startswith("https://"     ):
																											# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																											# like http_huggingface_co.png
																											__lowerCamelCase       :		Any		 =  requests.get(lowerCAmelCase_     ).content
																				else:
																											with open(lowerCAmelCase_				,       "rb"     ) as f:
																																		__lowerCamelCase       :		Dict		 =  f.read()

													if isinstance(lowerCAmelCase_				,       lowerCAmelCase_     ):
																				__lowerCamelCase       :		Optional[Any]		 =  ffmpeg_read(lowerCAmelCase_				,       self.feature_extractor.sampling_rate     )

													if not isinstance(lowerCAmelCase_				,       np.ndarray     ):
																				raise ValueError("We expect a numpy ndarray as input"     )
													if len(audio.shape     ) != 1:
																				raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline"     )

													__lowerCamelCase       :		Union[str, Any]		 =  self.feature_extractor(
													    [audio]				,       sampling_rate=self.feature_extractor.sampling_rate				,       return_tensors="pt"     )
													__lowerCamelCase       :		Any		 =  candidate_labels
													__lowerCamelCase       :		Union[str, Any]		 =  [hypothesis_template.format(lowerCAmelCase_     ) for x in candidate_labels]
													__lowerCamelCase       :		List[str]		 =  self.tokenizer(lowerCAmelCase_				,       return_tensors=self.framework				,       padding=lowerCAmelCase_     )
													__lowerCamelCase       :		int		 =  [text_inputs]
													return inputs






						def    lowerCamelCase__  (							self   :      List[str]				,       UpperCAmelCase   :      Optional[int]     ):
													__lowerCamelCase       :		Dict		 =  model_inputs.pop("candidate_labels"     )
													__lowerCamelCase       :		List[str]		 =  model_inputs.pop("text_inputs"     )
													if isinstance(text_inputs[0]				,       lowerCAmelCase_     ):
																				__lowerCamelCase       :		Optional[int]		 =  text_inputs[0]
													else:
																				# Batching case.
																				__lowerCamelCase       :		Optional[Any]		 =  text_inputs[0][0]

													__lowerCamelCase       :		Any		 =  self.model(**lowerCAmelCase_				,       **lowerCAmelCase_     )

													__lowerCamelCase       :		str		 =  {
													    "candidate_labels": candidate_labels,
													    "logits": outputs.logits_per_audio,
													}
													return model_outputs







						def    lowerCamelCase__  (							self   :      str				,       UpperCAmelCase   :      Optional[Any]     ):
													__lowerCamelCase       :		int		 =  model_outputs.pop("candidate_labels"     )
													__lowerCamelCase       :		Optional[int]		 =  model_outputs["logits"][0]

													if self.framework == "pt":
																				__lowerCamelCase       :		Tuple		 =  logits.softmax(dim=0     )
																				__lowerCamelCase       :		int		 =  probs.tolist()
													else:
																				raise ValueError("`tf` framework not supported."     )

													__lowerCamelCase       :		Union[str, Any]		 =  [
													    {"score": score, "label": candidate_label}
													    for score, candidate_label in sorted(zip(lowerCAmelCase_				,       lowerCAmelCase_     )				,       key=lambda UpperCAmelCase     : -x[0]     )
													]
													return result







"""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''': 512}

def 							snake_case (  A__		):
			UpperCAmelCase_	:					str     	=   set()
			UpperCAmelCase_	:					int     	=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						UpperCAmelCase_	:					Dict     	=   char

			UpperCAmelCase_	:					List[Any]     	=   set(A__		)
			return pairs







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']





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

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





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





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





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

				UpperCAmelCase_	:					Tuple     	=   token.split(" "		)
				UpperCAmelCase_	:					int     	=   []
				for token in tokens:
							if not len(lowerCAmelCase_		):
										continue

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

							if not pairs:
										words.append(lowerCAmelCase_		)
										continue

							while True:
										UpperCAmelCase_	:					Union[str, Any]     	=   min(lowerCAmelCase_      ,      key=lambda lowerCAmelCase_		: self.bpe_ranks.get(lowerCAmelCase_      ,      float("inf"		)		)		)
										if bigram not in self.bpe_ranks:
													break
										UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   bigram
										UpperCAmelCase_	:					Dict     	=   []
										UpperCAmelCase_	:					Dict     	=   0

										while i < len(lowerCAmelCase_		):
													try:
																UpperCAmelCase_	:					int     	=   word.index(lowerCAmelCase_      ,      lowerCAmelCase_		)
																new_word.extend(word[i:j]		)
																UpperCAmelCase_	:					Any     	=   j
													except ValueError:
																new_word.extend(word[i:]		)
																break

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

							UpperCAmelCase_	:					Optional[Any]     	=   word
							words.append(lowerCAmelCase_		)
				return " ".join(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   []

				UpperCAmelCase_	:					List[Any]     	=   re.findall(R"\S+\n?"      ,      lowerCAmelCase_		)

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					str		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   token.lower()
				return self.encoder.get(lowerCAmelCase_      ,      self.encoder.get(self.unk_token		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					int		)		->     str:
				return self.decoder.get(lowerCAmelCase_      ,      self.unk_token		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   " ".join(lowerCAmelCase_		).replace("@@ "      ,      ""		).strip()
				return out_string





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

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

				UpperCAmelCase_	:					Any     	=   0
				with open(lowerCAmelCase_      ,      "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 lowerCAmelCase_		: 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!"		)
													UpperCAmelCase_	:					List[Any]     	=   token_index
										writer.write(" ".join(lowerCAmelCase_		) + "\n"		)
										index += 1

				return vocab_file, merge_file



