


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def 		lowercase_  (     _UpperCamelCase  ):


   '''simple docstring'''







   return 1.0 / (1.0 + np.exp(-_outputs  ))


def 		lowercase_  (     _UpperCamelCase  ):


   '''simple docstring'''







   __lowercase 					=   np.max(_outputs       ,			axis=-1       ,			keepdims=_UpperCamelCase  )
   __lowercase 					=   np.exp(_outputs - maxes  )
   return shifted_exp / shifted_exp.sum(axis=-1       ,			keepdims=_UpperCamelCase  )
class 	lowerCamelCase_       (				lowerCAmelCase__				):



     '''simple docstring'''







     __UpperCAmelCase            =				"sigmoid"
     __UpperCAmelCase            =				"softmax"
     __UpperCAmelCase            =				"none"




@add_end_docstrings(
    lowerCAmelCase__     ,     R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "     ,     )
class 	lowerCamelCase_       (				lowerCAmelCase__				):



     '''simple docstring'''







     __UpperCAmelCase            =				False
     __UpperCAmelCase            =				ClassificationFunction.NONE






     def __init__(				self ,       **snake_case_						)   ->			Union[str, Any]:







        '''simple docstring'''

        super().__init__(**snake_case_						)

        self.check_model_type(
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
            if self.framework == '''tf'''
            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING						)






     def A      (				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_="" ,       **snake_case_						)   ->			List[str]:







        '''simple docstring'''

        __lowercase 					=   tokenizer_kwargs

        __lowercase 					=   {}
        if hasattr(self.model.config ,       '''return_all_scores'''						) and return_all_scores is None:
           __lowercase 					=   self.model.config.return_all_scores

        if isinstance(snake_case_ ,       snake_case_						) or top_k is None:
           __lowercase 					=   top_k
           __lowercase 					=   False
        elif return_all_scores is not None:
           warnings.warn(
               '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
               ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.''' ,       snake_case_ ,       )
           if return_all_scores:
              __lowercase 					=   None
           else:
              __lowercase 					=   1

        if isinstance(snake_case_ ,       snake_case_						):
           __lowercase 					=   ClassificationFunction[function_to_apply.upper()]

        if function_to_apply is not None:
           __lowercase 					=   function_to_apply
        return preprocess_params, {}, postprocess_params






     def __call__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Union[str, Any]:







        '''simple docstring'''

        __lowercase 					=   super().__call__(*snake_case_ ,       **snake_case_						)
        # TODO try and retrieve it in a nicer way from _sanitize_parameters.
        __lowercase 					=   '''top_k''' not in kwargs
        if isinstance(args[0] ,       snake_case_						) and _legacy:
           # This pipeline is odd, and return a list when single item is run
           return [result]
        else:
           return result






     def A      (				self ,       snake_case_ ,       **snake_case_						)   ->			Dict[str, GenericTensor]:







        '''simple docstring'''

        __lowercase 					=   self.framework
        if isinstance(snake_case_ ,       snake_case_						):
           return self.tokenizer(**snake_case_ ,       return_tensors=snake_case_ ,       **snake_case_						)
        elif isinstance(snake_case_ ,       snake_case_						) and len(snake_case_						) == 1 and isinstance(inputs[0] ,       snake_case_						) and len(inputs[0]						) == 2:
           # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
           return self.tokenizer(
               text=inputs[0][0] ,       text_pair=inputs[0][1] ,       return_tensors=snake_case_ ,       **snake_case_						)
        elif isinstance(snake_case_ ,       snake_case_						):
           # This is likely an invalid usage of the pipeline attempting to pass text pairs.
           raise ValueError(
               '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
               ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''						)
        return self.tokenizer(snake_case_ ,       return_tensors=snake_case_ ,       **snake_case_						)






     def A      (				self ,       snake_case_						)   ->			List[str]:







        '''simple docstring'''

        return self.model(**snake_case_						)






     def A      (				self ,       snake_case_ ,       snake_case_=None ,       snake_case_=1 ,       snake_case_=True						)   ->			Any:







        '''simple docstring'''

        if function_to_apply is None:
           if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
              __lowercase 					=   ClassificationFunction.SIGMOID
           elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
              __lowercase 					=   ClassificationFunction.SOFTMAX
           elif hasattr(self.model.config ,       '''function_to_apply'''						) and function_to_apply is None:
              __lowercase 					=   self.model.config.function_to_apply
           else:
              __lowercase 					=   ClassificationFunction.NONE

        __lowercase 					=   model_outputs['''logits'''][0]
        __lowercase 					=   outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
           __lowercase 					=   sigmoid(snake_case_						)
        elif function_to_apply == ClassificationFunction.SOFTMAX:
           __lowercase 					=   softmax(snake_case_						)
        elif function_to_apply == ClassificationFunction.NONE:
           __lowercase 					=   outputs
        else:
           raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'						)

        if top_k == 1 and _legacy:
           return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

        __lowercase 					=   [
            {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(snake_case_						)
        ]
        if not _legacy:
           dict_scores.sort(key=lambda snake_case_						: x["score"] ,       reverse=snake_case_						)
           if top_k is not None:
              __lowercase 					=   dict_scores[:top_k]
        return dict_scores






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


a : int		=				logging.get_logger(__name__)


a : Any		=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
a : Dict		=				{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

a : Optional[int]		=				{
    '''allenai/led-base-16384''': 16384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   (
			    list(range(ord('''!'''  )       ,			ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  )       ,			ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  )       ,			ord('''ÿ'''  ) + 1  )  )
			)
			__lowercase 					=   bs[:]
			__lowercase 					=   0
			for b in range(2**8  ):
						if b not in bs:
									bs.append(_UpperCamelCase  )
									cs.append(2**8 + n  )
									n += 1
			__lowercase 					=   [chr(_UpperCamelCase  ) for n in cs]
			return dict(zip(_UpperCamelCase       ,			_UpperCamelCase  )  )


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   set()
			__lowercase 					=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						__lowercase 					=   char
			return pairs





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["input_ids", "attention_mask"]






					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_="replace" ,       snake_case_="<s>" ,       snake_case_="</s>" ,       snake_case_="</s>" ,       snake_case_="<s>" ,       snake_case_="<unk>" ,       snake_case_="<pad>" ,       snake_case_="<mask>" ,       snake_case_=False ,       **snake_case_ ,       )   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else bos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else eos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else sep_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else cls_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else unk_token
								__lowercase 					=   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
								__lowercase 					=   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:
											__lowercase 					=   json.load(snake_case_						)
								__lowercase 					=   {v: k for k, v in self.encoder.items()}
								__lowercase 					=   errors  # how to handle errors in decoding
								__lowercase 					=   bytes_to_unicode()
								__lowercase 					=   {v: k for k, v in self.byte_encoder.items()}
								with open(snake_case_ ,       encoding='''utf-8'''						) as merges_handle:
											__lowercase 					=   merges_handle.read().split('''\n'''						)[1:-1]
								__lowercase 					=   [tuple(merge.split()						) for merge in bpe_merges]
								__lowercase 					=   dict(zip(snake_case_ ,       range(len(snake_case_						)						)						)						)
								__lowercase 					=   {}
								__lowercase 					=   add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								__lowercase 					=   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.bart.tokenization_bart.BartTokenizer.vocab_size
					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								return len(self.encoder						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if token in self.cache:
											return self.cache[token]
								__lowercase 					=   tuple(snake_case_						)
								__lowercase 					=   get_pairs(snake_case_						)

								if not pairs:
											return token

								while True:
											__lowercase 					=   min(snake_case_ ,       key=lambda snake_case_						: self.bpe_ranks.get(snake_case_ ,       float('''inf'''						)						)						)
											if bigram not in self.bpe_ranks:
														break
											__lowercase					, __lowercase 					=   bigram
											__lowercase 					=   []
											__lowercase 					=   0
											while i < len(snake_case_						):
														try:
																	__lowercase 					=   word.index(snake_case_ ,       snake_case_						)
														except ValueError:
																	new_word.extend(word[i:]						)
																	break
														else:
																	new_word.extend(word[i:j]						)
																	__lowercase 					=   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
											__lowercase 					=   tuple(snake_case_						)
											__lowercase 					=   new_word
											if len(snake_case_						) == 1:
														break
											else:
														__lowercase 					=   get_pairs(snake_case_						)
								__lowercase 					=   ''' '''.join(snake_case_						)
								__lowercase 					=   word
								return word






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   []
								for token in re.findall(self.pat ,       snake_case_						):
											__lowercase 					=   ''''''.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 A      (				self ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								return self.decoder.get(snake_case_						)






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   ''''''.join(snake_case_						)
								__lowercase 					=   bytearray([self.byte_decoder[c] for c in text]						).decode('''utf-8''' ,       errors=self.errors						)
								return text






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple[str]:







								'''simple docstring'''

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

								__lowercase 					=   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!'''						)
																	__lowercase 					=   token_index
														writer.write(''' '''.join(snake_case_						) + '''\n'''						)
														index += 1

								return vocab_file, merge_file






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowercase 					=   [self.cls_token_id]
								__lowercase 					=   [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = False						)   ->			List[int]:







								'''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 A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								__lowercase 					=   [self.sep_token_id]
								__lowercase 					=   [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 A      (				self ,       snake_case_ ,       snake_case_=False ,       **snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   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()):
											__lowercase 					=   ''' ''' + text
								return (text, kwargs)






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = PaddingStrategy.DO_NOT_PAD ,       snake_case_ = None ,       snake_case_ = None ,       )   ->			dict:







								'''simple docstring'''

								__lowercase 					=   super()._pad(
								    encoded_inputs=snake_case_ ,       max_length=snake_case_ ,       padding_strategy=snake_case_ ,       pad_to_multiple_of=snake_case_ ,       return_attention_mask=snake_case_ ,       )

								# Load from model defaults
								if return_attention_mask is None:
											__lowercase 					=   '''attention_mask''' in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
											__lowercase 					=   encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											__lowercase 					=   len(encoded_inputs['''global_attention_mask''']						) != len(snake_case_						)

											if needs_to_be_padded:
														__lowercase 					=   len(snake_case_						) - len(encoded_inputs['''global_attention_mask''']						)

														if self.padding_side == "right":
																	# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																	__lowercase 					=   (
																	    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																	)
														elif self.padding_side == "left":
																	__lowercase 					=   [-1] * difference + encoded_inputs[
																	    '''global_attention_mask'''
																	]
														else:
																	raise ValueError('''Invalid padding strategy:''' + str(self.padding_side						)						)

								return encoded_inputs


