




import os
from typing import List, Optional, Union

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


lowerCamelCase	:	Optional[int]				=   logging.get_logger(__name__)

lowerCamelCase	:	List[str]				=   {'vocab_file': 'vocab.txt'}

lowerCamelCase	:	List[str]				=   {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

lowerCamelCase	:	Dict				=   {
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:
   with open(lowercase						,"""r"""					) as f:
      snake_case				:    List[Any]           =	f.read().splitlines()
      return [l.strip() for l in lines]




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _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    ,				A    ,				A="<unk>"    ,				A="<cls>"    ,				A="<pad>"    ,				A="<mask>"    ,				A="<eos>"    ,				**A    ,				)		->    Optional[Any]:
         super().__init__(**A     )
         snake_case				:    Dict           =	load_vocab_file(A     )
         snake_case				:    str           =	dict(enumerate(self.all_tokens     )     )
         snake_case				:    Optional[int]           =	{tok: ind for ind, tok in enumerate(self.all_tokens     )}
         snake_case				:    List[Any]           =	unk_token
         snake_case				:    List[Any]           =	cls_token
         snake_case				:    Any           =	pad_token
         snake_case				:    Any           =	mask_token
         snake_case				:    Tuple           =	eos_token
         snake_case				:    List[str]           =	self.all_tokens
         self._create_trie(self.unique_no_split_tokens     )

      def      UpperCAmelCase				(				self    ,				A     )		->    str:
         return self._id_to_token.get(A    ,				self.unk_token     )

      def      UpperCAmelCase				(				self    ,				A     )		->    int:
         return self._token_to_id.get(A    ,				self._token_to_id.get(self.unk_token     )     )

      def      UpperCAmelCase				(				self    ,				A    ,				**A     )		->    Optional[int]:
         return text.split()

      def      UpperCAmelCase				(				self    ,				A=False     )		->    Optional[int]:
         return len(self._id_to_token     )

      def      UpperCAmelCase				(				self     )		->    Tuple:
         return {token: i for i, token in enumerate(self.all_tokens     )}

      def      UpperCAmelCase				(				self    ,				A     )		->    int:
         return self._token_to_id.get(A    ,				self._token_to_id.get(self.unk_token     )     )

      def      UpperCAmelCase				(				self    ,				A     )		->    str:
         return self._id_to_token.get(A    ,				self.unk_token     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    List[int]:
         snake_case				:    List[str]           =	[self.cls_token_id]
         snake_case				:    List[str]           =	[self.eos_token_id]  # No sep token in ESM vocabulary
         if token_ids_a is None:
            if self.eos_token_id is None:
               return cls + token_ids_a
            else:
               return cls + token_ids_a + sep
         elif self.eos_token_id is None:
            raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""     )
         return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

      def      UpperCAmelCase				(				self    ,				A    ,				A = None    ,				A = False     )		->    List[int]:
         if already_has_special_tokens:
            if token_ids_a is not None:
               raise ValueError(
                   """You should not supply a second sequence if the provided sequence of """
                   """ids is already formatted with special tokens for the model."""     )

            return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
         snake_case				:    Optional[int]           =	[1] + ([0] * len(A     )) + [1]
         if token_ids_a is not None:
            mask += [0] * len(A     ) + [1]
         return mask

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    str:
         snake_case				:    List[str]           =	os.path.join(A    ,				(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""     )
         with open(A    ,				"""w"""     ) as f:
            f.write("""\n""".join(self.all_tokens     )     )
         return (vocab_file,)

      @property
      def      UpperCAmelCase				(				self     )		->    int:
         return self.get_vocab_size(with_added_tokens=A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A = False     )		->    int:
         return super()._add_tokens(A    ,				special_tokens=A     )











from io import BytesIO
from typing import List, Union

import requests

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


if is_decord_available():
       import numpy as np
       from decord import VideoReader


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

lowerCamelCase	:	int				=   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__							)
class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				*A    ,				**A     )		->    Optional[int]:
         super().__init__(*A    ,				**A     )
         requires_backends(self    ,				"""decord"""     )
         self.check_model_type(A     )

      def      UpperCAmelCase				(				self    ,				A=None    ,				A=None    ,				A=None     )		->    int:
         snake_case				:    Any           =	{}
         if frame_sampling_rate is not None:
            snake_case				:    int           =	frame_sampling_rate
         if num_frames is not None:
            snake_case				:    Union[str, Any]           =	num_frames

         snake_case				:    str           =	{}
         if top_k is not None:
            snake_case				:    Optional[int]           =	top_k
         return preprocess_params, {}, postprocess_params

      def __call__(				self    ,				A    ,				**A     )		->    Dict:
         return super().__call__(A    ,				**A     )

      def      UpperCAmelCase				(				self    ,				A    ,				A=None    ,				A=1     )		->    Tuple:
         if num_frames is None:
            snake_case				:    Tuple           =	self.model.config.num_frames

         if video.startswith("""http://"""     ) or video.startswith("""https://"""     ):
            snake_case				:    Optional[int]           =	BytesIO(requests.get(A     ).content     )

         snake_case				:    Optional[Any]           =	VideoReader(A     )
         videoreader.seek(0     )

         snake_case				:    Optional[Any]           =	0
         snake_case				:    Optional[Any]           =	num_frames * frame_sampling_rate - 1
         snake_case				:    Any           =	np.linspace(A    ,				A    ,				num=A    ,				dtype=np.intaa     )

         snake_case				:    int           =	videoreader.get_batch(A     ).asnumpy()
         snake_case				:    List[Any]           =	list(A     )

         snake_case				:    Optional[Any]           =	self.image_processor(A    ,				return_tensors=self.framework     )
         return model_inputs

      def      UpperCAmelCase				(				self    ,				A     )		->    List[str]:
         snake_case				:    Dict           =	self.model(**A     )
         return model_outputs

      def      UpperCAmelCase				(				self    ,				A    ,				A=5     )		->    int:
         if top_k > self.model.config.num_labels:
            snake_case				:    str           =	self.model.config.num_labels

         if self.framework == "pt":
            snake_case				:    List[Any]           =	model_outputs.logits.softmax(-1     )[0]
            snake_case     , snake_case				:    Tuple           =	probs.topk(A     )
         else:
            raise ValueError(f"""Unsupported framework: {self.framework}"""     )

         snake_case				:    List[Any]           =	scores.tolist()
         snake_case				:    str           =	ids.tolist()
         return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A    ,				A     )]





