
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    lowerCamelCase      =None


lowerCamelCase      =logging.get_logger(__name__)


lowerCamelCase      ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase      ={
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

lowerCamelCase      ={
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
lowerCamelCase      =["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		   =  PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		   =  ['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE_		   =  MBartTokenizer

     SCREAMING_SNAKE_CASE_		   =  []
     SCREAMING_SNAKE_CASE_		   =  []






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="</s>"						,						__SCREAMING_SNAKE_CASE="<s>"						,						__SCREAMING_SNAKE_CASE="<unk>"						,						__SCREAMING_SNAKE_CASE="<pad>"						,						__SCREAMING_SNAKE_CASE="<mask>"						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				AddedToken(__SCREAMING_SNAKE_CASE						,						lstrip=__SCREAMING_SNAKE_CASE						,						rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) else mask_token

           super().__init__(
               vocab_file=__SCREAMING_SNAKE_CASE						,						tokenizer_file=__SCREAMING_SNAKE_CASE						,						bos_token=__SCREAMING_SNAKE_CASE						,						eos_token=__SCREAMING_SNAKE_CASE						,						sep_token=__SCREAMING_SNAKE_CASE						,						cls_token=__SCREAMING_SNAKE_CASE						,						unk_token=__SCREAMING_SNAKE_CASE						,						pad_token=__SCREAMING_SNAKE_CASE						,						mask_token=__SCREAMING_SNAKE_CASE						,						src_lang=__SCREAMING_SNAKE_CASE						,						tgt_lang=__SCREAMING_SNAKE_CASE						,						additional_special_tokens=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE						,						)

           UpperCamelCase__    :   Any					=				vocab_file
           UpperCamelCase__    :   int					=				False if not self.vocab_file else True

           UpperCamelCase__    :   Optional[int]					=				FAIRSEQ_LANGUAGE_CODES.copy()

           if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 _additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in _additional_special_tokens]  )

           self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}  )
           UpperCamelCase__    :   Union[str, Any]					=				{
               lang_code: self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           UpperCamelCase__    :   List[Any]					=				src_lang if src_lang is not None else '''en_XX'''
           UpperCamelCase__    :   List[Any]					=				self.convert_tokens_to_ids(self._src_lang  )
           UpperCamelCase__    :   Optional[Any]					=				tgt_lang
           self.set_src_lang_special_tokens(self._src_lang  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return self._src_lang






     @src_lang.setter
     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				new_src_lang
           self.set_src_lang_special_tokens(self._src_lang  )






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




           """simple docstring"""







           if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






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




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				[self.sep_token_id]
           UpperCamelCase__    :   Dict					=				[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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           if src_lang is None or tgt_lang is None:
                 raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
           UpperCamelCase__    :   Optional[Any]					=				src_lang
           UpperCamelCase__    :   Optional[Any]					=				self(__SCREAMING_SNAKE_CASE						,						add_special_tokens=__SCREAMING_SNAKE_CASE						,						return_tensors=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				tgt_lang_id
           return inputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = "en_XX"						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = "ro_RO"						,						**__SCREAMING_SNAKE_CASE						,						)  ->   BatchEncoding:




           """simple docstring"""







           UpperCamelCase__    :   int					=				src_lang
           UpperCamelCase__    :   List[str]					=				tgt_lang
           return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           return self.set_src_lang_special_tokens(self.src_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           return self.set_tgt_lang_special_tokens(self.tgt_lang  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[Any]					=				[]
           UpperCamelCase__    :   int					=				[self.eos_token_id, self.cur_lang_code]

           UpperCamelCase__    :   List[str]					=				self.convert_ids_to_tokens(self.prefix_tokens  )
           UpperCamelCase__    :   Optional[Any]					=				self.convert_ids_to_tokens(self.suffix_tokens  )

           UpperCamelCase__    :   List[str]					=				processors.TemplateProcessing(
               single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str						,						pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str						,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,						self.prefix_tokens + self.suffix_tokens  )  )						,						)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   str					=				[]
           UpperCamelCase__    :   List[str]					=				[self.eos_token_id, self.cur_lang_code]

           UpperCamelCase__    :   str					=				self.convert_ids_to_tokens(self.prefix_tokens  )
           UpperCamelCase__    :   int					=				self.convert_ids_to_tokens(self.suffix_tokens  )

           UpperCamelCase__    :   Tuple					=				processors.TemplateProcessing(
               single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str						,						pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str						,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,						self.prefix_tokens + self.suffix_tokens  )  )						,						)






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




           """simple docstring"""







           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                     '''tokenizer.'''  )

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

           if os.path.abspath(self.vocab_file  ) != os.path.abspath(__SCREAMING_SNAKE_CASE  ):
                 copyfile(self.vocab_file						,						__SCREAMING_SNAKE_CASE  )

           return (out_vocab_file,)




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      =logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_ )
class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           super().__init__(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           requires_backends(self						,						'''decord'''  )
           self.check_model_type(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				{}
           if frame_sampling_rate is not None:
                 UpperCamelCase__    :   Tuple					=				frame_sampling_rate
           if num_frames is not None:
                 UpperCamelCase__    :   str					=				num_frames

           UpperCamelCase__    :   List[str]					=				{}
           if top_k is not None:
                 UpperCamelCase__    :   List[Any]					=				top_k
           return preprocess_params, {}, postprocess_params






     def __call__(							self						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=1  )  ->   Optional[Any]:




           """simple docstring"""







           if num_frames is None:
                 UpperCamelCase__    :   Optional[Any]					=				self.model.config.num_frames

           if video.startswith('''http://'''  ) or video.startswith('''https://'''  ):
                 UpperCamelCase__    :   str					=				BytesIO(requests.get(__SCREAMING_SNAKE_CASE  ).content  )

           UpperCamelCase__    :   Tuple					=				VideoReader(__SCREAMING_SNAKE_CASE  )
           videoreader.seek(0  )

           UpperCamelCase__    :   Optional[int]					=				0
           UpperCamelCase__    :   str					=				num_frames * frame_sampling_rate - 1
           UpperCamelCase__    :   List[Any]					=				np.linspace(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						num=__SCREAMING_SNAKE_CASE						,						dtype=np.intaa  )

           UpperCamelCase__    :   str					=				videoreader.get_batch(__SCREAMING_SNAKE_CASE  ).asnumpy()
           UpperCamelCase__    :   Tuple					=				list(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				self.image_processor(__SCREAMING_SNAKE_CASE						,						return_tensors=self.framework  )
           return model_inputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.model(**__SCREAMING_SNAKE_CASE  )
           return model_outputs






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=5  )  ->   str:




           """simple docstring"""







           if top_k > self.model.config.num_labels:
                 UpperCamelCase__    :   Dict					=				self.model.config.num_labels

           if self.framework == "pt":
                 UpperCamelCase__    :   Any					=				model_outputs.logits.softmax(-1  )[0]
                 UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				probs.topk(__SCREAMING_SNAKE_CASE  )
           else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''  )

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


