


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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_ta import TaTokenizer
else:
			SCREAMING_SNAKE_CASE       :		Optional[Any]         =				None


SCREAMING_SNAKE_CASE       :		str         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		Any         =				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE       :		Any         =				{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}






class      UpperCamelCase					(		lowercase__     ):


			'''simple docstring'''







			lowercase					:	int									=VOCAB_FILES_NAMES
			lowercase					:	List[Any]									=PRETRAINED_VOCAB_FILES_MAP
			lowercase					:	str									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase					:	Optional[Any]									=["""input_ids""", """attention_mask"""]
			lowercase					:	Dict									=TaTokenizer

			lowercase					:	List[int]									=[]
			def __init__(     self   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_="</s>"   ,							UpperCamelCase_="<unk>"   ,							UpperCamelCase_="<pad>"   ,							UpperCamelCase_=100   ,							UpperCamelCase_=None   ,							**UpperCamelCase_   ,							):
								# Add extra_ids to the special token list
								if extra_ids > 0 and additional_special_tokens is None:
													lowercase_       :Optional[Any]     =       [f"<extra_id_{i}>" for i in range(UpperCamelCase_		)]
								elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra special tokens
													lowercase_       :int     =       len(set(filter(lambda UpperCamelCase_		: bool('''extra_id_''' in str(UpperCamelCase_		)		)   ,							UpperCamelCase_		)		)		)
													if extra_tokens != extra_ids:
																		raise ValueError(
																		    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																		    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																		    ''' tokens'''		)

								super().__init__(
								    UpperCamelCase_   ,							tokenizer_file=UpperCamelCase_   ,							eos_token=UpperCamelCase_   ,							unk_token=UpperCamelCase_   ,							pad_token=UpperCamelCase_   ,							extra_ids=UpperCamelCase_   ,							additional_special_tokens=UpperCamelCase_   ,							**UpperCamelCase_   ,							)

								lowercase_       :Tuple     =       vocab_file
								lowercase_       :Union[str, Any]     =       False if not self.vocab_file else True
								lowercase_       :List[str]     =       extra_ids
			@staticmethod
			def 		UpperCamelCase  (     UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
								if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
													lowercase_       :Optional[int]     =       TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
													if init_max_model_length is not None and init_max_model_length != max_model_length:
																		return init_max_model_length
													elif init_max_model_length is None:
																		warnings.warn(
																		    '''This tokenizer was incorrectly instantiated with a model max length of'''
																		    f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																		    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																		    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																		    f" {pretrained_model_name_or_path} automatically truncating your input to"
																		    f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																		    f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																		    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																		    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''   ,							UpperCamelCase_   ,							)

								return max_model_length
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
								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(UpperCamelCase_		):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
													return
								lowercase_       :Dict     =       os.path.join(
								    UpperCamelCase_   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase_		):
													copyfile(self.vocab_file   ,							UpperCamelCase_		)
													logger.info(f"Copy vocab file to {out_vocab_file}"		)

								return (out_vocab_file,)
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
								lowercase_       :Any     =       token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
													return self.prefix_tokens + token_ids_a
								else:
													lowercase_       :int     =       token_ids_a + [self.eos_token_id]
													return self.prefix_tokens + token_ids_a + token_ids_a
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
								lowercase_       :List[Any]     =       [self.eos_token_id]

								if token_ids_a is None:
													return len(token_ids_a + eos		) * [0]
								return len(token_ids_a + eos + token_ids_a + eos		) * [0]
			def 		UpperCamelCase  (     self		):
								return list(
								    set(filter(lambda UpperCamelCase_		: bool(re.search(R'''<extra_id_\d+>'''   ,							UpperCamelCase_		)		) is not None   ,							self.additional_special_tokens		)		)		)




			def 		UpperCamelCase  (     self		):
								return [self.convert_tokens_to_ids(UpperCamelCase_		) for token in self.get_sentinel_tokens()]





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

SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)






@add_end_docstrings(lowercase__     )
class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        super().__init__(*UpperCamelCase_   ,							**UpperCamelCase_		)
        requires_backends(self   ,							'''decord'''		)
        self.check_model_type(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None		):
        lowercase_       :int     =       {}
        if frame_sampling_rate is not None:
             lowercase_       :int     =       frame_sampling_rate
        if num_frames is not None:
             lowercase_       :int     =       num_frames

        lowercase_       :str     =       {}
        if top_k is not None:
             lowercase_       :Optional[int]     =       top_k
        return preprocess_params, {}, postprocess_params
   def __call__(     self   ,							UpperCamelCase_   ,							**UpperCamelCase_		):
        return super().__call__(UpperCamelCase_   ,							**UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=1		):
        if num_frames is None:
             lowercase_       :str     =       self.model.config.num_frames

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

        lowercase_       :Optional[int]     =       VideoReader(UpperCamelCase_		)
        videoreader.seek(0		)

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

        lowercase_       :Dict     =       videoreader.get_batch(UpperCamelCase_		).asnumpy()
        lowercase_       :List[Any]     =       list(UpperCamelCase_		)

        lowercase_       :Any     =       self.image_processor(UpperCamelCase_   ,							return_tensors=self.framework		)
        return model_inputs
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :List[str]     =       self.model(**UpperCamelCase_		)
        return model_outputs




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=5		):
        if top_k > self.model.config.num_labels:
             lowercase_       :List[str]     =       self.model.config.num_labels

        if self.framework == "pt":
             lowercase_       :Optional[int]     =       model_outputs.logits.softmax(-1		)[0]
             lowercase_    ,      lowercase_       :Dict     =       probs.topk(UpperCamelCase_		)
        else:
             raise ValueError(f"Unsupported framework: {self.framework}"		)

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

