


from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser






@dataclass
class      UpperCamelCase					:


   '''simple docstring'''







   lowercase					:	str									=field(
       metadata={"""help""": """The output directory where the model will be written."""}						,      )
   lowercase					:	str									=field(
       metadata={
           """help""": (
               """The encoder model checkpoint for weights initialization."""
               """Don't set if you want to train an encoder model from scratch."""
           )
       }						,      )
   lowercase					:	str									=field(
       metadata={
           """help""": (
               """The decoder model checkpoint for weights initialization."""
               """Don't set if you want to train a decoder model from scratch."""
           )
       }						,      )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}     )
   lowercase					:	Optional[str]									=field(
       default=lowercase__						,      metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}     )




def     UpperCamelCase    (      )					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Tuple     =       HfArgumentParser((ModelArguments,)		)
     ((lowercase_)    ,      )       :Optional[int]     =       parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

     # Use explicit specified encoder config
     if model_args.encoder_config_name:
          lowercase_       :Optional[int]     =       AutoConfig.from_pretrained(model_args.encoder_config_name		)
     # Use pretrained encoder model's config
     else:
          lowercase_       :List[Any]     =       AutoConfig.from_pretrained(model_args.encoder_model_name_or_path		)

     # Use explicit specified decoder config
     if model_args.decoder_config_name:
          lowercase_       :List[Any]     =       AutoConfig.from_pretrained(model_args.decoder_config_name		)
     # Use pretrained decoder model's config
     else:
          lowercase_       :List[str]     =       AutoConfig.from_pretrained(model_args.decoder_model_name_or_path		)

     # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
     lowercase_       :Optional[Any]     =       True
     lowercase_       :List[Any]     =       True

     lowercase_       :List[Any]     =       FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
         encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path       ,		decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path       ,		encoder_config=_a       ,		decoder_config=_a       ,		)

     # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
     lowercase_       :int     =       decoder_config.decoder_start_token_id
     lowercase_       :List[Any]     =       decoder_config.pad_token_id
     if decoder_start_token_id is None:
          lowercase_       :Optional[int]     =       decoder_config.bos_token_id
     if pad_token_id is None:
          lowercase_       :Dict     =       decoder_config.eos_token_id

     # This is necessary to make Flax's generate() work
     lowercase_       :Any     =       decoder_config.eos_token_id
     lowercase_       :Optional[Any]     =       decoder_start_token_id
     lowercase_       :Union[str, Any]     =       pad_token_id

     lowercase_       :int     =       AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path		)

     lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path		)
     lowercase_       :Optional[int]     =       tokenizer.convert_ids_to_tokens(model.config.pad_token_id		)

     model.save_pretrained(model_args.output_dir		)
     image_processor.save_pretrained(model_args.output_dir		)
     tokenizer.save_pretrained(model_args.output_dir		)


if __name__ == "__main__":
   main()





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_		)]

