





"""simple docstring"""




import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							torch.load(UpperCamelCase__       ,    map_location='cpu'				)

							A__	     =							chkpt['model']

							# We have the base model one level deeper than the original XLM repository
							A__	     =							{}
							for k, v in state_dict.items():
														if "pred_layer" in k:
																					A__	     =							v
														else:
																					A__	     =							v

							A__	     =							chkpt['params']
							A__	     =							{n: v for n, v in config.items() if not isinstance(UpperCamelCase__       ,    (torch.FloatTensor, numpy.ndarray)				)}

							A__	     =							chkpt['dico_word2id']
							A__	     =							{s + '</w>' if s.find('@@'				) == -1 and i > 13 else s.replace('@@'       ,    ''				): i for s, i in vocab.items()}

							# Save pytorch-model
							A__	     =							pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							A__	     =							pytorch_dump_folder_path + '/' + CONFIG_NAME
							A__	     =							pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

							print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
							torch.save(UpperCamelCase__       ,    UpperCamelCase__				)

							print(F'''Save configuration file to {pytorch_config_dump_path}'''				)
							with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'				) as f:
														f.write(json.dumps(UpperCamelCase__       ,    indent=2				) + '\n'				)

							print(F'''Save vocab file to {pytorch_config_dump_path}'''				)
							with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'				) as f:
														f.write(json.dumps(UpperCamelCase__       ,    indent=2				) + '\n'				)


if __name__ == "__main__":
				__lowerCamelCase											=  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__lowerCamelCase											=  parser.parse_args()
				convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""




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(__A )
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )
													requires_backends(self		,'decord'     )
													self.check_model_type(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None     )					->				Any:
													A__	     =							{}
													if frame_sampling_rate is not None:
																				A__	     =							frame_sampling_rate
													if num_frames is not None:
																				A__	     =							num_frames

													A__	     =							{}
													if top_k is not None:
																				A__	     =							top_k
													return preprocess_params, {}, postprocess_params





						def __call__(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase=1     )					->				List[Any]:
													if num_frames is None:
																				A__	     =							self.model.config.num_frames

													if video.startswith('http://'     ) or video.startswith('https://'     ):
																				A__	     =							BytesIO(requests.get(__UpperCAmelCase     ).content     )

													A__	     =							VideoReader(__UpperCAmelCase     )
													videoreader.seek(0     )

													A__	     =							0
													A__	     =							num_frames * frame_sampling_rate - 1
													A__	     =							np.linspace(__UpperCAmelCase		,__UpperCAmelCase		,num=__UpperCAmelCase		,dtype=np.intaa     )

													A__	     =							videoreader.get_batch(__UpperCAmelCase     ).asnumpy()
													A__	     =							list(__UpperCAmelCase     )

													A__	     =							self.image_processor(__UpperCAmelCase		,return_tensors=self.framework     )
													return model_inputs





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[int]:
													A__	     =							self.model(**__UpperCAmelCase     )
													return model_outputs





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=5     )					->				int:
													if top_k > self.model.config.num_labels:
																				A__	     =							self.model.config.num_labels

													if self.framework == "pt":
																				A__	     =							model_outputs.logits.softmax(-1     )[0]
																				A__     ,  A__	     =							probs.topk(__UpperCAmelCase     )
													else:
																				raise ValueError(f'''Unsupported framework: {self.framework}'''     )

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





