


import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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




def     UpperCamelCase    (      _a		)					->							Optional[int]:
					'''simple docstring'''





					lowercase_       :str     =       torch.load(_lowerCAmelCase       ,		map_location='''cpu'''		)
					if "model" in sd.keys():
										lowercase_       :Optional[Any]     =       torch.load(_lowerCAmelCase       ,		map_location='''cpu'''		)["model"]

					# pop unnecessary weights
					lowercase_       :Optional[Any]     =       [
					    "decoder.version",
					    "decoder.output_projection.weight",
					]
					for key in keys_to_delete:
										if key in sd:
															sd.pop(_lowerCAmelCase		)

					lowercase_       :Union[str, Any]     =       {
					    "decoder.project_in_dim.weight": "decoder.project_in.weight",
					    "decoder.project_out_dim.weight": "decoder.project_out.weight",
					    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
					    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
					}
					for old_key, new_key in keys_to_rename.items():
										if old_key in sd:
															lowercase_       :Optional[Any]     =       sd.pop(_lowerCAmelCase		)

					lowercase_       :Any     =       list(sd.keys()		)
					for key in keys:
										if ".qkv_proj." in key:
															lowercase_       :Optional[Any]     =       sd[key]
															# We split QKV in separate Q,K,V

															lowercase_       :int     =       key.replace('''.qkv_proj.'''       ,		'''.q_proj.'''		)
															lowercase_       :List[Any]     =       key.replace('''.qkv_proj.'''       ,		'''.k_proj.'''		)
															lowercase_       :Dict     =       key.replace('''.qkv_proj.'''       ,		'''.v_proj.'''		)

															lowercase_       :Any     =       value.shape[0]
															assert depth % 3 == 0
															# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
															# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
															lowercase_       :List[str]     =       torch.split(_lowerCAmelCase       ,		depth // 3       ,		dim=0		)

															lowercase_       :str     =       q
															lowercase_       :Dict     =       k
															lowercase_       :int     =       v
															del sd[key]

					return sd




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							str:
					'''simple docstring'''





					lowercase_       :Any     =       load_checkpoint(_lowerCAmelCase		)

					if config is not None:
										lowercase_       :str     =       OPTConfig.from_pretrained(_lowerCAmelCase		)
					else:
										lowercase_       :Any     =       OPTConfig()

					lowercase_       :Optional[int]     =       OPTModel(_lowerCAmelCase		).half().eval()
					model.load_state_dict(_lowerCAmelCase		)

					# Check results
					Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
					model.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE       :		List[str]         =				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			SCREAMING_SNAKE_CASE       :		str         =				parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





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_		)]

