






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
	from ..tf_utils import stable_softmax

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__lowerCamelCase :      Union[str, Any]										=				logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_						)
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def __init__( self					:  str							,		*__A					:  Any							,		**__A					:  Tuple	):
						super().__init__(*__A							,		**__A	)
						requires_backends(self							,		"vision"	)
						self.check_model_type(
						    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == "tf"
						    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING	)



					def 	_lowercase					( self					:  str							,		__A					:  Dict=None	):
						snake_case__			:				Optional[int]       =  {}
						if top_k is not None:
							snake_case__			:				Dict       =  top_k
						return {}, {}, postprocess_params



					def __call__( self					:  str							,		__A					:  Union[str, List[str], "Image.Image", List["Image.Image"]]							,		**__A					:  List[str]	):
						return super().__call__(__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Tuple	):
						snake_case__			:				List[str]       =  load_image(__A	)
						snake_case__			:				Optional[int]       =  self.image_processor(images=__A							,		return_tensors=self.framework	)
						return model_inputs



					def 	_lowercase					( self					:  Dict							,		__A					:  Dict	):
						snake_case__			:				List[Any]       =  self.model(**__A	)
						return model_outputs






					def 	_lowercase					( self					:  Dict							,		__A					:  Union[str, Any]							,		__A					:  Union[str, Any]=5	):
						if top_k > self.model.config.num_labels:
							snake_case__			:				str       =  self.model.config.num_labels

						if self.framework == "pt":
							snake_case__			:				Any       =  model_outputs.logits.softmax(-1	)[0]
							snake_case__			:				Tuple       =  probs.topk(__A	)
						elif self.framework == "tf":
							snake_case__			:				List[str]       =  stable_softmax(model_outputs.logits							,		axis=-1	)[0]
							snake_case__			:				Dict       =  tf.math.top_k(__A							,		k=__A	)
							snake_case__			:				Optional[Any]       =  topk.values.numpy(), topk.indices.numpy()
						else:
							raise ValueError(f'''Unsupported framework: {self.framework}'''	)

						snake_case__			:				Optional[Any]       =  scores.tolist()
						snake_case__			:				List[str]       =  ids.tolist()
						return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__A							,		__A	)]









import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
	)
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--hf_config""",
	    default="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      str										=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)



