


'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 			a      (			__a   )  ->	Optional[int]:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= [
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__a ,					__a   )


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :Optional[Any]												= list(s_dict.keys()   )
					for key in keys:
										if "transformer_layers" in key:
															UpperCamelCase__   :str												= s_dict.pop(__a   )
										elif "subsample" in key:
															UpperCamelCase__   :Dict												= s_dict.pop(__a   )


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= emb.weight.shape
					UpperCamelCase__   :List[Any]												= nn.Linear(__a ,					__a ,					bias=__a   )
					UpperCamelCase__   :List[Any]												= emb.weight.data
					return lin_layer


def 			a      (			__a ,					__a   )  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= torch.load(__a ,					map_location='''cpu'''   )
					UpperCamelCase__   :Tuple												= mam_aaa['''args''']
					UpperCamelCase__   :Optional[Any]												= mam_aaa['''model''']
					UpperCamelCase__   :Any												= state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__a   )
					rename_keys(__a   )

					UpperCamelCase__   :List[Any]												= state_dict['''decoder.embed_tokens.weight'''].shape[0]

					UpperCamelCase__   :Any												= args.share_decoder_input_output_embed

					UpperCamelCase__   :int												= [int(__a   ) for i in args.conv_kernel_sizes.split(''','''   )]
					UpperCamelCase__   :Union[str, Any]												= SpeechaTextConfig(
					    vocab_size=__a ,					max_source_positions=args.max_source_positions ,					max_target_positions=args.max_target_positions ,					encoder_layers=args.encoder_layers ,					decoder_layers=args.decoder_layers ,					encoder_attention_heads=args.encoder_attention_heads ,					decoder_attention_heads=args.decoder_attention_heads ,					encoder_ffn_dim=args.encoder_ffn_embed_dim ,					decoder_ffn_dim=args.decoder_ffn_embed_dim ,					d_model=args.encoder_embed_dim ,					dropout=args.dropout ,					attention_dropout=args.attention_dropout ,					activation_dropout=args.activation_dropout ,					activation_function='''relu''' ,					num_conv_layers=len(__a   ) ,					conv_channels=args.conv_channels ,					conv_kernel_sizes=__a ,					input_feat_per_channel=args.input_feat_per_channel ,					input_channels=args.input_channels ,					tie_word_embeddings=__a ,					num_beams=5 ,					max_length=200 ,					use_cache=__a ,					decoder_start_token_id=2 ,					early_stopping=__a ,					)

					UpperCamelCase__   :Tuple												= SpeechaTextForConditionalGeneration(__a   )
					UpperCamelCase__					,				UpperCamelCase__   :Dict												= model.model.load_state_dict(__a ,					strict=__a   )
					if len(__a   ) > 0 and not set(__a   ) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
										    f''' but all the following weights are missing {missing}'''   )

					if tie_embeds:
										UpperCamelCase__   :Union[str, Any]												= make_linear_from_emb(model.model.decoder.embed_tokens   )
					else:
										UpperCamelCase__   :List[Any]												= lm_head_weights

					model.save_pretrained(__a   )


if __name__ == "__main__":
		__snake_case							      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		__snake_case							      =     parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'camembert'




		def __init__(		self    ,   UpperCamelCase_=30522    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-12    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_="absolute"    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_    ,   bos_token_id=UpperCamelCase_    ,   eos_token_id=UpperCamelCase_    ,   **UpperCamelCase_							)

							UpperCamelCase__   :int												= vocab_size
							UpperCamelCase__   :Optional[int]												= hidden_size
							UpperCamelCase__   :Optional[int]												= num_hidden_layers
							UpperCamelCase__   :List[Any]												= num_attention_heads
							UpperCamelCase__   :Union[str, Any]												= hidden_act
							UpperCamelCase__   :List[Any]												= intermediate_size
							UpperCamelCase__   :int												= hidden_dropout_prob
							UpperCamelCase__   :Tuple												= attention_probs_dropout_prob
							UpperCamelCase__   :Union[str, Any]												= max_position_embeddings
							UpperCamelCase__   :Tuple												= type_vocab_size
							UpperCamelCase__   :int												= initializer_range
							UpperCamelCase__   :List[str]												= layer_norm_eps
							UpperCamelCase__   :int												= position_embedding_type
							UpperCamelCase__   :Any												= use_cache
							UpperCamelCase__   :Any												= classifier_dropout



class    lowercase	(							A__					):

		"""simple docstring"""

		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.task == "multiple-choice":
												UpperCamelCase__   :List[str]												= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												UpperCamelCase__   :Tuple												= {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]							)