
'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	_snake_case   						=		[
	    '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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	_snake_case   						=		list(s_dict.keys()	)
	for key in keys:
		if "transformer_layers" in key:
			_snake_case   						=		s_dict.pop(__A	)
		elif "subsample" in key:
			_snake_case   						=		s_dict.pop(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	_snake_case						, _snake_case   						=		emb.weight.shape
	_snake_case   						=		nn.Linear(__A			,				__A			,				bias=__A	)
	_snake_case   						=		emb.weight.data
	return lin_layer







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		torch.load(__A			,				map_location='cpu'	)
	_snake_case   						=		mam_aaa['args']
	_snake_case   						=		mam_aaa['model']
	_snake_case   						=		state_dict['decoder.output_projection.weight']

	remove_ignore_keys_(__A	)
	rename_keys(__A	)

	_snake_case   						=		state_dict['decoder.embed_tokens.weight'].shape[0]

	_snake_case   						=		args.share_decoder_input_output_embed

	_snake_case   						=		[int(__A	) for i in args.conv_kernel_sizes.split(','	)]
	_snake_case   						=		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			,				)

	_snake_case   						=		SpeechaTextForConditionalGeneration(__A	)
	_snake_case						, _snake_case   						=		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:
		_snake_case   						=		make_linear_from_emb(model.model.decoder.embed_tokens	)
	else:
		_snake_case   						=		lm_head_weights

	model.save_pretrained(__A	)


if __name__ == "__main__":
					lowercase    :							str      =					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.")
					lowercase    :							Any      =					parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase    :							Tuple      =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							int      =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					lowercase    :							Union[str, Any]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

