

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def      A_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any    						=			[
						    "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_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			list(s_dict.keys()       )
						for key in keys:
												if "transformer_layers" in key:
																		SCREAMING_SNAKE_CASE_:					Optional[int]    						=			s_dict.pop(_A       )
												elif "subsample" in key:
																		SCREAMING_SNAKE_CASE_:					Optional[int]    						=			s_dict.pop(_A       )







def      A_ (       _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_:					str    						=			emb.weight.shape
						SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			nn.Linear(_A							,      _A							,      bias=_A       )
						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			emb.weight.data
						return lin_layer







def      A_ (       _UpperCAmelCase							,      _UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Dict    						=			torch.load(_A							,      map_location="cpu"       )
						SCREAMING_SNAKE_CASE_:					List[str]    						=			mam_aaa["args"]
						SCREAMING_SNAKE_CASE_:					Dict    						=			mam_aaa["model"]
						SCREAMING_SNAKE_CASE_:					Dict    						=			state_dict["decoder.output_projection.weight"]

						remove_ignore_keys_(_A       )
						rename_keys(_A       )

						SCREAMING_SNAKE_CASE_:					List[str]    						=			state_dict["decoder.embed_tokens.weight"].shape[0]

						SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			args.share_decoder_input_output_embed

						SCREAMING_SNAKE_CASE_:					Union[str, Any]    						=			[int(_A       ) for i in args.conv_kernel_sizes.split(","       )]
						SCREAMING_SNAKE_CASE_:					Tuple    						=			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=2_00							,      use_cache=_A							,      decoder_start_token_id=2							,      early_stopping=_A							,      )

						SCREAMING_SNAKE_CASE_:					Any    						=			SpeechaTextForConditionalGeneration(_A       )
						SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			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:
												SCREAMING_SNAKE_CASE_:					Optional[Any]    						=			make_linear_from_emb(model.model.decoder.embed_tokens       )
						else:
												SCREAMING_SNAKE_CASE_:					Dict    						=			lm_head_weights

						model.save_pretrained(_A       )


if __name__ == "__main__":
						lowerCAmelCase				:    Optional[Any]        =		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.""")
						lowerCAmelCase				:    Any        =		parser.parse_args()
						convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)












from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
