





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    :					Tuple =      logging.get_logger(__name__)

A    :					Dict =      {
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''cvt'''






			def __init__(			self   :    Optional[int]							,	__lowerCAmelCase   :    Optional[Any]=3							,	__lowerCAmelCase   :    Optional[Any]=[7, 3, 3]							,	__lowerCAmelCase   :    Optional[int]=[4, 2, 2]							,	__lowerCAmelCase   :    List[Any]=[2, 1, 1]							,	__lowerCAmelCase   :    Tuple=[64, 1_92, 3_84]							,	__lowerCAmelCase   :    Dict=[1, 3, 6]							,	__lowerCAmelCase   :    Dict=[1, 2, 10]							,	__lowerCAmelCase   :    Any=[4.0, 4.0, 4.0]							,	__lowerCAmelCase   :    str=[0.0, 0.0, 0.0]							,	__lowerCAmelCase   :    List[Any]=[0.0, 0.0, 0.0]							,	__lowerCAmelCase   :    Dict=[0.0, 0.0, 0.1]							,	__lowerCAmelCase   :    Optional[int]=[True, True, True]							,	__lowerCAmelCase   :    Optional[int]=[False, False, True]							,	__lowerCAmelCase   :    str=["dw_bn", "dw_bn", "dw_bn"]							,	__lowerCAmelCase   :    int=[3, 3, 3]							,	__lowerCAmelCase   :    Dict=[1, 1, 1]							,	__lowerCAmelCase   :    Union[str, Any]=[2, 2, 2]							,	__lowerCAmelCase   :    List[str]=[1, 1, 1]							,	__lowerCAmelCase   :    Tuple=[1, 1, 1]							,	__lowerCAmelCase   :    Any=0.0_2							,	__lowerCAmelCase   :    Any=1e-12							,	**__lowerCAmelCase   :    List[Any]							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase   )
					A__        =   num_channels
					A__        =   patch_sizes
					A__        =   patch_stride
					A__        =   patch_padding
					A__        =   embed_dim
					A__        =   num_heads
					A__        =   depth
					A__        =   mlp_ratio
					A__        =   attention_drop_rate
					A__        =   drop_rate
					A__        =   drop_path_rate
					A__        =   qkv_bias
					A__        =   cls_token
					A__        =   qkv_projection_method
					A__        =   kernel_qkv
					A__        =   padding_kv
					A__        =   stride_kv
					A__        =   padding_q
					A__        =   stride_q
					A__        =   initializer_range
					A__        =   layer_norm_eps










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







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






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''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.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



