





import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def    __lowerCamelCase					(							__a   :List[Any]    )		->				Tuple:






		"""simple docstring"""

		return EnvironmentCommand()







def    __lowerCamelCase					(							__a   :Optional[int]    )		->				Optional[Any]:






		"""simple docstring"""

		return EnvironmentCommand(args.accelerate_config_file    )






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




			@staticmethod
			def    a_       (			__lowerCAmelCase   :    ArgumentParser   )	->	Any:







					"""simple docstring"""






					A__        =   parser.add_parser("""env"""   )
					download_parser.set_defaults(func=__lowerCAmelCase   )
					download_parser.add_argument(
					    """--accelerate-config_file"""							,	default=__lowerCAmelCase							,	help="""The accelerate config file to use for the default values in the launching script."""							,	)
					download_parser.set_defaults(func=__lowerCAmelCase   )






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    Dict							,	*__lowerCAmelCase   :    List[Any]   )	->	None:







					"""simple docstring"""






					A__        =   accelerate_config_file






			def    a_       (			self   :    Optional[int]   )	->	List[Any]:







					"""simple docstring"""






					A__        =   """not installed"""
					if is_safetensors_available():
							import safetensors

							A__        =   safetensors.__version__
					elif importlib.util.find_spec("""safetensors"""   ) is not None:
							import safetensors

							A__        =   f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

					A__        =   """not installed"""
					A__        =   A__        =   """not found"""
					if is_accelerate_available():
							import accelerate
							from accelerate.commands.config import default_config_file, load_config_from_file

							A__        =   accelerate.__version__
							# Get the default from the config file.
							if self._accelerate_config_file is not None or os.path.isfile(__lowerCAmelCase   ):
									A__        =   load_config_from_file(self._accelerate_config_file   ).to_dict()

							A__        =   (
							    """\n""".join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]   )
							    if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   )
							    else f'\t{accelerate_config}'
							)

					A__        =   """not installed"""
					A__        =   """NA"""
					if is_torch_available():
							import torch

							A__        =   torch.__version__
							A__        =   torch.cuda.is_available()

					A__        =   """not installed"""
					A__        =   """NA"""
					if is_tf_available():
							import tensorflow as tf

							A__        =   tf.__version__
							try:
									# deprecated in v2.1
									A__        =   tf.test.is_gpu_available()
							except AttributeError:
									# returns list of devices, convert to bool
									A__        =   bool(tf.config.list_physical_devices("""GPU"""   )   )

					A__        =   """not installed"""
					A__        =   """not installed"""
					A__        =   """not installed"""
					A__        =   """NA"""
					if is_flax_available():
							import flax
							import jax
							import jaxlib

							A__        =   flax.__version__
							A__        =   jax.__version__
							A__        =   jaxlib.__version__
							A__        =   jax.lib.xla_bridge.get_backend().platform

					A__        =   {
					    """`transformers` version""": version,
					    """Platform""": platform.platform(),
					    """Python version""": platform.python_version(),
					    """Huggingface_hub version""": huggingface_hub.__version__,
					    """Safetensors version""": f'{safetensors_version}',
					    """Accelerate version""": f'{accelerate_version}',
					    """Accelerate config""": f'{accelerate_config_str}',
					    """PyTorch version (GPU?)""": f'{pt_version} ({pt_cuda_available})',
					    """Tensorflow version (GPU?)""": f'{tf_version} ({tf_cuda_available})',
					    """Flax version (CPU?/GPU?/TPU?)""": f'{flax_version} ({jax_backend})',
					    """Jax version""": f'{jax_version}',
					    """JaxLib version""": f'{jaxlib_version}',
					    """Using GPU in script?""": """<fill in>""",
					    """Using distributed or parallel set-up in script?""": """<fill in>""",
					}

					print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""   )
					print(self.format_dict(__lowerCAmelCase   )   )

					return info







			@staticmethod
			def    a_       (			__lowerCAmelCase   :    Union[str, Any]   )	->	Any:







					"""simple docstring"""






					return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]   ) + "\n"










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)



