
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 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return EnvironmentCommand()


def 						UpperCAmelCase			(	a_						)		->	Union[str, Any]:







				"""simple docstring"""



				return EnvironmentCommand(args.accelerate_config_file						)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	@staticmethod
	def 				UpperCamelCase_  (       A					: ArgumentParser ):
					__A   			= parser.add_parser("env" )
					download_parser.set_defaults(func=A )
					download_parser.add_argument(
					    "--accelerate-config_file"							,default=A							,help="The accelerate config file to use for the default values in the launching script."							,)
					download_parser.set_defaults(func=A )






	def __init__(       self					: str							,A					: str							,*A					: List[Any] ):
					__A   			= accelerate_config_file






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__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(A ):
													__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(A							,A )
									    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(A ) )

					return info







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


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''layer.''', '''layer_'''),
					    ('''word_embeddings.weight''', '''word_embeddings'''),
					    ('''position_embeddings.weight''', '''position_embeddings'''),
					    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
					    ('''.''', '''/'''),
					    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
					    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
					    ('''weight''', '''kernel'''),
					)

					if not os.path.isdir(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

					tf.reset_default_graph()
					with tf.Session() as session:
										for var_name in state_dict:
															lowercase							=						to_tf_var_name(lowerCAmelCase__		)
															lowercase							=						state_dict[var_name].numpy()
															if any(x in var_name for x in tensors_to_transpose		):
																				lowercase							=						torch_tensor.T
															lowercase							=						create_tf_var(tensor=lowerCAmelCase__ ,			name=lowerCAmelCase__ ,			session=lowerCAmelCase__		)
															tf.keras.backend.set_value(lowerCAmelCase__ ,			lowerCAmelCase__		)
															lowercase							=						session.run(lowerCAmelCase__		)
															print(f'Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)}'		)

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						BertModel.from_pretrained(
					    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path		) ,			cache_dir=args.cache_dir ,			)

					convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




