


'''simple docstring'''



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def 				lowerCamelCase__				(	A       :      BertModel				,       A       :      str				,       A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

						UpperCAmelCase					    =     (
						    ('''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(A					):
												os.makedirs(A					)

						UpperCAmelCase					    =     model.state_dict()

						def to_tf_var_name(A       :      str					):
												for patt, repl in iter(A					):
																		UpperCAmelCase					    =     name.replace(A				,       A					)
												return f"""bert/{name}"""

						def create_tf_var(A       :      np.ndarray				,       A       :      str				,       A       :      tf.Session					):
												UpperCAmelCase					    =     tf.dtypes.as_dtype(tensor.dtype					)
												UpperCAmelCase					    =     tf.get_variable(dtype=A				,       shape=tensor.shape				,       name=A				,       initializer=tf.zeros_initializer()					)
												session.run(tf.variables_initializer([tf_var]					)					)
												session.run(A					)
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		UpperCAmelCase					    =     to_tf_var_name(A					)
																		UpperCAmelCase					    =     state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose					):
																								UpperCAmelCase					    =     torch_tensor.T
																		UpperCAmelCase					    =     create_tf_var(tensor=A				,       name=A				,       session=A					)
																		tf.keras.backend.set_value(A				,       A					)
																		UpperCAmelCase					    =     session.run(A					)
																		print(f"""Successfully created {tf_name}: {np.allclose(A				,       A					)}"""					)

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


def 				lowerCamelCase__				(	A       :      List[str]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     argparse.ArgumentParser()
						parser.add_argument('''--model_name'''				,       type=A				,       required=A				,       help='''model name e.g. bert-base-uncased'''					)
						parser.add_argument(
						    '''--cache_dir'''				,       type=A				,       default=A				,       required=A				,       help='''Directory containing pytorch model'''					)
						parser.add_argument('''--pytorch_model_path'''				,       type=A				,       required=A				,       help='''/path/to/<pytorch-model-name>.bin'''					)
						parser.add_argument('''--tf_cache_dir'''				,       type=A				,       required=A				,       help='''Directory in which to save tensorflow model'''					)
						UpperCAmelCase					    =     parser.parse_args(A					)

						UpperCAmelCase					    =     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=A				,       ckpt_dir=args.tf_cache_dir				,       model_name=args.model_name					)


if __name__ == "__main__":
	main()









'''simple docstring'''



import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image


def 				lowerCamelCase__				(	A       :      str=""					):




						'''simple docstring'''






						UpperCAmelCase					    =     tempfile.mkdtemp()
						return os.path.join(A				,       str(uuid.uuida()					) + suffix					)






@require_soundfile
@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			int				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.rand(12				,   dtype=torch.floataa				) - 0.5
										UpperCAmelCase					    =     AgentAudio(lowerCAmelCase				)
										UpperCAmelCase					    =     str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type.to_raw()				,   atol=1E-4				)				)

										del agent_type

										# Ensure the path remains even after the object deletion
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)

										# Ensure that the file contains the same value as the original tensor
										UpperCAmelCase    ,    UpperCAmelCase					    =     sf.read(lowerCAmelCase				)
										self.assertTrue(torch.allclose(lowerCAmelCase				,   torch.tensor(lowerCAmelCase				)				,   atol=1E-4				)				)





				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.rand(12				,   dtype=torch.floataa				) - 0.5
										UpperCAmelCase					    =     get_new_path(suffix='''.wav'''				)
										sf.write(lowerCAmelCase				,   lowerCAmelCase				,   16000				)

										UpperCAmelCase					    =     AgentAudio(lowerCAmelCase				)

										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type.to_raw()				,   atol=1E-4				)				)
										self.assertEqual(agent_type.to_string()				,   lowerCAmelCase				)







@require_vision
@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			List[Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.randint(0				,   256				,   (64, 64, 3)				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)
										UpperCAmelCase					    =     str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type._tensor				,   atol=1E-4				)				)

										self.assertIsInstance(agent_type.to_raw()				,   Image.Image				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)




				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
										UpperCAmelCase					    =     Image.open(lowerCAmelCase				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)

										self.assertTrue(path.samefile(agent_type.to_string()				)				)
										self.assertTrue(image == agent_type.to_raw()				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)





				def 					a__(		self      :			Optional[Any]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
										UpperCAmelCase					    =     Image.open(lowerCAmelCase				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)

										self.assertFalse(path.samefile(agent_type.to_string()				)				)
										self.assertTrue(image == agent_type.to_raw()				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)







class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			int				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     '''Hey!'''
										UpperCAmelCase					    =     AgentText(lowerCAmelCase				)

										self.assertEqual(lowerCAmelCase				,   agent_type.to_string()				)
										self.assertEqual(lowerCAmelCase				,   agent_type.to_raw()				)
										self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)





