


"""simple docstring"""




from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image


__UpperCamelCase						=      ['''text''', '''image''', '''audio''']


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					[]

						for input_type in input_types:
												if input_type == "text":
																		inputs.append('Text input'  )
												elif input_type == "image":
																		inputs.append(
																		    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'  )  ) / '000000039769.png'  ).resize((5_12, 5_12)  )  )
												elif input_type == "audio":
																		inputs.append(torch.ones(30_00  )  )
												elif isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		inputs.append(create_inputs(SCREAMING_SNAKE_CASE_  )  )
												else:
																		raise ValueError(F'Invalid type requested: {input_type}'  )

						return inputs


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List  )   ->						int:
						SCREAMING_SNAKE_CASE													=					[]

						for output in outputs:
												if isinstance(SCREAMING_SNAKE_CASE_ ,			(str, AgentText)  ):
																		output_types.append('text'  )
												elif isinstance(SCREAMING_SNAKE_CASE_ ,			(Image.Image, AgentImage)  ):
																		output_types.append('image'  )
												elif isinstance(SCREAMING_SNAKE_CASE_ ,			(torch.Tensor, AgentAudio)  ):
																		output_types.append('audio'  )
												else:
																		raise ValueError(F'Invalid output: {output}'  )

						return output_types


@is_tool_test
class 					lowerCAmelCase	:




		'''simple docstring'''







		def 							__A						(	self				)     ->							List[Any]:
								self.assertTrue(hasattr(self.tool						,       'inputs'				)				)
								self.assertTrue(hasattr(self.tool						,       'outputs'				)				)

								SCREAMING_SNAKE_CASE													=					self.tool.inputs
								for _input in inputs:
														if isinstance(_input						,       lowerCAmelCase__				):
																				for __input in _input:
																										self.assertTrue(__input in authorized_types				)
														else:
																				self.assertTrue(_input in authorized_types				)

								SCREAMING_SNAKE_CASE													=					self.tool.outputs
								for _output in outputs:
														self.assertTrue(_output in authorized_types				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					create_inputs(self.tool.inputs				)
								SCREAMING_SNAKE_CASE													=					self.tool(*lowerCAmelCase__				)

								# There is a single output
								if len(self.tool.outputs				) == 1:
														SCREAMING_SNAKE_CASE													=					[outputs]

								self.assertListEqual(output_types(lowerCAmelCase__				)						,       self.tool.outputs				)







		def 							__A						(	self				)     ->							List[Any]:
								self.assertTrue(hasattr(self.tool						,       'description'				)				)
								self.assertTrue(hasattr(self.tool						,       'default_checkpoint'				)				)
								self.assertTrue(self.tool.description.startswith('This is a tool that'				)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					create_inputs(self.tool.inputs				)
								SCREAMING_SNAKE_CASE													=					self.tool(*lowerCAmelCase__				)

								if not isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[outputs]

								self.assertEqual(len(lowerCAmelCase__				)						,       len(self.tool.outputs				)				)

								for output, output_type in zip(lowerCAmelCase__						,       self.tool.outputs				):
														SCREAMING_SNAKE_CASE													=					AGENT_TYPE_MAPPING[output_type]
														self.assertTrue(isinstance(lowerCAmelCase__						,       lowerCAmelCase__				)				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					create_inputs(self.tool.inputs				)

								SCREAMING_SNAKE_CASE													=					[]

								for _input, input_type in zip(lowerCAmelCase__						,       self.tool.inputs				):
														if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
																				_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
														else:
																				_inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
								SCREAMING_SNAKE_CASE													=					self.tool(*lowerCAmelCase__				)

								if not isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[outputs]

								self.assertEqual(len(lowerCAmelCase__				)						,       len(self.tool.outputs				)				)






"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('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(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

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

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

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


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

						SCREAMING_SNAKE_CASE													=					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=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


