






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def   a			(						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   a			(						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()












import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MobileNetVaForImageClassification, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(lowerCamelCase,  '''tf_padding'''  )  )
								self.parent.assertTrue(hasattr(lowerCamelCase,  '''depth_multiplier'''  )  )



class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    List[str],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]=13,  lowerCamelCase						:    List[str]=3,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    Union[str, Any]=0.25,  lowerCamelCase						:    int=8,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    Optional[int]=1_024,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    Optional[int]="relu6",  lowerCamelCase						:    Optional[int]=0.1,  lowerCamelCase						:    Optional[Any]=0.02,  lowerCamelCase						:    List[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    Dict=10,  lowerCamelCase						:    Optional[int]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			num_channels
								lowercase__    =			image_size
								lowercase__    =			depth_multiplier
								lowercase__    =			min_depth
								lowercase__    =			tf_padding
								lowercase__    =			int(last_hidden_size * depth_multiplier  )
								lowercase__    =			output_stride
								lowercase__    =			hidden_act
								lowercase__    =			classifier_dropout_prob
								lowercase__    =			use_labels
								lowercase__    =			is_training
								lowercase__    =			num_labels
								lowercase__    =			initializer_range
								lowercase__    =			scope




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size, self.image_size, self.image_size],  self.num_labels  )

								lowercase__    =			self.get_config()

								return config, pixel_values, labels, pixel_labels




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								return MobileNetVaConfig(
								    num_channels=self.num_channels,  image_size=self.image_size,  depth_multiplier=self.depth_multiplier,  min_depth=self.min_depth,  tf_padding=self.tf_padding,  hidden_act=self.hidden_act,  classifier_dropout_prob=self.classifier_dropout_prob,  initializer_range=self.initializer_range,  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			MobileNetVaModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape,  (
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    ),  )




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    List[str],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			MobileNetVaForImageClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()
								lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__    =			config_and_inputs
								lowercase__    =			{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
				lowercase__											=       (
				    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			MobileNetVaModelTester(self  )
								lowercase__    =			MobileNetVaConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''  )
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''MobileNetV1 does not output attentions'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__    =			model_class(lowerCamelCase  )
												lowercase__    =			inspect.signature(model.forward  )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowercase__    =			[*signature.parameters.keys()]

												lowercase__    =			['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase						:    str,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    List[str]  ):
												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.eval()

												with torch.no_grad():
																lowercase__    =			model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )

												lowercase__    =			outputs.hidden_states

												lowercase__    =			26
												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__    =			True
												check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowercase__    =			True

												check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )




				@slow
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			MobileNetVaModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )






def   a			(						):






				'''simple docstring'''
				lowercase__    =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image




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

				"""simple docstring"""




				@cached_property
				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								return (
								    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''  ) if is_vision_available() else None
								)




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''  ).to(lowerCamelCase  )

								lowercase__    =			self.default_image_processor
								lowercase__    =			prepare_img()
								lowercase__    =			image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__    =			model(**lowerCamelCase  )

								# verify the logits
								lowercase__    =			torch.Size((1, 1_001)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )

								lowercase__    =			torch.tensor([-4.1739, -1.1233, 3.1205]  ).to(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4  )  )




