



import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					def __init__(		self     :List[str]      ,  lowercase__     :Union[str, Any]      ,  lowercase__     :Tuple=3      ,  lowercase__     :Union[str, Any]=32      ,  lowercase__     :Dict=3      ,  lowercase__     :Union[str, Any]=10      ,  lowercase__     :Any=[10, 20, 30, 40]      ,  lowercase__     :Tuple=[1, 1, 2, 1]      ,  lowercase__     :str=True      ,  lowercase__     :int=True      ,  lowercase__     :List[str]="relu"      ,  lowercase__     :Optional[Any]=3      ,  lowercase__     :Optional[Any]=None      ,  ):
										lowercase						   =       parent
										lowercase						   =       batch_size
										lowercase						   =       image_size
										lowercase						   =       num_channels
										lowercase						   =       embeddings_size
										lowercase						   =       hidden_sizes
										lowercase						   =       depths
										lowercase						   =       is_training
										lowercase						   =       use_labels
										lowercase						   =       hidden_act
										lowercase						   =       num_labels
										lowercase						   =       scope
										lowercase						   =       len(lowercase__    )

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										lowercase						   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										lowercase						   =       self.get_config()

										return config, pixel_values

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return ResNetConfig(
										    num_channels=self.num_channels      ,  embeddings_size=self.embeddings_size      ,  hidden_sizes=self.hidden_sizes      ,  depths=self.depths      ,  hidden_act=self.hidden_act      ,  num_labels=self.num_labels      ,  image_size=self.image_size      ,  )

					def 				__UpperCAmelCase		(		self     :Any      ,  lowercase__     :Optional[int]      ,  lowercase__     :Optional[Any]    ):
										lowercase						   =       FlaxResNetModel(config=lowercase__    )
										lowercase						   =       model(lowercase__    )

										# Output shape (b, c, h, w)
										self.parent.assertEqual(
										    result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,  )

					def 				__UpperCAmelCase		(		self     :List[Any]      ,  lowercase__     :Optional[Any]      ,  lowercase__     :Union[str, Any]    ):
										lowercase						   =       self.num_labels
										lowercase						   =       FlaxResNetForImageClassification(config=lowercase__    )
										lowercase						   =       model(lowercase__    )
										self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)    )



					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										lowercase						   =       self.prepare_config_and_inputs()
										lowercase   ,      lowercase						   =       config_and_inputs
										lowercase						   =       {'pixel_values': pixel_values}
										return config, inputs_dict




@require_flax
class 	a__       (       _snake_case  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				Tuple					 =				(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

					A__  :				Tuple					 =				False
					A__  :				Union[str, Any]					 =				False
					A__  :				int					 =				False

					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       FlaxResNetModelTester(self    )
										lowercase						   =       ConfigTester(self      ,  config_class=lowercase__      ,  has_text_modality=lowercase__    )

					def 				__UpperCAmelCase		(		self     :int    ):
										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()

					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										return

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__    )

					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										lowercase						   =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase__    )

					@unittest.skip(reason='ResNet does not use inputs_embeds'    )
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										pass

					@unittest.skip(reason='ResNet does not support input and output embeddings'    )
					def 				__UpperCAmelCase		(		self     :str    ):
										pass

					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase   ,      lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

					def 				__UpperCAmelCase		(		self     :str    ):
										def check_hidden_states_output(lowercase__     :Union[str, Any]      ,  lowercase__     :Tuple      ,  lowercase__     :Optional[Any]    ):
															lowercase						   =       model_class(lowercase__    )

															lowercase						   =       model(**self._prepare_for_class(lowercase__      ,  lowercase__    )    )

															lowercase						   =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															lowercase						   =       self.model_tester.num_stages
															self.assertEqual(len(lowercase__    )      ,  expected_num_stages + 1    )

					@unittest.skip(reason='ResNet does not use feedforward chunking'    )
					def 				__UpperCAmelCase		(		self     :List[str]    ):
										pass



					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase   ,      lowercase						   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__    ):
																				lowercase						   =       self._prepare_for_class(lowercase__      ,  lowercase__    )
																				lowercase						   =       model_class(lowercase__    )

																				@jax.jit
																				def model_jitted(lowercase__     :int      ,  **lowercase__     :List[Any]    ):
																									return model(pixel_values=lowercase__      ,  **lowercase__    )

																				with self.subTest('JIT Enabled'    ):
																									lowercase						   =       model_jitted(**lowercase__    ).to_tuple()

																				with self.subTest('JIT Disabled'    ):
																									with jax.disable_jit():
																														lowercase						   =       model_jitted(**lowercase__    ).to_tuple()

																				self.assertEqual(len(lowercase__    )      ,  len(lowercase__    )    )
																				for jitted_output, output in zip(lowercase__      ,  lowercase__    ):
																									self.assertEqual(jitted_output.shape      ,  output.shape    )





def 		__snake_case						(    ):
					"""simple docstring"""






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




@require_flax
class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					@cached_property
					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										return AutoImageProcessor.from_pretrained('microsoft/resnet-50'    ) if is_vision_available() else None



					@slow
					def 				__UpperCAmelCase		(		self     :str    ):
										lowercase						   =       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'    )

										lowercase						   =       self.default_image_processor
										lowercase						   =       prepare_img()
										lowercase						   =       image_processor(images=lowercase__      ,  return_tensors='np'    )

										lowercase						   =       model(**lowercase__    )

										# verify the logits
										lowercase						   =       (1, 1000)
										self.assertEqual(outputs.logits.shape      ,  lowercase__    )

										lowercase						   =       jnp.array([-11.1069, -9.7877, -8.3777]    )

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











import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






