





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				(					__snake_case  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							int	=						self.config_class(**self.inputs_dict      )
							self.parent.assertTrue(hasattr(A_						,	"""tf_padding"""      )      )
							self.parent.assertTrue(hasattr(A_						,	"""depth_multiplier"""      )      )




class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       int						,	snake_case_ :       int						,	snake_case_ :       Optional[Any]=13						,	snake_case_ :       Optional[int]=3						,	snake_case_ :       int=32						,	snake_case_ :       Optional[Any]=0.25						,	snake_case_ :       Dict=8						,	snake_case_ :       Dict=True						,	snake_case_ :       int=1024						,	snake_case_ :       Dict=32						,	snake_case_ :       str="relu6"						,	snake_case_ :       Any=0.1						,	snake_case_ :       Optional[int]=0.02						,	snake_case_ :       Union[str, Any]=True						,	snake_case_ :       Dict=True						,	snake_case_ :       int=10						,	snake_case_ :       Any=None						,	):
							UpperCamelCase_:							Any	=						parent
							UpperCamelCase_:							Optional[int]	=						batch_size
							UpperCamelCase_:							Optional[int]	=						num_channels
							UpperCamelCase_:							Tuple	=						image_size
							UpperCamelCase_:							Any	=						depth_multiplier
							UpperCamelCase_:							Any	=						min_depth
							UpperCamelCase_:							Dict	=						tf_padding
							UpperCamelCase_:							int	=						int(last_hidden_size * depth_multiplier      )
							UpperCamelCase_:							Optional[int]	=						output_stride
							UpperCamelCase_:							Any	=						hidden_act
							UpperCamelCase_:							Any	=						classifier_dropout_prob
							UpperCamelCase_:							Optional[int]	=						use_labels
							UpperCamelCase_:							List[str]	=						is_training
							UpperCamelCase_:							List[Any]	=						num_labels
							UpperCamelCase_:							Optional[Any]	=						initializer_range
							UpperCamelCase_:							Union[str, Any]	=						scope

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Optional[int]	=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

							UpperCamelCase_:							int	=						None
							UpperCamelCase_:							Dict	=						None
							if self.use_labels:
											UpperCamelCase_:							Any	=						ids_tensor([self.batch_size]						,	self.num_labels      )
											UpperCamelCase_:							Union[str, Any]	=						ids_tensor([self.batch_size, self.image_size, self.image_size]						,	self.num_labels      )

							UpperCamelCase_:							Union[str, Any]	=						self.get_config()

							return config, pixel_values, labels, pixel_labels

			def       lowerCAmelCase__	(       self :       int      ):
							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       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Tuple						,	snake_case_ :       Dict						,	snake_case_ :       Union[str, Any]						,	snake_case_ :       int      ):
							UpperCamelCase_:							List[str]	=						MobileNetVaModel(config=A_      )
							model.to(A_      )
							model.eval()
							UpperCamelCase_:							List[Any]	=						model(A_      )
							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       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Tuple						,	snake_case_ :       Dict						,	snake_case_ :       str      ):
							UpperCamelCase_:							List[Any]	=						self.num_labels
							UpperCamelCase_:							List[str]	=						MobileNetVaForImageClassification(A_      )
							model.to(A_      )
							model.eval()
							UpperCamelCase_:							Tuple	=						model(A_						,	labels=A_      )
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)      )




			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Optional[int]	=						self.prepare_config_and_inputs()
							UpperCamelCase_:							int	=						config_and_inputs
							UpperCamelCase_:							Optional[Any]	=						{"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class        _UpperCamelCase				(					__snake_case     ,       __snake_case     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
			__UpperCamelCase      :						Optional[int]					      = (
			    {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__UpperCamelCase      :						Optional[Any]					      = False
			__UpperCamelCase      :						List[str]					      = False
			__UpperCamelCase      :						int					      = False
			__UpperCamelCase      :						int					      = False

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Union[str, Any]	=						MobileNetVaModelTester(self      )
							UpperCamelCase_:							List[str]	=						MobileNetVaConfigTester(self						,	config_class=A_						,	has_text_modality=A_      )

			def       lowerCAmelCase__	(       self :       List[str]      ):
							self.config_tester.run_common_tests()

			@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""      )
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							pass

			@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""      )
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							pass

			@unittest.skip(reason="""MobileNetV1 does not output attentions"""      )
			def       lowerCAmelCase__	(       self :       Dict      ):
							pass

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Any	=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:							List[str]	=						model_class(A_      )
											UpperCamelCase_:							Dict	=						inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCamelCase_:							List[Any]	=						[*signature.parameters.keys()]

											UpperCamelCase_:							Optional[Any]	=						["pixel_values"]
											self.assertListEqual(arg_names[:1]						,	A_      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Optional[int]	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )

			def       lowerCAmelCase__	(       self :       Any      ):




							def check_hidden_states_output(snake_case_ :       Any						,	snake_case_ :       Any						,	snake_case_ :       Optional[Any]      ):
											UpperCamelCase_:							Dict	=						model_class(A_      )
											model.to(A_      )
											model.eval()

											with torch.no_grad():
															UpperCamelCase_:							str	=						model(**self._prepare_for_class(A_						,	A_      )      )

											UpperCamelCase_:							Union[str, Any]	=						outputs.hidden_states

											UpperCamelCase_:							List[str]	=						26
											self.assertEqual(len(A_      )						,	A_      )

							UpperCamelCase_:							int	=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:							Optional[int]	=						True
											check_hidden_states_output(A_						,	A_						,	A_      )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											UpperCamelCase_:							List[Any]	=						True

											check_hidden_states_output(A_						,	A_						,	A_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Optional[Any]	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_      )




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




							for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase_:							Tuple	=						MobileNetVaModel.from_pretrained(A_      )
											self.assertIsNotNone(A_      )







def        A__					(						)  ->	Any:
				UpperCamelCase_:							List[str]	=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
				return image





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






			'''simple docstring'''

			@cached_property
			def       lowerCAmelCase__	(       self :       Any      ):
							return (
							    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""      ) if is_vision_available() else None
							)




			@slow
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Tuple	=						MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""      ).to(A_      )

							UpperCamelCase_:							List[Any]	=						self.default_image_processor
							UpperCamelCase_:							Dict	=						prepare_img()
							UpperCamelCase_:							Any	=						image_processor(images=A_						,	return_tensors="""pt"""      ).to(A_      )

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							Any	=						model(**A_      )

							# verify the logits
							UpperCamelCase_:							Optional[int]	=						torch.Size((1, 1001)      )
							self.assertEqual(outputs.logits.shape						,	A_      )

							UpperCamelCase_:							Any	=						torch.tensor([-4.1739, -1.1233, 3.1205]      ).to(A_      )

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








import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

				# Load weights from tf checkpoint
				load_tf_weights_in_ta(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






