





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       int						,	snake_case_ :       Optional[Any]						,	snake_case_ :       str=13						,	snake_case_ :       Any=64						,	snake_case_ :       List[Any]=2						,	snake_case_ :       str=3						,	snake_case_ :       Tuple=True						,	snake_case_ :       str=True						,	snake_case_ :       Union[str, Any]=32						,	snake_case_ :       Dict=5						,	snake_case_ :       Optional[Any]=4						,	snake_case_ :       List[Any]=37						,	snake_case_ :       Dict="gelu"						,	snake_case_ :       List[Any]=0.1						,	snake_case_ :       int=0.1						,	snake_case_ :       Dict=10						,	snake_case_ :       Any=0.02						,	snake_case_ :       Any=[1, 16, 4, 4]						,	snake_case_ :       str=None						,	):
							UpperCamelCase_:							Dict	=						parent
							UpperCamelCase_:							List[str]	=						batch_size
							UpperCamelCase_:							Optional[Any]	=						image_size
							UpperCamelCase_:							Tuple	=						patch_size
							UpperCamelCase_:							List[str]	=						num_channels
							UpperCamelCase_:							Tuple	=						is_training
							UpperCamelCase_:							List[Any]	=						use_labels
							UpperCamelCase_:							Optional[Any]	=						hidden_size
							UpperCamelCase_:							Dict	=						num_hidden_layers
							UpperCamelCase_:							str	=						num_attention_heads
							UpperCamelCase_:							str	=						intermediate_size
							UpperCamelCase_:							Optional[Any]	=						hidden_act
							UpperCamelCase_:							int	=						hidden_dropout_prob
							UpperCamelCase_:							Any	=						attention_probs_dropout_prob
							UpperCamelCase_:							Any	=						type_sequence_label_size
							UpperCamelCase_:							Optional[Any]	=						initializer_range
							UpperCamelCase_:							str	=						scope
							UpperCamelCase_:							List[Any]	=						backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							UpperCamelCase_:							Dict	=						(self.image_size // 32) ** 2
							UpperCamelCase_:							Tuple	=						num_patches + 1

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Dict	=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

							UpperCamelCase_:							List[str]	=						None
							if self.use_labels:
											UpperCamelCase_:							List[str]	=						ids_tensor([self.batch_size]						,	self.type_sequence_label_size      )

							UpperCamelCase_:							str	=						self.get_config()

							return config, pixel_values, labels

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Any	=						{
							    """global_padding""": """same""",
							    """layer_type""": """bottleneck""",
							    """depths""": [3, 4, 9],
							    """out_features""": ["""stage1""", """stage2""", """stage3"""],
							    """embedding_dynamic_padding""": True,
							    """hidden_sizes""": [4, 8, 16, 32],
							    """num_groups""": 2,
							}

							return ViTHybridConfig(
							    image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	is_decoder=snake_case_						,	initializer_range=self.initializer_range						,	backbone_featmap_shape=self.backbone_featmap_shape						,	backbone_config=snake_case_						,	)

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Tuple						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[Any]      ):
							UpperCamelCase_:							Optional[int]	=						ViTHybridModel(config=snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Union[str, Any]	=						model(snake_case_      )
							self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Tuple						,	snake_case_ :       Tuple						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							str	=						self.type_sequence_label_size
							UpperCamelCase_:							Optional[Any]	=						ViTHybridForImageClassification(snake_case_      )
							model.to(snake_case_      )
							model.eval()
							UpperCamelCase_:							Tuple	=						model(snake_case_						,	labels=snake_case_      )
							self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)      )




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




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






			'''simple docstring'''

			__UpperCamelCase      :						Optional[Any]					      = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			__UpperCamelCase      :						Optional[Any]					      = (
			    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			__UpperCamelCase      :						List[Any]					      = False
			__UpperCamelCase      :						str					      = False
			__UpperCamelCase      :						Optional[Any]					      = False

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Dict	=						ViTHybridModelTester(self      )
							UpperCamelCase_:							Tuple	=						ConfigTester(self						,	config_class=snake_case_						,	has_text_modality=snake_case_						,	hidden_size=37      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							self.config_tester.run_common_tests()

			@unittest.skip(reason="""ViT does not use inputs_embeds"""      )
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							pass

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_,   UpperCamelCase_:							str	=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:							Any	=						model_class(snake_case_      )
											self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)      )
											UpperCamelCase_:							Optional[int]	=						model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(snake_case_						,	nn.Linear      )      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_,   UpperCamelCase_:							Optional[int]	=						self.model_tester.prepare_config_and_inputs_for_common()

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

											UpperCamelCase_:							List[str]	=						["""pixel_values"""]
											self.assertListEqual(arg_names[:1]						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Tuple	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case_      )

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

							UpperCamelCase_:							Tuple	=						_config_zero_init(snake_case_      )
							for model_class in self.all_model_classes:
											UpperCamelCase_:							Dict	=						model_class(config=snake_case_      )
											# Skip the check for the backbone
											for name, module in model.named_modules():
															if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																			UpperCamelCase_:							Dict	=						[f'''{name}.{key}''' for key in module.state_dict().keys()]
																			break

											for name, param in model.named_parameters():
															if param.requires_grad:
																			if name in backbone_params:
																							continue
																			self.assertIn(
																			    ((param.data.mean() * 1e9).round() / 1e9).item()						,	[0.0, 1.0]						,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''						,	)




			@slow
			def       lowerCAmelCase__	(       self :       str      ):

							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase_:							List[Any]	=						ViTHybridModel.from_pretrained(snake_case_      )
											self.assertIsNotNone(snake_case_      )







def        A__					(						)  ->	str:
				UpperCamelCase_:							Tuple	=						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 (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
							    if is_vision_available()
							    else None
							)

			@slow
			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
							    snake_case_      )

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

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							List[Any]	=						model(**snake_case_      )

							# verify the logits
							UpperCamelCase_:							Union[str, Any]	=						torch.Size((1, 1000)      )
							self.assertEqual(outputs.logits.shape						,	snake_case_      )

							UpperCamelCase_:							Any	=						torch.tensor([-1.9090, -0.4993, -0.2389]      ).to(snake_case_      )

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




			@slow
			@require_accelerate
			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							List[Any]	=						ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""      )
							UpperCamelCase_:							Any	=						ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""						,	device_map="""auto"""      )

							UpperCamelCase_:							Tuple	=						prepare_img()

							UpperCamelCase_:							Optional[Any]	=						image_processor(images=snake_case_						,	return_tensors="""pt"""      )
							UpperCamelCase_:							Optional[Any]	=						model(**snake_case_      )
							UpperCamelCase_:							str	=						outputs.logits
							# model predicts one of the 1000 ImageNet classes
							UpperCamelCase_:							str	=						logits.argmax(-1      ).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]						,	"""tabby, tabby cat"""      )








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)






