






import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Tuple=13,  lowerCamelCase						:    Optional[int]=32,  lowerCamelCase						:    Union[str, Any]=3,  lowerCamelCase						:    Tuple=4,  lowerCamelCase						:    List[str]=[10, 20, 30, 40],  lowerCamelCase						:    Optional[Any]=[2, 2, 3, 2],  lowerCamelCase						:    Any=True,  lowerCamelCase						:    Optional[int]=True,  lowerCamelCase						:    List[str]=37,  lowerCamelCase						:    Dict="gelu",  lowerCamelCase						:    str=10,  lowerCamelCase						:    Any=0.02,  lowerCamelCase						:    Dict=["stage2", "stage3", "stage4"],  lowerCamelCase						:    Optional[int]=[2, 3, 4],  lowerCamelCase						:    int=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			image_size
								lowercase__    =			num_channels
								lowercase__    =			num_stages
								lowercase__    =			hidden_sizes
								lowercase__    =			depths
								lowercase__    =			is_training
								lowercase__    =			use_labels
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			num_labels
								lowercase__    =			initializer_range
								lowercase__    =			out_features
								lowercase__    =			out_indices
								lowercase__    =			scope




				def 				lowercase__   (     self						:    List[str]  ):

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

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

								lowercase__    =			self.get_config()

								return config, pixel_values, labels




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								return ConvNextVaConfig(
								    num_channels=self.num_channels,  hidden_sizes=self.hidden_sizes,  depths=self.depths,  num_stages=self.num_stages,  hidden_act=self.hidden_act,  is_decoder=lowerCamelCase,  initializer_range=self.initializer_range,  out_features=self.out_features,  out_indices=self.out_indices,  num_labels=self.num_labels,  )




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

								'''simple docstring'''
								lowercase__    =			ConvNextVaModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			ConvNextVaForImageClassification(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,  lowerCamelCase						:    str,  lowerCamelCase						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ConvNextVaBackbone(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )

								# verify hidden states
								self.parent.assertEqual(len(result.feature_maps  ),  len(config.out_features  )  )
								self.parent.assertListEqual(list(result.feature_maps[0].shape  ),  [self.batch_size, self.hidden_sizes[1], 4, 4]  )

								# verify channels
								self.parent.assertEqual(len(model.channels  ),  len(config.out_features  )  )
								self.parent.assertListEqual(model.channels,  config.hidden_sizes[1:]  )

								# verify backbone works with out_features=None
								lowercase__    =			None
								lowercase__    =			ConvNextVaBackbone(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps  ),  1  )
								self.parent.assertListEqual(list(result.feature_maps[0].shape  ),  [self.batch_size, self.hidden_sizes[-1], 1, 1]  )

								# verify channels
								self.parent.assertEqual(len(model.channels  ),  1  )
								self.parent.assertListEqual(model.channels,  [config.hidden_sizes[-1]]  )




				def 				lowercase__   (     self						:    str  ):

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




				def 				lowercase__   (     self						:    int  ):

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



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

				"""simple docstring"""




				lowercase__											=       (
				    (
				        ConvNextVaModel,
				        ConvNextVaForImageClassification,
				        ConvNextVaBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__											=       (
				    {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
				    if is_torch_available()
				    else {}
				)

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




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			ConvNextVaModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase,  hidden_size=37  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								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 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								return




				@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds'''  )
				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings'''  )
				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if not self.model_tester.is_training:
												return

								for model_class in self.all_model_classes:
												lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_with_labels()
												lowercase__    =			True

												if model_class.__name__ in [
												    *get_values(lowerCamelCase  ),
												    *get_values(lowerCamelCase  ),
												]:
																continue

												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.train()
												lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__    =			model(**lowerCamelCase  ).loss
												loss.backward()




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

								'''simple docstring'''
								if not self.model_tester.is_training:
												return

								for model_class in self.all_model_classes:
												lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_with_labels()
												lowercase__    =			False
												lowercase__    =			True

												if (
												    model_class.__name__
												    in [*get_values(lowerCamelCase  ), *get_values(lowerCamelCase  )]
												    or not model_class.supports_gradient_checkpointing
												):
																continue

												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.gradient_checkpointing_enable()
												model.train()
												lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__    =			model(**lowerCamelCase  ).loss
												loss.backward()




				def 				lowercase__   (     self						:    str  ):

								'''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						:    Optional[int]  ):

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

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

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

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

												# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]  ),  [self.model_tester.image_size // 4, self.model_tester.image_size // 4],  )

								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						:    Optional[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						:    Tuple  ):

								'''simple docstring'''
								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			ConvNextVaModel.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 AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224'''  ) if is_vision_available() else None




				@slow
				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224'''  ).to(lowerCamelCase  )

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

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

								# verify the logits
								lowercase__    =			torch.Size((1, 1_000)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )

								lowercase__    =			torch.tensor([0.9996, 0.1966, -0.4386]  ).to(lowerCamelCase  )
								self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4  )  )












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()




