




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

class     A					:







							def __init__(						self :						Dict							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						int=13							,    lowerCAmelCase_ :						List[str]=3							,    lowerCAmelCase_ :						Any=True							,    lowerCAmelCase_ :						Union[str, Any]=True							,    lowerCAmelCase_ :						Any=0.1							,    lowerCAmelCase_ :						Optional[int]=0.1							,    lowerCAmelCase_ :						List[Any]=2_24							,    lowerCAmelCase_ :						Any=10_00							,    lowerCAmelCase_ :						Tuple=[3, 3, 6, 4]							,    lowerCAmelCase_ :						Union[str, Any]=[48, 56, 1_12, 2_20]							,    )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       parent
														_a			  =       batch_size
														_a			  =       num_channels
														_a			  =       is_training
														_a			  =       use_labels
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       num_labels
														_a			  =       image_size
														_a			  =       layer_depths
														_a			  =       embed_dims







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Any:


														"""simple docstring"""


														_a			  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

														_a			  =       self.get_config()

														return config, pixel_values, labels







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


														return SwiftFormerConfig(
														    depths=self.layer_depths							,    embed_dims=self.embed_dims							,    mlp_ratio=4							,    downsamples=[True, True, True, True]							,    hidden_act='''gelu'''							,    num_labels=self.num_labels							,    down_patch_size=3							,    down_stride=2							,    down_pad=1							,    drop_rate=0.0							,    drop_path_rate=0.0							,    use_layer_scale=lowerCAmelCase_							,    layer_scale_init_value=1e-5							,    )







							def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       SwiftFormerModel(config=lowerCAmelCase_      )
														model.to(lowerCAmelCase_      )
														model.eval()
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.embed_dims[-1], 7, 7)      )







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Optional[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.num_labels
														_a			  =       SwiftFormerForImageClassification(lowerCAmelCase_      )
														model.to(lowerCAmelCase_      )
														model.eval()
														_a			  =       model(lowerCAmelCase_							,    labels=lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)      )

														_a			  =       SwiftFormerForImageClassification(lowerCAmelCase_      )
														model.to(lowerCAmelCase_      )
														model.eval()

														_a			  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														((_a)  ,							(_a)  ,							(_a))			  =       self.prepare_config_and_inputs()
														_a			  =       {'''pixel_values''': pixel_values}
														return config, inputs_dict



@require_torch
class     A					(	_a  ,_a  ,unittest.TestCase					):
							lowercase_           =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							lowercase_           =       (
							    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase_           =       False
							lowercase_           =       False
							lowercase_           =       False
							lowercase_           =       False
							lowercase_           =       False







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       SwiftFormerModelTester(self      )
														_a			  =       ConfigTester(
														    self							,    config_class=lowerCAmelCase_							,    has_text_modality=lowerCAmelCase_							,    hidden_size=37							,    num_attention_heads=12							,    num_hidden_layers=12							,    )







							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[int]:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''      )
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							int:


														"""simple docstring"""


														pass







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a			  =       model_class(lowerCAmelCase_      )
																					_a			  =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowerCAmelCase_							,    nn.Linear      )      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					_a			  =       ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_      )







							@slow
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Union[str, Any]:


														"""simple docstring"""


														for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_a			  =       SwiftFormerModel.from_pretrained(lowerCAmelCase_      )
																					self.assertIsNotNone(lowerCAmelCase_      )







							@unittest.skip(reason='''SwiftFormer does not output attentions'''      )
							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[int]:


														"""simple docstring"""


														pass







							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														def check_hidden_states_output(lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						List[Any]      ):
																					_a			  =       model_class(lowerCAmelCase_      )
																					model.to(lowerCAmelCase_      )
																					model.eval()

																					with torch.no_grad():
																												_a			  =       model(**self._prepare_for_class(lowerCAmelCase_							,    lowerCAmelCase_      )      )

																					_a			  =       outputs.hidden_states

																					_a			  =       8
																					self.assertEqual(len(lowerCAmelCase_      )							,    lowerCAmelCase_      )  # TODO

																					# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																					# with the width and height being successively divided by 2, after every 2 blocks
																					for i in range(len(lowerCAmelCase_      )      ):
																												self.assertEqual(
																												    hidden_states[i].shape							,    torch.Size(
																												        [
																												            self.model_tester.batch_size,
																												            self.model_tester.embed_dims[i // 2],
																												            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																												            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																												        ]      )							,    )

														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a			  =       True
																					check_hidden_states_output(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )

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

																					check_hidden_states_output(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							List[str]:


														"""simple docstring"""


														def _config_zero_init(lowerCAmelCase_ :						Optional[Any]      ):
																					_a			  =       copy.deepcopy(lowerCAmelCase_      )
																					for key in configs_no_init.__dict__.keys():
																												if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																																			setattr(lowerCAmelCase_							,    lowerCAmelCase_							,    1e-10      )
																												if isinstance(getattr(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )							,    lowerCAmelCase_      ):
																																			_a			  =       _config_zero_init(getattr(lowerCAmelCase_							,    lowerCAmelCase_      )      )
																																			setattr(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )
																					return configs_no_init

														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

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







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
							def      __lowerCAmelCase    (						self :						Any      )    ->							int:


														"""simple docstring"""


														pass



def 	snake_case_					():





							'''simple docstring'''


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

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







							@cached_property
							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Any:


														"""simple docstring"""


														return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''      ) if is_vision_available() else None







							@slow
							def      __lowerCAmelCase    (						self :						Tuple      )    ->							int:


														"""simple docstring"""


														_a			  =       SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''      ).to(lowerCAmelCase_      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''pt'''      ).to(lowerCAmelCase_      )

														# forward pass
														with torch.no_grad():
																					_a			  =       model(**lowerCAmelCase_      )

														# verify the logits
														_a			  =       torch.Size((1, 10_00)      )
														self.assertEqual(outputs.logits.shape							,    lowerCAmelCase_      )

														_a			  =       torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]      ).to(lowerCAmelCase_      )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]							,    lowerCAmelCase_							,    atol=1e-4      )      )







'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	snake_case_					(UpperCamelCase :						BertModel				,		UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

							_a			  =       (
							    ('''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(UpperCamelCase   ):
														os.makedirs(UpperCamelCase   )

							_a			  =       model.state_dict()

							def to_tf_var_name(UpperCamelCase :						str   ):
														for patt, repl in iter(UpperCamelCase   ):
																					_a			  =       name.replace(UpperCamelCase				,		UpperCamelCase   )
														return f'bert/{name}'

							def create_tf_var(UpperCamelCase :						np.ndarray				,		UpperCamelCase :						str				,		UpperCamelCase :						tf.Session   ):
														_a			  =       tf.dtypes.as_dtype(tensor.dtype   )
														_a			  =       tf.get_variable(dtype=UpperCamelCase				,		shape=tensor.shape				,		name=UpperCamelCase				,		initializer=tf.zeros_initializer()   )
														session.run(tf.variables_initializer([tf_var]   )   )
														session.run(UpperCamelCase   )
														return tf_var

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					_a			  =       to_tf_var_name(UpperCamelCase   )
																					_a			  =       state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose   ):
																												_a			  =       torch_tensor.T
																					_a			  =       create_tf_var(tensor=UpperCamelCase				,		name=UpperCamelCase				,		session=UpperCamelCase   )
																					tf.keras.backend.set_value(UpperCamelCase				,		UpperCamelCase   )
																					_a			  =       session.run(UpperCamelCase   )
																					print(f'Successfully created {tf_name}: {np.allclose(UpperCamelCase				,		UpperCamelCase   )}'   )

														_a			  =       tf.train.Saver(tf.trainable_variables()   )
														saver.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		model_name.replace('''-'''				,		'''_'''   ) + '''.ckpt'''   )   )
def 	snake_case_					(UpperCamelCase :						Tuple=None   ):





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument('''--model_name'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''model name e.g. bert-base-uncased'''   )
							parser.add_argument(
							    '''--cache_dir'''				,		type=UpperCamelCase				,		default=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory containing pytorch model'''   )
							parser.add_argument('''--pytorch_model_path'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''/path/to/<pytorch-model-name>.bin'''   )
							parser.add_argument('''--tf_cache_dir'''				,		type=UpperCamelCase				,		required=UpperCamelCase				,		help='''Directory in which to save tensorflow model'''   )
							_a			  =       parser.parse_args(UpperCamelCase   )

							_a			  =       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=UpperCamelCase				,		ckpt_dir=args.tf_cache_dir				,		model_name=args.model_name   )


if __name__ == "__main__":
				main()

