




'''simple docstring'''


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import YolosImageProcessor

class     A					(	unittest.TestCase					):







							def __init__(						self :						Dict							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Any=7							,    lowerCAmelCase_ :						Any=3							,    lowerCAmelCase_ :						List[Any]=30							,    lowerCAmelCase_ :						Optional[Any]=4_00							,    lowerCAmelCase_ :						Optional[int]=True							,    lowerCAmelCase_ :						List[Any]=None							,    lowerCAmelCase_ :						int=True							,    lowerCAmelCase_ :						str=[0.5, 0.5, 0.5]							,    lowerCAmelCase_ :						Optional[Any]=[0.5, 0.5, 0.5]							,    lowerCAmelCase_ :						Optional[int]=True							,    lowerCAmelCase_ :						Optional[int]=1 / 2_55							,    lowerCAmelCase_ :						int=True							,    )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
														_a			  =       parent
														_a			  =       batch_size
														_a			  =       num_channels
														_a			  =       min_resolution
														_a			  =       max_resolution
														_a			  =       do_resize
														_a			  =       size
														_a			  =       do_normalize
														_a			  =       image_mean
														_a			  =       image_std
														_a			  =       do_rescale
														_a			  =       rescale_factor
														_a			  =       do_pad







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


														"""simple docstring"""


														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_rescale": self.do_rescale,
														    "rescale_factor": self.rescale_factor,
														    "do_pad": self.do_pad,
														}







							def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Union[str, Any]=False      )    ->							Dict:


														"""simple docstring"""


														if not batched:
																					_a			  =       image_inputs[0]
																					if isinstance(lowerCAmelCase_							,    Image.Image      ):
																												_a  ,							_a			  =       image.size
																					else:
																												_a  ,							_a			  =       image.shape[1], image.shape[2]
																					if w < h:
																												_a			  =       int(self.size['''shortest_edge'''] * h / w      )
																												_a			  =       self.size['''shortest_edge''']
																					elif w > h:
																												_a			  =       self.size['''shortest_edge''']
																												_a			  =       int(self.size['''shortest_edge'''] * w / h      )
																					else:
																												_a			  =       self.size['''shortest_edge''']
																												_a			  =       self.size['''shortest_edge''']

														else:
																					_a			  =       []
																					for image in image_inputs:
																												_a  ,							_a			  =       self.get_expected_values([image]      )
																												expected_values.append((expected_height, expected_width)      )
																					_a			  =       max(lowerCAmelCase_							,    key=lambda lowerCAmelCase_      : item[0]      )[0]
																					_a			  =       max(lowerCAmelCase_							,    key=lambda lowerCAmelCase_      : item[1]      )[1]

														return expected_height, expected_width



@require_torch
@require_vision
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       YolosImageProcessor if is_vision_available() else None







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


														"""simple docstring"""


														_a			  =       YolosImageProcessingTester(self      )







							@property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							str:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def      __lowerCAmelCase    (						self :						int      )    ->							int:


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														self.assertTrue(hasattr(lowerCAmelCase_							,    '''image_mean'''      )      )
														self.assertTrue(hasattr(lowerCAmelCase_							,    '''image_std'''      )      )
														self.assertTrue(hasattr(lowerCAmelCase_							,    '''do_normalize'''      )      )
														self.assertTrue(hasattr(lowerCAmelCase_							,    '''do_resize'''      )      )
														self.assertTrue(hasattr(lowerCAmelCase_							,    '''size'''      )      )







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


														"""simple docstring"""


														_a			  =       self.image_processing_class.from_dict(self.image_processor_dict      )
														self.assertEqual(image_processor.size							,    {'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
														self.assertEqual(image_processor.do_pad							,    lowerCAmelCase_      )

														_a			  =       self.image_processing_class.from_dict(
														    self.image_processor_dict							,    size=42							,    max_size=84							,    pad_and_return_pixel_mask=lowerCAmelCase_      )
														self.assertEqual(image_processor.size							,    {'''shortest_edge''': 42, '''longest_edge''': 84}      )
														self.assertEqual(image_processor.do_pad							,    lowerCAmelCase_      )







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


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														# create random PIL images
														_a			  =       prepare_image_inputs(self.image_processor_tester							,    equal_resolution=lowerCAmelCase_      )
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_							,    Image.Image      )

														# Test not batched input
														_a			  =       image_processing(image_inputs[0]							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_      )

														self.assertEqual(
														    encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

														# Test batched
														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_							,    batched=lowerCAmelCase_      )

														_a			  =       image_processing(lowerCAmelCase_							,    return_tensors='''pt'''      ).pixel_values
														self.assertEqual(
														    encoded_images.shape							,    (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,    )







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


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														# create random numpy tensors
														_a			  =       prepare_image_inputs(self.image_processor_tester							,    equal_resolution=lowerCAmelCase_							,    numpify=lowerCAmelCase_      )
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_							,    np.ndarray      )

														# Test not batched input
														_a			  =       image_processing(image_inputs[0]							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_      )

														self.assertEqual(
														    encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

														# Test batched
														_a			  =       image_processing(lowerCAmelCase_							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_							,    batched=lowerCAmelCase_      )

														self.assertEqual(
														    encoded_images.shape							,    (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,    )







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														# create random PyTorch tensors
														_a			  =       prepare_image_inputs(self.image_processor_tester							,    equal_resolution=lowerCAmelCase_							,    torchify=lowerCAmelCase_      )
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_							,    torch.Tensor      )

														# Test not batched input
														_a			  =       image_processing(image_inputs[0]							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_      )

														self.assertEqual(
														    encoded_images.shape							,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,    )

														# Test batched
														_a			  =       image_processing(lowerCAmelCase_							,    return_tensors='''pt'''      ).pixel_values

														_a  ,							_a			  =       self.image_processor_tester.get_expected_values(lowerCAmelCase_							,    batched=lowerCAmelCase_      )

														self.assertEqual(
														    encoded_images.shape							,    (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,    )







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


														"""simple docstring"""


														_a			  =       self.image_processing_class(**self.image_processor_dict      )
														_a			  =       self.image_processing_class(do_resize=lowerCAmelCase_							,    do_normalize=lowerCAmelCase_							,    do_rescale=lowerCAmelCase_      )
														# create random PyTorch tensors
														_a			  =       prepare_image_inputs(self.image_processor_tester							,    equal_resolution=lowerCAmelCase_							,    torchify=lowerCAmelCase_      )
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_							,    torch.Tensor      )

														# Test whether the method "pad" and calling the image processor return the same tensors
														_a			  =       image_processing_a.pad(lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       image_processing_a(lowerCAmelCase_							,    return_tensors='''pt'''      )

														self.assertTrue(
														    torch.allclose(encoded_images_with_method['''pixel_values''']							,    encoded_images['''pixel_values''']							,    atol=1e-4      )      )







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


														"""simple docstring"""


														_a			  =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
														with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,    '''r'''      ) as f:
																					_a			  =       json.loads(f.read()      )

														_a			  =       {'''image_id''': 3_97_69, '''annotations''': target}

														# encode them
														_a			  =       YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''      )
														_a			  =       image_processing(images=lowerCAmelCase_							,    annotations=lowerCAmelCase_							,    return_tensors='''pt'''      )

														# verify pixel values
														_a			  =       torch.Size([1, 3, 8_00, 10_66]      )
														self.assertEqual(encoding['''pixel_values'''].shape							,    lowerCAmelCase_      )

														_a			  =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
														self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,    lowerCAmelCase_							,    atol=1e-4      )      )

														# verify area
														_a			  =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,    lowerCAmelCase_      )      )
														# verify boxes
														_a			  =       torch.Size([6, 4]      )
														self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,    lowerCAmelCase_      )
														_a			  =       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,    lowerCAmelCase_							,    atol=1e-3      )      )
														# verify image_id
														_a			  =       torch.tensor([3_97_69]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,    lowerCAmelCase_      )      )
														# verify is_crowd
														_a			  =       torch.tensor([0, 0, 0, 0, 0, 0]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,    lowerCAmelCase_      )      )
														# verify class_labels
														_a			  =       torch.tensor([75, 75, 63, 65, 17, 17]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,    lowerCAmelCase_      )      )
														# verify orig_size
														_a			  =       torch.tensor([4_80, 6_40]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,    lowerCAmelCase_      )      )
														# verify size
														_a			  =       torch.tensor([8_00, 10_66]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,    lowerCAmelCase_      )      )







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


														"""simple docstring"""


														_a			  =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
														with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,    '''r'''      ) as f:
																					_a			  =       json.loads(f.read()      )

														_a			  =       {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

														_a			  =       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

														# encode them
														_a			  =       YolosImageProcessor(format='''coco_panoptic'''      )
														_a			  =       image_processing(images=lowerCAmelCase_							,    annotations=lowerCAmelCase_							,    masks_path=lowerCAmelCase_							,    return_tensors='''pt'''      )

														# verify pixel values
														_a			  =       torch.Size([1, 3, 8_00, 10_66]      )
														self.assertEqual(encoding['''pixel_values'''].shape							,    lowerCAmelCase_      )

														_a			  =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
														self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,    lowerCAmelCase_							,    atol=1e-4      )      )

														# verify area
														_a			  =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,    lowerCAmelCase_      )      )
														# verify boxes
														_a			  =       torch.Size([6, 4]      )
														self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,    lowerCAmelCase_      )
														_a			  =       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,    lowerCAmelCase_							,    atol=1e-3      )      )
														# verify image_id
														_a			  =       torch.tensor([3_97_69]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,    lowerCAmelCase_      )      )
														# verify is_crowd
														_a			  =       torch.tensor([0, 0, 0, 0, 0, 0]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,    lowerCAmelCase_      )      )
														# verify class_labels
														_a			  =       torch.tensor([17, 17, 63, 75, 75, 93]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,    lowerCAmelCase_      )      )
														# verify masks
														_a			  =       82_28_73
														self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,    lowerCAmelCase_      )
														# verify orig_size
														_a			  =       torch.tensor([4_80, 6_40]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,    lowerCAmelCase_      )      )
														# verify size
														_a			  =       torch.tensor([8_00, 10_66]      )
														self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,    lowerCAmelCase_      )      )







'''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()

