


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 	__lowercase					(	unittest.TestCase	):





      def __init__(		self    :       Dict       ,							__lowerCamelCase    :       List[Any]       ,							__lowerCamelCase    :       Any=7       ,							__lowerCamelCase    :       Optional[int]=3       ,							__lowerCamelCase    :       Dict=3_0       ,							__lowerCamelCase    :       Dict=4_0_0       ,							__lowerCamelCase    :       Dict=True       ,							__lowerCamelCase    :       str=None       ,							__lowerCamelCase    :       Optional[Any]=True       ,							__lowerCamelCase    :       Dict=[0.5, 0.5, 0.5]       ,							__lowerCamelCase    :       Tuple=[0.5, 0.5, 0.5]       ,							__lowerCamelCase    :       Optional[Any]=True       ,							__lowerCamelCase    :       Optional[Any]=1 / 2_5_5       ,							__lowerCamelCase    :       Any=True       ,							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  min_resolution
         UpperCAmelCase      					=  max_resolution
         UpperCAmelCase      					=  do_resize
         UpperCAmelCase      					=  size
         UpperCAmelCase      					=  do_normalize
         UpperCAmelCase      					=  image_mean
         UpperCAmelCase      					=  image_std
         UpperCAmelCase      					=  do_rescale
         UpperCAmelCase      					=  rescale_factor
         UpperCAmelCase      					=  do_pad





      def 							_lowercase					(		self    :       Any							)     -> Optional[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 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       int=False							)     -> Union[str, Any]:






         """simple docstring"""







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

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

         return expected_height, expected_width






@require_torch
@require_vision
class 	__lowercase					(	__snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							YolosImageProcessor if is_vision_available() else None





      def 							_lowercase					(		self    :       Optional[int]							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  YolosImageProcessingTester(self							)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> str:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





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






         """simple docstring"""







         UpperCAmelCase      					=  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 							_lowercase					(		self    :       str							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,							{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}							)
         self.assertEqual(image_processor.do_pad       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  self.image_processing_class.from_dict(
             self.image_processor_dict       ,							size=4_2       ,							max_size=8_4       ,							pad_and_return_pixel_mask=__lowerCamelCase							)
         self.assertEqual(image_processor.size       ,							{"""shortest_edge""": 4_2, """longest_edge""": 8_4}							)
         self.assertEqual(image_processor.do_pad       ,							__lowerCamelCase							)





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






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       int							)     -> Any:






         """simple docstring"""







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

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

         UpperCAmelCase					,	UpperCAmelCase      					=  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
         UpperCAmelCase					,	UpperCAmelCase      					=  self.image_processor_tester.get_expected_values(__lowerCamelCase       ,							batched=__lowerCamelCase							)

         UpperCAmelCase      					=  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 							_lowercase					(		self    :       int							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing(__lowerCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  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 							_lowercase					(		self    :       Optional[int]							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing(image_inputs[0]       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing(__lowerCamelCase       ,							return_tensors="""pt"""							).pixel_values

         UpperCAmelCase					,	UpperCAmelCase      					=  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 							_lowercase					(		self    :       Union[str, Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.image_processing_class(**self.image_processor_dict							)
         UpperCAmelCase      					=  self.image_processing_class(do_resize=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_rescale=__lowerCamelCase							)
         # create random PyTorch tensors
         UpperCAmelCase      					=  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
         UpperCAmelCase      					=  image_processing_a.pad(__lowerCamelCase       ,							return_tensors="""pt"""							)
         UpperCAmelCase      					=  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 							_lowercase					(		self    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







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

         UpperCAmelCase      					=  {"""image_id""": 3_9_7_6_9, """annotations""": target}

         # encode them
         UpperCAmelCase      					=  YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""							)
         UpperCAmelCase      					=  image_processing(images=__lowerCamelCase       ,							annotations=__lowerCamelCase       ,							return_tensors="""pt"""							)

         # verify pixel values
         UpperCAmelCase      					=  torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							__lowerCamelCase       ,							atol=1e-4							)							)

         # verify area
         UpperCAmelCase      					=  torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							__lowerCamelCase							)							)
         # verify boxes
         UpperCAmelCase      					=  torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							__lowerCamelCase							)
         UpperCAmelCase      					=  torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							__lowerCamelCase       ,							atol=1e-3							)							)
         # verify image_id
         UpperCAmelCase      					=  torch.tensor([3_9_7_6_9]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							__lowerCamelCase							)							)
         # verify is_crowd
         UpperCAmelCase      					=  torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							__lowerCamelCase							)							)
         # verify class_labels
         UpperCAmelCase      					=  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							__lowerCamelCase							)							)
         # verify orig_size
         UpperCAmelCase      					=  torch.tensor([4_8_0, 6_4_0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							__lowerCamelCase							)							)
         # verify size
         UpperCAmelCase      					=  torch.tensor([8_0_0, 1_0_6_6]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							__lowerCamelCase							)							)





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> Union[str, Any]:






         """simple docstring"""







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

         UpperCAmelCase      					=  {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

         UpperCAmelCase      					=  pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

         # encode them
         UpperCAmelCase      					=  YolosImageProcessor(format="""coco_panoptic"""							)
         UpperCAmelCase      					=  image_processing(images=__lowerCamelCase       ,							annotations=__lowerCamelCase       ,							masks_path=__lowerCamelCase       ,							return_tensors="""pt"""							)

         # verify pixel values
         UpperCAmelCase      					=  torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,							__lowerCamelCase       ,							atol=1e-4							)							)

         # verify area
         UpperCAmelCase      					=  torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,							__lowerCamelCase							)							)
         # verify boxes
         UpperCAmelCase      					=  torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,							__lowerCamelCase							)
         UpperCAmelCase      					=  torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,							__lowerCamelCase       ,							atol=1e-3							)							)
         # verify image_id
         UpperCAmelCase      					=  torch.tensor([3_9_7_6_9]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,							__lowerCamelCase							)							)
         # verify is_crowd
         UpperCAmelCase      					=  torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,							__lowerCamelCase							)							)
         # verify class_labels
         UpperCAmelCase      					=  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,							__lowerCamelCase							)							)
         # verify masks
         UpperCAmelCase      					=  8_2_2_8_7_3
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,							__lowerCamelCase							)
         # verify orig_size
         UpperCAmelCase      					=  torch.tensor([4_8_0, 6_4_0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,							__lowerCamelCase							)							)
         # verify size
         UpperCAmelCase      					=  torch.tensor([8_0_0, 1_0_6_6]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,							__lowerCamelCase							)							)







import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

   UpperCAmelCase      					=  (
       ("""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_ )

   UpperCAmelCase      					=  model.state_dict()

   def to_tf_var_name(lowerCAmelCase_ ):
      for patt, repl in iter(lowerCAmelCase_ ):
         UpperCAmelCase      					=  name.replace(lowerCAmelCase_	,    lowerCAmelCase_ )
      return F"""bert/{name}"""

   def create_tf_var(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ ):
      UpperCAmelCase      					=  tf.dtypes.as_dtype(tensor.dtype )
      UpperCAmelCase      					=  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:
         UpperCAmelCase      					=  to_tf_var_name(lowerCAmelCase_ )
         UpperCAmelCase      					=  state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose ):
            UpperCAmelCase      					=  torch_tensor.T
         UpperCAmelCase      					=  create_tf_var(tensor=lowerCAmelCase_	,    name=lowerCAmelCase_	,    session=lowerCAmelCase_ )
         tf.keras.backend.set_value(lowerCAmelCase_	,    lowerCAmelCase_ )
         UpperCAmelCase      					=  session.run(lowerCAmelCase_ )
         print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_	,    lowerCAmelCase_ )}""" )

      UpperCAmelCase      					=  tf.train.Saver(tf.trainable_variables() )
      saver.save(lowerCAmelCase_	,    os.path.join(lowerCAmelCase_	,    model_name.replace("""-"""	,    """_""" ) + """.ckpt""" ) )
def 	_UpperCamelCase					(    lowerCAmelCase_=None )    ->Union[str, Any]:
   UpperCAmelCase      					=  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""" )
   UpperCAmelCase      					=  parser.parse_args(lowerCAmelCase_ )

   UpperCAmelCase      					=  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()



