





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Any:







   """simple docstring"""
   return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase="attention"					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]					)
   lowerCamelCase__       :						Tuple        =							k_tmp.reshape(k_tmp.shape[0]      ,  k_tmp.shape[1] * k_tmp.shape[2]					)
   lowerCamelCase__       :						Union[str, Any]        =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]					)
   lowerCamelCase__       :						Optional[int]        =							o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,  o_tmp.shape[2]					)
   lowerCamelCase__       :						List[Any]        =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]					)
   lowerCamelCase__       :						Any        =							q_tmp.reshape(q_tmp.shape[0]      ,  q_tmp.shape[1] * q_tmp.shape[2]					)
   lowerCamelCase__       :						Optional[Any]        =							np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]					)
   lowerCamelCase__       :						Any        =							v_tmp.reshape(v_tmp.shape[0]      ,  v_tmp.shape[1] * v_tmp.shape[2]					)
   return k, o, q, v



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   if split_mlp_wi:
      lowerCamelCase__       :						Tuple        =							params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
      lowerCamelCase__       :						str        =							params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
      lowerCamelCase__       :						Optional[int]        =							(wi_a, wi_a)
   else:
      lowerCamelCase__       :						int        =							params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

   lowerCamelCase__       :						Any        =							params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
   return wi, wo



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]



def 	_a						(   UpperCAmelCase      ,  *, UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							traverse_util.flatten_dict(variables['''target''']					)
   lowerCamelCase__       :						Any        =							{'''/'''.join(UpperCAmelCase					): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   lowerCamelCase__       :						Any        =							'''encoder/encoder/mlp/wi_0/kernel''' in old
   print('''Split MLP:'''      ,  UpperCAmelCase					)

   lowerCamelCase__       :						Any        =							collections.OrderedDict()

   # Shared embeddings.
   lowerCamelCase__       :						int        =							old['''token_embedder/embedding''']

   # Encoder.
   for i in range(UpperCAmelCase					):
      # Block i, layer 0 (Self Attention).
      lowerCamelCase__       :						str        =							tax_layer_norm_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''encoder'''      ,  '''pre_attention_layer_norm'''					)
      lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							tax_attention_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''encoder'''      ,  '''attention'''					)
      lowerCamelCase__       :						Dict        =							layer_norm
      lowerCamelCase__       :						List[str]        =							k.T
      lowerCamelCase__       :						str        =							o.T
      lowerCamelCase__       :						Union[str, Any]        =							q.T
      lowerCamelCase__       :						List[str]        =							v.T

      # Block i, layer 1 (MLP).
      lowerCamelCase__       :						Union[str, Any]        =							tax_layer_norm_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''encoder'''      ,  '''pre_mlp_layer_norm'''					)
      lowerCamelCase__       ,							lowerCamelCase__       :						str        =							tax_mlp_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''encoder'''      ,  UpperCAmelCase					)
      lowerCamelCase__       :						Any        =							layer_norm
      if split_mlp_wi:
         lowerCamelCase__       :						Optional[Any]        =							wi[0].T
         lowerCamelCase__       :						Any        =							wi[1].T
      else:
         lowerCamelCase__       :						Any        =							wi.T
      lowerCamelCase__       :						Dict        =							wo.T
      if scalable_attention:
         # convert the rel_embedding of each layer
         lowerCamelCase__       :						Dict        =							tax_relpos_bias_lookup(
             UpperCAmelCase      ,  UpperCAmelCase      ,  '''encoder'''					).T

   lowerCamelCase__       :						Tuple        =							old['''encoder/encoder_norm/scale''']

   if not scalable_attention:
      lowerCamelCase__       :						Optional[Any]        =							tax_relpos_bias_lookup(
          UpperCAmelCase      ,  0      ,  '''encoder'''					).T
      lowerCamelCase__       :						Optional[Any]        =							tax_relpos_bias_lookup(
          UpperCAmelCase      ,  0      ,  '''decoder'''					).T

   if not is_encoder_only:
      # Decoder.
      for i in range(UpperCAmelCase					):
         # Block i, layer 0 (Self Attention).
         lowerCamelCase__       :						Optional[int]        =							tax_layer_norm_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''      ,  '''pre_self_attention_layer_norm'''					)
         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							tax_attention_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''      ,  '''self_attention'''					)
         lowerCamelCase__       :						Optional[Any]        =							layer_norm
         lowerCamelCase__       :						List[Any]        =							k.T
         lowerCamelCase__       :						List[Any]        =							o.T
         lowerCamelCase__       :						Any        =							q.T
         lowerCamelCase__       :						List[str]        =							v.T

         # Block i, layer 1 (Cross Attention).
         lowerCamelCase__       :						Any        =							tax_layer_norm_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''      ,  '''pre_cross_attention_layer_norm'''					)
         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							tax_attention_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''      ,  '''encoder_decoder_attention'''					)
         lowerCamelCase__       :						Dict        =							layer_norm
         lowerCamelCase__       :						Optional[int]        =							k.T
         lowerCamelCase__       :						List[Any]        =							o.T
         lowerCamelCase__       :						str        =							q.T
         lowerCamelCase__       :						Dict        =							v.T

         # Block i, layer 2 (MLP).
         lowerCamelCase__       :						List[str]        =							tax_layer_norm_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''      ,  '''pre_mlp_layer_norm'''					)
         lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							tax_mlp_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''      ,  UpperCAmelCase					)
         lowerCamelCase__       :						int        =							layer_norm
         if split_mlp_wi:
            lowerCamelCase__       :						Dict        =							wi[0].T
            lowerCamelCase__       :						str        =							wi[1].T
         else:
            lowerCamelCase__       :						Dict        =							wi.T
         lowerCamelCase__       :						Union[str, Any]        =							wo.T

         if scalable_attention:
            # convert the rel_embedding of each layer
            lowerCamelCase__       :						Optional[int]        =							tax_relpos_bias_lookup(UpperCAmelCase      ,  UpperCAmelCase      ,  '''decoder'''					).T

      lowerCamelCase__       :						Tuple        =							old['''decoder/decoder_norm/scale''']

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         lowerCamelCase__       :						Optional[Any]        =							old['''decoder/logits_dense/kernel'''].T

   return new



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      lowerCamelCase__       :						str        =							state_dict['''shared.weight''']

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         lowerCamelCase__       :						Dict        =							state_dict['''shared.weight''']

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print('''Using shared word embeddings as lm_head.'''					)
         lowerCamelCase__       :						Any        =							state_dict['''shared.weight''']

   return state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						int        =							checkpoints.load_tax_checkpoint(UpperCAmelCase					)
   lowerCamelCase__       :						Dict        =							convert_tax_to_pytorch(
       UpperCAmelCase      ,  num_layers=config.num_layers      ,  is_encoder_only=UpperCAmelCase      ,  scalable_attention=UpperCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							make_state_dict(UpperCAmelCase      ,  UpperCAmelCase					)
   model.load_state_dict(UpperCAmelCase      ,  strict=UpperCAmelCase					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = False      ,  UpperCAmelCase = False      ,  )    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							MTaConfig.from_json_file(UpperCAmelCase					)
   print(f"Building PyTorch model from configuration: {config}"					)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      lowerCamelCase__       :						Any        =							UMTaEncoderModel(UpperCAmelCase					)
   else:
      lowerCamelCase__       :						Optional[Any]        =							UMTaForConditionalGeneration(UpperCAmelCase					)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)

   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"					)
   model.save_pretrained(UpperCAmelCase					)

   # Verify that we can load the checkpoint.
   model.from_pretrained(UpperCAmelCase					)
   print('''Done'''					)


if __name__ == "__main__":
       _A :   List[Any]  			=  argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
       # Required parameters
       parser.add_argument(
           '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
       )
       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.'
       )
       parser.add_argument(
           '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
       )
       parser.add_argument(
           '--scalable_attention',
           action='store_true',
           help='Whether the model uses scaled attention (umt5 model)',
           default=False,
       )
       _A :   Optional[Any]  			=  parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )










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 DetrImageProcessor


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def __init__(				self					:     Optional[int]		, A					:     Union[str, Any]		, A					:     Any=7		, A					:     Optional[int]=3		, A					:     Tuple=3_0		, A					:     List[Any]=4_0_0		, A					:     str=True		, A					:     Optional[int]=None		, A					:     Tuple=True		, A					:     Union[str, Any]=1 / 2_5_5		, A					:     Any=True		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     Optional[int]=[0.5, 0.5, 0.5]		, A					:     str=True		, )       ->List[Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          lowerCamelCase__       :						Union[str, Any]        =							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
          lowerCamelCase__       :						List[str]        =							parent
          lowerCamelCase__       :						Union[str, Any]        =							batch_size
          lowerCamelCase__       :						int        =							num_channels
          lowerCamelCase__       :						Optional[Any]        =							min_resolution
          lowerCamelCase__       :						List[Any]        =							max_resolution
          lowerCamelCase__       :						int        =							do_resize
          lowerCamelCase__       :						List[str]        =							size
          lowerCamelCase__       :						Union[str, Any]        =							do_rescale
          lowerCamelCase__       :						Optional[int]        =							rescale_factor
          lowerCamelCase__       :						List[str]        =							do_normalize
          lowerCamelCase__       :						Tuple        =							image_mean
          lowerCamelCase__       :						str        =							image_std
          lowerCamelCase__       :						List[Any]        =							do_pad







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_pad": self.do_pad,
          }







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     int		, A					:     List[str]=False      )       ->int:
          if not batched:
             lowerCamelCase__       :						Union[str, Any]        =							image_inputs[0]
             if isinstance(A		, Image.Image      ):
                lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							image.size
             else:
                lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							image.shape[1], image.shape[2]
             if w < h:
                lowerCamelCase__       :						List[Any]        =							int(self.size['''shortest_edge'''] * h / w      )
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
             elif w > h:
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Dict        =							int(self.size['''shortest_edge'''] * w / h      )
             else:
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Any        =							self.size['''shortest_edge''']

          else:
             lowerCamelCase__       :						Optional[Any]        =							[]
             for image in image_inputs:
                lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							self.get_expected_values([image]      )
                expected_values.append((expected_height, expected_width)      )
             lowerCamelCase__       :						Dict        =							max(A		, key=lambda A      : item[0]      )[0]
             lowerCamelCase__       :						Dict        =							max(A		, key=lambda A      : item[1]      )[1]

          return expected_height, expected_width


@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   DetrImageProcessor if is_vision_available() else None







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessingTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          return self.image_processor_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A		, '''image_mean'''      )      )
          self.assertTrue(hasattr(A		, '''image_std'''      )      )
          self.assertTrue(hasattr(A		, '''do_normalize'''      )      )
          self.assertTrue(hasattr(A		, '''do_rescale'''      )      )
          self.assertTrue(hasattr(A		, '''rescale_factor'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''do_pad'''      )      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[Any]        =							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		, A      )

          lowerCamelCase__       :						str        =							self.image_processing_class.from_dict(
              self.image_processor_dict		, size=4_2		, max_size=8_4		, pad_and_return_pixel_mask=A      )
          self.assertEqual(image_processor.size		, {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}      )
          self.assertEqual(image_processor.do_pad		, A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowerCamelCase__       :						str        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, Image.Image      )

          # Test not batched input
          lowerCamelCase__       :						Optional[Any]        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						Any        =							image_processing(A		, 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					:     List[str]      )       ->List[str]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowerCamelCase__       :						int        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A		, numpify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, np.ndarray      )

          # Test not batched input
          lowerCamelCase__       :						List[str]        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       :						List[Any]        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

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







       def 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          # Initialize image_processing
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						List[str]        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A		, torchify=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, torch.Tensor      )

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

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A      )

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

          # Test batched
          lowerCamelCase__       :						str        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

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







       @slow
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          # prepare image and target
          lowerCamelCase__       :						Union[str, Any]        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Union[str, Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						List[str]        =							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

          # encode them
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
          lowerCamelCase__       :						List[Any]        =							image_processing(images=A		, annotations=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[int]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						Any        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						Dict        =							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Any        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						str        =							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						str        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						List[str]        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						List[Any]        =							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A      )      )
          # verify orig_size
          lowerCamelCase__       :						str        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						str        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[str]:
          # prepare image, target and masks_path
          lowerCamelCase__       :						Any        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Optional[Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						Union[str, Any]        =							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

          lowerCamelCase__       :						List[Any]        =							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

          # encode them
          lowerCamelCase__       :						Any        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
          lowerCamelCase__       :						Tuple        =							image_processing(images=A		, annotations=A		, masks_path=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						str        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						int        =							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						Optional[Any]        =							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						int        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						Dict        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						int        =							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A      )      )
          # verify masks
          lowerCamelCase__       :						Union[str, Any]        =							8_2_2_8_7_3
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, A      )
          # verify orig_size
          lowerCamelCase__       :						List[Any]        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						Tuple        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )



