




"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   __snake_case		=       '''platform'''

   import jax
   import jax.numpy as jnp

   from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict=None,     _lowerCAmelCase     :					Union[str, Any]=None,     _lowerCAmelCase     :					int=None,     _lowerCAmelCase     :					List[str]=None,     _lowerCAmelCase     :					List[Any]=None,     _lowerCAmelCase     :					str=None,     ):






     """simple docstring"""

     if attention_mask is None:
          _a    	=				np.where(input_ids != config.pad_token_id,     1,     0       )
     if decoder_attention_mask is None:
          _a    	=				np.where(decoder_input_ids != config.pad_token_id,     1,     0       )
     if head_mask is None:
          _a    	=				np.ones((config.encoder_layers, config.encoder_attention_heads)       )
     if decoder_head_mask is None:
          _a    	=				np.ones((config.decoder_layers, config.decoder_attention_heads)       )
     if cross_attn_head_mask is None:
          _a    	=				np.ones((config.decoder_layers, config.decoder_attention_heads)       )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": attention_mask,
     }


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=13    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase=99    ,				__UpperCAmelCase=16    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase=4    ,				__UpperCAmelCase="gelu"    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=0.1    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=2    ,				__UpperCAmelCase=1    ,				__UpperCAmelCase=0    ,				__UpperCAmelCase=0.02    ,				)       ->							Tuple:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				seq_length
          _a    	=				is_training
          _a    	=				use_labels
          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				intermediate_size
          _a    	=				hidden_act
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				max_position_embeddings
          _a    	=				eos_token_id
          _a    	=				pad_token_id
          _a    	=				bos_token_id
          _a    	=				initializer_range


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,				self.vocab_size							)    ,				3    ,				self.vocab_size							)
          _a    	=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,				dtype=np.intaa							))    ,				-1							)

          _a    	=				shift_tokens_right(__UpperCAmelCase    ,				1    ,				2							)

          _a    	=				BlenderbotSmallConfig(
              vocab_size=self.vocab_size    ,				d_model=self.hidden_size    ,				encoder_layers=self.num_hidden_layers    ,				decoder_layers=self.num_hidden_layers    ,				encoder_attention_heads=self.num_attention_heads    ,				decoder_attention_heads=self.num_attention_heads    ,				encoder_ffn_dim=self.intermediate_size    ,				decoder_ffn_dim=self.intermediate_size    ,				dropout=self.hidden_dropout_prob    ,				attention_dropout=self.attention_probs_dropout_prob    ,				max_position_embeddings=self.max_position_embeddings    ,				eos_token_id=self.eos_token_id    ,				bos_token_id=self.bos_token_id    ,				pad_token_id=self.pad_token_id    ,				initializer_range=self.initializer_range    ,				use_cache=__UpperCAmelCase    ,				)
          _a    	=				prepare_blenderbot_inputs_dict(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)
          return config, inputs_dict


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a	,      _a    	=				self.prepare_config_and_inputs()
          return config, inputs_dict


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
          _a    	=				20
          _a    	=				model_class_name(__UpperCAmelCase							)

          _a    	=				model.encode(inputs_dict['''input_ids''']							)

          _a	,      _a    	=				(
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          _a    	=				model.init_cache(decoder_input_ids.shape[0]    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,				dtype='''i4'''							)

          _a    	=				jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]    ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,				)
          _a    	=				model.decode(
              decoder_input_ids[:, :-1]    ,				__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase    ,				past_key_values=__UpperCAmelCase    ,				decoder_position_ids=__UpperCAmelCase    ,				)

          _a    	=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,				dtype='''i4'''							)
          _a    	=				model.decode(
              decoder_input_ids[:, -1:]    ,				__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase    ,				past_key_values=outputs_cache.past_key_values    ,				decoder_position_ids=__UpperCAmelCase    ,				)

          _a    	=				model.decode(__UpperCAmelCase    ,				__UpperCAmelCase							)

          _a    	=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
          self.parent.assertTrue(diff < 1e-3    ,				msg=F'Max diff is {diff}'							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							str:
          _a    	=				20
          _a    	=				model_class_name(__UpperCAmelCase							)

          _a    	=				model.encode(inputs_dict['''input_ids''']							)

          _a	,      _a    	=				(
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          _a    	=				jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
              ]    ,				axis=-1    ,				)

          _a    	=				model.init_cache(decoder_input_ids.shape[0]    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)
          _a    	=				jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]    ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,				)

          _a    	=				model.decode(
              decoder_input_ids[:, :-1]    ,				__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase    ,				past_key_values=__UpperCAmelCase    ,				decoder_position_ids=__UpperCAmelCase    ,				)
          _a    	=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,				dtype='''i4'''							)
          _a    	=				model.decode(
              decoder_input_ids[:, -1:]    ,				__UpperCAmelCase    ,				past_key_values=outputs_cache.past_key_values    ,				decoder_attention_mask=__UpperCAmelCase    ,				decoder_position_ids=__UpperCAmelCase    ,				)

          _a    	=				model.decode(__UpperCAmelCase    ,				__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase							)

          _a    	=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
          self.parent.assertTrue(diff < 1e-3    ,				msg=F'Max diff is {diff}'							)







@require_flax
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Optional[int]    =		99


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ]    ,				dtype=np.intaa    ,				)

          _a    	=				input_ids.shape[0]
          _a    	=				BlenderbotSmallConfig(
              vocab_size=self.vocab_size    ,				d_model=24    ,				encoder_layers=2    ,				decoder_layers=2    ,				encoder_attention_heads=2    ,				decoder_attention_heads=2    ,				encoder_ffn_dim=32    ,				decoder_ffn_dim=32    ,				max_position_embeddings=48    ,				eos_token_id=2    ,				pad_token_id=1    ,				bos_token_id=0    ,				)
          return config, input_ids, batch_size


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a	,      _a	,      _a    	=				self._get_config_and_data()
          _a    	=				FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
          _a    	=				lm_model(input_ids=__UpperCAmelCase							)
          _a    	=				(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				BlenderbotSmallConfig(
              vocab_size=self.vocab_size    ,				d_model=14    ,				encoder_layers=2    ,				decoder_layers=2    ,				encoder_attention_heads=2    ,				decoder_attention_heads=2    ,				encoder_ffn_dim=8    ,				decoder_ffn_dim=8    ,				max_position_embeddings=48    ,				)
          _a    	=				FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
          _a    	=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,				dtype=np.intaa							)
          _a    	=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,				dtype=np.intaa							)
          _a    	=				lm_model(input_ids=__UpperCAmelCase    ,				decoder_input_ids=__UpperCAmelCase							)
          _a    	=				(*summary.shape, config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape    ,				__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,				dtype=np.intaa							)
          _a    	=				shift_tokens_right(__UpperCAmelCase    ,				1    ,				2							)
          _a    	=				np.equal(__UpperCAmelCase    ,				1							).astype(np.floataa							).sum()
          _a    	=				np.equal(__UpperCAmelCase    ,				1							).astype(np.floataa							).sum()
          self.assertEqual(shifted.shape    ,				input_ids.shape							)
          self.assertEqual(__UpperCAmelCase    ,				n_pad_before - 1							)
          self.assertTrue(np.equal(shifted[:, 0]    ,				2							).all()							)







@require_flax
class     __lowerCamelCase  (					a__  ,					unittest.TestCase  ,					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		True
     A_       :	List[Any]    =		(
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     A_       :	List[str]    =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				FlaxBlenderbotSmallModelTester(self							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                    _a    	=				self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)
                    _a    	=				model_class(__UpperCAmelCase							)

                    @jax.jit
                    def encode_jitted(__UpperCAmelCase    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase							):
                         return model.encode(input_ids=__UpperCAmelCase    ,				attention_mask=__UpperCAmelCase							)

                    with self.subTest('''JIT Enabled'''							):
                         _a    	=				encode_jitted(**__UpperCAmelCase							).to_tuple()

                    with self.subTest('''JIT Disabled'''							):
                         with jax.disable_jit():
                              _a    	=				encode_jitted(**__UpperCAmelCase							).to_tuple()

                    self.assertEqual(len(__UpperCAmelCase							)    ,				len(__UpperCAmelCase							)							)
                    for jitted_output, output in zip(__UpperCAmelCase    ,				__UpperCAmelCase							):
                         self.assertEqual(jitted_output.shape    ,				output.shape							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                    _a    	=				model_class(__UpperCAmelCase							)
                    _a    	=				model.encode(inputs_dict['''input_ids''']    ,				inputs_dict['''attention_mask''']							)

                    _a    	=				{
                        '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                        '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                        '''encoder_outputs''': encoder_outputs,
                    }

                    @jax.jit
                    def decode_jitted(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							):
                         return model.decode(
                             decoder_input_ids=__UpperCAmelCase    ,				decoder_attention_mask=__UpperCAmelCase    ,				encoder_outputs=__UpperCAmelCase    ,				)

                    with self.subTest('''JIT Enabled'''							):
                         _a    	=				decode_jitted(**__UpperCAmelCase							).to_tuple()

                    with self.subTest('''JIT Disabled'''							):
                         with jax.disable_jit():
                              _a    	=				decode_jitted(**__UpperCAmelCase							).to_tuple()

                    self.assertEqual(len(__UpperCAmelCase							)    ,				len(__UpperCAmelCase							)							)
                    for jitted_output, output in zip(__UpperCAmelCase    ,				__UpperCAmelCase							):
                         self.assertEqual(jitted_output.shape    ,				output.shape							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							int:
          for model_class_name in self.all_model_classes:
               _a    	=				model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''							)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _a    	=				np.ones((1, 1)							) * model.config.eos_token_id
               _a    	=				model(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)



"""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 DeformableDetrImageProcessor


class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=7    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=30    ,				__UpperCAmelCase=400    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=[0.5, 0.5, 0.5]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=1 / 255    ,				__UpperCAmelCase=True    ,				)       ->							Optional[int]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _a    	=				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          _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 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=False							)       ->							List[str]:
          if not batched:
               _a    	=				image_inputs[0]
               if isinstance(__UpperCAmelCase    ,				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(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase							: item[0]							)[0]
               _a    	=				max(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase							: item[1]							)[1]

          return expected_height, expected_width







@require_torch
@require_vision
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	List[str]    =		DeformableDetrImageProcessor if is_vision_available() else None


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				DeformableDetrImageProcessingTester(self							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''image_mean'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''image_std'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_normalize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_resize'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_rescale'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''do_pad'''							)							)
          self.assertTrue(hasattr(__UpperCAmelCase    ,				'''size'''							)							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size    ,				{'''shortest_edge''': 18, '''longest_edge''': 1333}							)
          self.assertEqual(image_processor.do_pad    ,				__UpperCAmelCase							)

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


     def 							_UpperCAmelCase      (      self							)       ->							int:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				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(__UpperCAmelCase							)

          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(__UpperCAmelCase    ,				batched=__UpperCAmelCase							)

          _a    	=				image_processing(__UpperCAmelCase    ,				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 							_UpperCAmelCase      (      self							)       ->							int:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase    ,				numpify=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				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(__UpperCAmelCase							)

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

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

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

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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          # Initialize image_processing
          _a    	=				self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          _a    	=				prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=__UpperCAmelCase    ,				torchify=__UpperCAmelCase							)
          for image in image_inputs:
               self.assertIsInstance(__UpperCAmelCase    ,				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(__UpperCAmelCase							)

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

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

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

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


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          # prepare image and target
          _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''': 39769, '''annotations''': target}

          # encode them
          _a    	=				DeformableDetrImageProcessor()
          _a    	=				image_processing(images=__UpperCAmelCase    ,				annotations=__UpperCAmelCase    ,				return_tensors='''pt'''							)

          # verify pixel values
          _a    	=				torch.Size([1, 3, 800, 1066]							)
          self.assertEqual(encoding['''pixel_values'''].shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor([0.2796, 0.3138, 0.3481]							)
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)

          # verify area
          _a    	=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				__UpperCAmelCase							)							)
          # verify boxes
          _a    	=				torch.Size([6, 4]							)
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				__UpperCAmelCase							)
          _a    	=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,				__UpperCAmelCase    ,				atol=1e-3							)							)
          # verify image_id
          _a    	=				torch.tensor([39769]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				__UpperCAmelCase							)							)
          # verify is_crowd
          _a    	=				torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				__UpperCAmelCase							)							)
          # verify class_labels
          _a    	=				torch.tensor([75, 75, 63, 65, 17, 17]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				__UpperCAmelCase							)							)
          # verify orig_size
          _a    	=				torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				__UpperCAmelCase							)							)
          # verify size
          _a    	=				torch.tensor([800, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							str:
          # prepare image, target and masks_path
          _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''': 39769, '''segments_info''': target}

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

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

          # verify pixel values
          _a    	=				torch.Size([1, 3, 800, 1066]							)
          self.assertEqual(encoding['''pixel_values'''].shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor([0.2796, 0.3138, 0.3481]							)
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)

          # verify area
          _a    	=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,				__UpperCAmelCase							)							)
          # verify boxes
          _a    	=				torch.Size([6, 4]							)
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,				__UpperCAmelCase							)
          _a    	=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,				__UpperCAmelCase    ,				atol=1e-3							)							)
          # verify image_id
          _a    	=				torch.tensor([39769]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,				__UpperCAmelCase							)							)
          # verify is_crowd
          _a    	=				torch.tensor([0, 0, 0, 0, 0, 0]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,				__UpperCAmelCase							)							)
          # verify class_labels
          _a    	=				torch.tensor([17, 17, 63, 75, 75, 93]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,				__UpperCAmelCase							)							)
          # verify masks
          _a    	=				822873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,				__UpperCAmelCase							)
          # verify orig_size
          _a    	=				torch.tensor([480, 640]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,				__UpperCAmelCase							)							)
          # verify size
          _a    	=				torch.tensor([800, 1066]							)
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,				__UpperCAmelCase							)							)