




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
     from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	):
  if attention_mask is None:
    lowercase			:int							=  input_ids.ne(config.pad_token_id      )
  if decoder_attention_mask is None:
    lowercase			:List[str]							=  decoder_input_ids.ne(config.pad_token_id      )
  if head_mask is None:
    lowercase			:Optional[int]							=  torch.ones(config.encoder_layers,	config.encoder_attention_heads,	device=lowerCamelCase      )
  if decoder_head_mask is None:
    lowercase			:Optional[Any]							=  torch.ones(config.decoder_layers,	config.decoder_attention_heads,	device=lowerCamelCase      )
  if cross_attn_head_mask is None:
    lowercase			:List[Any]							=  torch.ones(config.decoder_layers,	config.decoder_attention_heads,	device=lowerCamelCase      )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }




class       __lowerCAmelCase				:






       def __init__(      self:    int				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Optional[Any]=7				,    _lowerCAmelCase:    str=True				,    _lowerCAmelCase:    Optional[int]=False				,    _lowerCAmelCase:    int=99				,    _lowerCAmelCase:    Any=16				,    _lowerCAmelCase:    List[Any]=2				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    List[Any]=4				,    _lowerCAmelCase:    List[str]="relu"				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Union[str, Any]=0.1				,    _lowerCAmelCase:    Union[str, Any]=0.0				,    _lowerCAmelCase:    Tuple=0.0				,    _lowerCAmelCase:    Optional[Any]=20				,    _lowerCAmelCase:    Tuple=2				,    _lowerCAmelCase:    List[Any]=1				,    _lowerCAmelCase:    str=0				,    ):
         lowercase			:int							=  parent
         lowercase			:Any							=  batch_size
         lowercase			:Optional[Any]							=  seq_length
         lowercase			:int							=  is_training
         lowercase			:Optional[Any]							=  use_labels
         lowercase			:List[Any]							=  vocab_size
         lowercase			:str							=  hidden_size
         lowercase			:str							=  num_hidden_layers
         lowercase			:List[str]							=  num_attention_heads
         lowercase			:Optional[Any]							=  intermediate_size
         lowercase			:int							=  hidden_act
         lowercase			:Optional[int]							=  hidden_dropout_prob
         lowercase			:Dict							=  attention_probs_dropout_prob
         lowercase			:Tuple							=  encoder_layerdrop
         lowercase			:Dict							=  decoder_layerdrop
         lowercase			:Optional[Any]							=  max_position_embeddings
         lowercase			:Any							=  eos_token_id
         lowercase			:Optional[Any]							=  pad_token_id
         lowercase			:Dict							=  bos_token_id






       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         lowercase			:int							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )
         lowercase			:Dict							=  self.eos_token_id  # Eos Token
         lowercase			:Optional[Any]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

         # we need to clamp the input ids here to avoid having pad token in between
         # this is because for M2M100 the position_ids are prepared such that
         # all pad tokens have pos id = 2 and rest are between 2..seq_length
         # and the seq_length here is seq_length - num_pad_tokens
         # but when using past, there is no way of knowing if the past input ids had
         # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
         # position_ids being off by num_pad_tokens in past input
         lowercase			:List[str]							=  input_ids.clamp(self.pad_token_id + 1 )
         lowercase			:Any							=  decoder_input_ids.clamp(self.pad_token_id + 1 )

         lowercase			:Optional[int]							=  self.get_config()
         lowercase			:Union[str, Any]							=  prepare_mam_aaa_inputs_dict(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )
         return config, inputs_dict






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         return MaMaaaConfig(
             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				,    encoder_layerdrop=self.encoder_layerdrop				,    decoder_layerdrop=self.decoder_layerdrop				,    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				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase			:List[str]							=  self.prepare_config_and_inputs()
         return config, inputs_dict






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    Optional[Any] ):
         lowercase			:Union[str, Any]							=  MaMaaaModel(config=_lowerCAmelCase ).get_decoder().to(_lowerCAmelCase ).eval()
         lowercase			:Dict							=  inputs_dict["input_ids"]
         lowercase			:List[Any]							=  inputs_dict["attention_mask"]
         lowercase			:Tuple							=  inputs_dict["head_mask"]

         # first forward pass
         lowercase			:Any							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    head_mask=_lowerCAmelCase				,    use_cache=_lowerCAmelCase )

         lowercase		,			lowercase			:Tuple							=  outputs.to_tuple()

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase			:Tuple							=  ids_tensor((self.batch_size, 3)				,    config.vocab_size )
         lowercase			:List[Any]							=  ids_tensor((self.batch_size, 3)				,    2 )

         # append to next input_ids and
         lowercase			:List[Any]							=  torch.cat([input_ids, next_tokens]				,    dim=-1 )
         lowercase			:Optional[int]							=  torch.cat([attention_mask, next_attn_mask]				,    dim=-1 )

         lowercase			:List[Any]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )["last_hidden_state"]
         lowercase			:List[str]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    past_key_values=_lowerCAmelCase )[
             "last_hidden_state"
         ]

         # select random slice
         lowercase			:str							=  ids_tensor((1,)				,    output_from_past.shape[-1] ).item()
         lowercase			:Tuple							=  output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase			:Union[str, Any]							=  output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(_lowerCAmelCase				,    _lowerCAmelCase				,    atol=1e-2 ) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    Any ):
         lowercase			:Optional[Any]							=  MaMaaaModel(config=_lowerCAmelCase ).to(_lowerCAmelCase ).eval()
         lowercase			:str							=  model(**_lowerCAmelCase )

         lowercase			:int							=  outputs.encoder_last_hidden_state
         lowercase			:Dict							=  outputs.last_hidden_state

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase			:Any							=  model.get_encoder()
           encoder.save_pretrained(_lowerCAmelCase )
           lowercase			:List[Any]							=  MaMaaaEncoder.from_pretrained(_lowerCAmelCase ).to(_lowerCAmelCase )

         lowercase			:Optional[Any]							=  encoder(inputs_dict["input_ids"]				,    attention_mask=inputs_dict["attention_mask"] )[
             0
         ]

         self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase			:Union[str, Any]							=  model.get_decoder()
           decoder.save_pretrained(_lowerCAmelCase )
           lowercase			:Any							=  MaMaaaDecoder.from_pretrained(_lowerCAmelCase ).to(_lowerCAmelCase )

         lowercase			:Any							=  decoder(
             input_ids=inputs_dict["decoder_input_ids"]				,    attention_mask=inputs_dict["decoder_attention_mask"]				,    encoder_hidden_states=_lowerCAmelCase				,    encoder_attention_mask=inputs_dict["attention_mask"]				,    )[0]

         self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )







@require_torch
class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       unittest.TestCase):
       _a           =  (
           (
               MaMaaaModel,
               MaMaaaForConditionalGeneration,
           )
           if is_torch_available()
           else ()
       )
       _a           =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
       _a           =  (
           {
               '''conversational''': MaMaaaForConditionalGeneration,
               '''feature-extraction''': MaMaaaModel,
               '''summarization''': MaMaaaForConditionalGeneration,
               '''text2text-generation''': MaMaaaForConditionalGeneration,
               '''translation''': MaMaaaForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       _a           =  True
       _a           =  True
       _a           =  False
       _a           =  False






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    Optional[int]				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[int] ):
         if pipeline_test_casse_name == "TranslationPipelineTests":
           # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
           # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
           return True

         return False






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase			:List[Any]							=  MaMaaaModelTester(self )
         lowercase			:Optional[int]							=  ConfigTester(self				,    config_class=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase			:Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
           lowercase			:Dict							=  model_class(_lowerCAmelCase )

           with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(_lowerCAmelCase )
             lowercase		,			lowercase			:str							=  model_class.from_pretrained(_lowerCAmelCase				,    output_loading_info=_lowerCAmelCase )
           self.assertEqual(info["missing_keys"]				,    [] )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:List[str]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         lowercase			:str							=  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase		,			lowercase			:int							=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
           lowercase			:Union[str, Any]							=  model_class(_lowerCAmelCase )
           model.to(_lowerCAmelCase )
           model.eval()

           lowercase			:Optional[int]							=  copy.deepcopy(self._prepare_for_class(_lowerCAmelCase				,    _lowerCAmelCase ) )

           if not self.is_encoder_decoder:
             lowercase			:List[str]							=  inputs["input_ids"]
             del inputs["input_ids"]
           else:
             lowercase			:Optional[int]							=  inputs["input_ids"]
             lowercase			:List[str]							=  inputs.get("decoder_input_ids"				,    _lowerCAmelCase )
             del inputs["input_ids"]
             inputs.pop("decoder_input_ids"				,    _lowerCAmelCase )

           lowercase			:Optional[int]							=  model.get_input_embeddings()
           if not self.is_encoder_decoder:
             lowercase			:Tuple							=  wte(_lowerCAmelCase )
           else:
             lowercase			:Tuple							=  wte(_lowerCAmelCase )
             lowercase			:Dict							=  wte(_lowerCAmelCase )

           with torch.no_grad():
             model(**_lowerCAmelCase )[0]






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase		,			lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         lowercase			:Optional[int]							=  input_dict["input_ids"]
         lowercase			:str							=  input_ids.ne(1 ).to(_lowerCAmelCase )
         lowercase			:int							=  MaMaaaForConditionalGeneration(_lowerCAmelCase ).eval().to(_lowerCAmelCase )
         if torch_device == "cuda":
           model.half()
         model.generate(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         model.generate(num_beams=4				,    do_sample=_lowerCAmelCase				,    early_stopping=_lowerCAmelCase				,    num_return_sequences=3 )







def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return torch.tensor(lowerCamelCase,	dtype=torch.long,	device=lowerCamelCase      )


_UpperCAmelCase		:  int	=   1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class       __lowerCAmelCase				(							unittest.TestCase):






       @cached_property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:List[str]							=  MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(_lowerCAmelCase )
         lowercase			:List[str]							=  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
         lowercase			:Tuple							=  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
         lowercase			:str							=  prepare_mam_aaa_inputs_dict(model.config				,    _lowerCAmelCase				,    _lowerCAmelCase )
         with torch.no_grad():
           lowercase			:Tuple							=  model(**_lowerCAmelCase )[0]
         lowercase			:List[str]							=  torch.Size((1, 11, 10_24) )
         self.assertEqual(output.shape				,    _lowerCAmelCase )
         # change to expected output here
         lowercase			:Tuple							=  torch.tensor(
             [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]				,    device=_lowerCAmelCase )
         self.assertTrue(torch.allclose(output[:, :3, :3]				,    _lowerCAmelCase				,    atol=_lowerCAmelCase ) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:str							=  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(_lowerCAmelCase )

         # change to intended input
         lowercase			:Tuple							=  _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]] )
         lowercase			:Optional[Any]							=  _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]] )
         lowercase			:Any							=  prepare_mam_aaa_inputs_dict(model.config				,    _lowerCAmelCase				,    _lowerCAmelCase )
         with torch.no_grad():
           lowercase			:List[Any]							=  model(**_lowerCAmelCase )[0]
         lowercase			:Union[str, Any]							=  torch.Size((1, 11, model.config.vocab_size) )
         self.assertEqual(output.shape				,    _lowerCAmelCase )
         # change to expected output here
         lowercase			:Union[str, Any]							=  torch.tensor(
             [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]				,    device=_lowerCAmelCase )
         self.assertTrue(torch.allclose(output[:, :3, :3]				,    _lowerCAmelCase				,    atol=_lowerCAmelCase ) )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:List[str]							=  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(_lowerCAmelCase )
         lowercase			:List[Any]							=  MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"				,    src_lang="fr"				,    tgt_lang="en" )

         lowercase			:Tuple							=  [
             "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
             "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
             "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
             " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
             " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
         ]

         # The below article tests that we don't add any hypotheses outside of the top n_beams
         lowercase			:str							=  tokenizer(_lowerCAmelCase				,    padding=_lowerCAmelCase				,    return_tensors="pt" )

         lowercase			:Optional[int]							=  model.generate(
             input_ids=dct["input_ids"].to(_lowerCAmelCase )				,    attention_mask=dct["attention_mask"].to(_lowerCAmelCase )				,    num_beams=5				,    forced_bos_token_id=tokenizer.get_lang_id("en" )				,    )

         lowercase			:Dict							=  [
             "The NSA case highlights the total absence of intelligence debate",
             "I think there are two levels of response from the French government.",
             "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
             " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
             " communications in France.",
         ]

         lowercase			:Union[str, Any]							=  tokenizer.batch_decode(
             hypotheses_batch.tolist()				,    clean_up_tokenization_spaces=_lowerCAmelCase				,    skip_special_tokens=_lowerCAmelCase )
         assert generated == expected_en









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 GLPNImageProcessor




class       __lowerCAmelCase				(							unittest.TestCase):






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Optional[Any]=3				,    _lowerCAmelCase:    List[Any]=18				,    _lowerCAmelCase:    int=30				,    _lowerCAmelCase:    Union[str, Any]=4_00				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    Union[str, Any]=32				,    _lowerCAmelCase:    int=True				,    ):
         lowercase			:Any							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:Any							=  num_channels
         lowercase			:Optional[Any]							=  image_size
         lowercase			:Optional[Any]							=  min_resolution
         lowercase			:str							=  max_resolution
         lowercase			:str							=  do_resize
         lowercase			:Optional[int]							=  size_divisor
         lowercase			:Tuple							=  do_rescale






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







@require_torch
@require_vision
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  GLPNImageProcessor if is_vision_available() else None






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:int							=  GLPNImageProcessingTester(self )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return self.image_processor_tester.prepare_image_processor_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_resize" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "size_divisor" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "resample" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_rescale" ) )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         pass






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    Image.Image )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:str							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowercase			:Union[str, Any]							=  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 (GLPNImageProcessor doesn't support batching)
         lowercase			:Optional[int]							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         # Initialize image_processing
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowercase			:List[str]							=  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 (GLPNImageProcessor doesn't support batching)
         lowercase			:Dict							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )



