




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
     import torch

class     __a:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=14  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=99  ,_SCREAMING_SNAKE_CASE=32  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=37  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=0.02  ,)					->						Optional[int]:
         UpperCAmelCase_     :				Union[str, Any]  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				List[str]  =     seq_length
         UpperCAmelCase_     :				Union[str, Any]  =     is_training
         UpperCAmelCase_     :				Dict  =     use_input_mask
         UpperCAmelCase_     :				Optional[Any]  =     use_token_type_ids
         UpperCAmelCase_     :				Tuple  =     use_labels
         UpperCAmelCase_     :				List[Any]  =     vocab_size
         UpperCAmelCase_     :				List[str]  =     hidden_size
         UpperCAmelCase_     :				Optional[int]  =     rotary_dim
         UpperCAmelCase_     :				Optional[int]  =     num_hidden_layers
         UpperCAmelCase_     :				Any  =     num_attention_heads
         UpperCAmelCase_     :				List[str]  =     intermediate_size
         UpperCAmelCase_     :				Optional[Any]  =     hidden_act
         UpperCAmelCase_     :				int  =     hidden_dropout_prob
         UpperCAmelCase_     :				int  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				List[str]  =     max_position_embeddings
         UpperCAmelCase_     :				List[Any]  =     initializer_range
         UpperCAmelCase_     :				str  =     None
         UpperCAmelCase_     :				Any  =     vocab_size - 1
         UpperCAmelCase_     :				Optional[int]  =     vocab_size - 1
         UpperCAmelCase_     :				Optional[int]  =     vocab_size - 1





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size    )

         UpperCAmelCase_     :				Any  =     None
         if self.use_input_mask:
              UpperCAmelCase_     :				Dict  =     random_attention_mask([self.batch_size, self.seq_length]    )

         UpperCAmelCase_     :				List[Any]  =     GPTJConfig(
             vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,use_cache=_SCREAMING_SNAKE_CASE  ,bos_token_id=self.bos_token_id  ,eos_token_id=self.eos_token_id  ,pad_token_id=self.pad_token_id  ,rotary_dim=self.rotary_dim  ,)

         return (config, input_ids, input_mask)





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     self.prepare_config_and_inputs()
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     config_and_inputs
         UpperCAmelCase_     :				Optional[Any]  =     {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				Any  =     20
         UpperCAmelCase_     :				List[Any]  =     model_class_name(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Any  =     model.init_cache(input_ids.shape[0]  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     jnp.ones((input_ids.shape[0], max_decoder_length)  ,dtype='''i4'''    )

         UpperCAmelCase_     :				List[Any]  =     jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1    )[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)    )
         UpperCAmelCase_     :				Optional[Any]  =     model(
             input_ids[:, :-1]  ,attention_mask=_SCREAMING_SNAKE_CASE  ,past_key_values=_SCREAMING_SNAKE_CASE  ,position_ids=_SCREAMING_SNAKE_CASE  ,)

         UpperCAmelCase_     :				Dict  =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype='''i4'''    )
         UpperCAmelCase_     :				Dict  =     model(
             input_ids[:, -1:]  ,attention_mask=_SCREAMING_SNAKE_CASE  ,past_key_values=outputs_cache.past_key_values  ,position_ids=_SCREAMING_SNAKE_CASE  ,)

         UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						List[Any]:
         UpperCAmelCase_     :				Dict  =     20
         UpperCAmelCase_     :				Tuple  =     model_class_name(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])    )]  ,axis=-1  ,)

         UpperCAmelCase_     :				List[str]  =     model.init_cache(input_ids.shape[0]  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1    )[None, :]  ,(input_ids.shape[0], input_ids.shape[-1] - 1)    )

         UpperCAmelCase_     :				str  =     model(
             input_ids[:, :-1]  ,attention_mask=_SCREAMING_SNAKE_CASE  ,past_key_values=_SCREAMING_SNAKE_CASE  ,position_ids=_SCREAMING_SNAKE_CASE  ,)
         UpperCAmelCase_     :				List[Any]  =     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,dtype='''i4'''    )
         UpperCAmelCase_     :				Tuple  =     model(
             input_ids[:, -1:]  ,past_key_values=outputs_cache.past_key_values  ,attention_mask=_SCREAMING_SNAKE_CASE  ,position_ids=_SCREAMING_SNAKE_CASE  ,)

         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE  ,attention_mask=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Union[str, Any]  =     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     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    lowerCAmelCase         = (FlaxGPTJForCausalLM,) if is_flax_available() else ()





    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Tuple  =     FlaxGPTJModelTester(self    )





    def 				a__ ( self    )					->						Optional[Any]:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Dict:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  _SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    @tooslow
    def 				a__ ( self    )					->						Optional[Any]:
         UpperCAmelCase_     :				Optional[Any]  =     GPTaTokenizer.from_pretrained('''gpt2'''  ,pad_token='''<|endoftext|>'''  ,padding_side='''left'''    )
         UpperCAmelCase_     :				Any  =     tokenizer(['''Hello this is a long string''', '''Hey''']  ,return_tensors='''np'''  ,padding=_SCREAMING_SNAKE_CASE  ,truncation=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''    )
         UpperCAmelCase_     :				Optional[Any]  =     False
         UpperCAmelCase_     :				Union[str, Any]  =     model.config.eos_token_id

         UpperCAmelCase_     :				Optional[int]  =     jax.jit(model.generate    )

         UpperCAmelCase_     :				Any  =     jit_generate(
             inputs['''input_ids''']  ,attention_mask=inputs['''attention_mask''']  ,pad_token_id=tokenizer.pad_token_id    ).sequences

         UpperCAmelCase_     :				int  =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  ,skip_special_tokens=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Union[str, Any]  =     [
             '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
             '''Hey, I\'m a little late to the party. I\'m going to''',
         ]

         self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    @is_pt_flax_cross_test
    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   # prepare inputs
                   UpperCAmelCase_     :				Dict  =     self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Optional[Any]  =     {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   UpperCAmelCase_     :				List[Any]  =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   UpperCAmelCase_     :				Tuple  =     getattr(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

                   UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     pt_inputs['''input_ids'''].shape
                   UpperCAmelCase_     :				Dict  =     np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)    )
                   for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE    ):
                        UpperCAmelCase_     :				Union[str, Any]  =     0
                        UpperCAmelCase_     :				int  =     1
                        UpperCAmelCase_     :				Dict  =     0
                        UpperCAmelCase_     :				List[Any]  =     1
                   UpperCAmelCase_     :				Optional[int]  =     pt_model_class(_SCREAMING_SNAKE_CASE    ).eval()
                   UpperCAmelCase_     :				str  =     model_class(_SCREAMING_SNAKE_CASE  ,dtype=jnp.floataa    )

                   UpperCAmelCase_     :				Optional[Any]  =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				str  =     fx_state

                   with torch.no_grad():
                        UpperCAmelCase_     :				Tuple  =     pt_model(**_SCREAMING_SNAKE_CASE    ).to_tuple()

                   UpperCAmelCase_     :				int  =     fx_model(**_SCREAMING_SNAKE_CASE    ).to_tuple()
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,len(_SCREAMING_SNAKE_CASE    )  ,'''Output lengths differ between Flax and PyTorch'''    )
                   for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                        self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2    )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                        pt_model.save_pretrained(_SCREAMING_SNAKE_CASE    )
                        UpperCAmelCase_     :				Optional[int]  =     model_class.from_pretrained(_SCREAMING_SNAKE_CASE  ,from_pt=_SCREAMING_SNAKE_CASE    )

                   UpperCAmelCase_     :				Any  =     fx_model_loaded(**_SCREAMING_SNAKE_CASE    ).to_tuple()
                   self.assertEqual(
                       len(_SCREAMING_SNAKE_CASE    )  ,len(_SCREAMING_SNAKE_CASE    )  ,'''Output lengths differ between Flax and PyTorch'''    )
                   for fx_output_loaded, pt_output in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                        self.assert_almost_equals(fx_output_loaded[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2    )





    @is_pt_flax_cross_test
    def 				a__ ( self    )					->						str:
         UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   # prepare inputs
                   UpperCAmelCase_     :				Union[str, Any]  =     self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Dict  =     {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   UpperCAmelCase_     :				Optional[int]  =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   UpperCAmelCase_     :				Dict  =     getattr(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

                   UpperCAmelCase_     :				Union[str, Any]  =     pt_model_class(_SCREAMING_SNAKE_CASE    ).eval()
                   UpperCAmelCase_     :				Any  =     model_class(_SCREAMING_SNAKE_CASE  ,dtype=jnp.floataa    )

                   UpperCAmelCase_     :				Optional[Any]  =     load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE  ,fx_model.params    )
                   UpperCAmelCase_,       UpperCAmelCase_     :				int  =     pt_inputs['''input_ids'''].shape
                   UpperCAmelCase_     :				int  =     np.random.randint(0  ,seq_length - 1  ,size=(batch_size,)    )
                   for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE    ):
                        UpperCAmelCase_     :				Optional[int]  =     0
                        UpperCAmelCase_     :				Union[str, Any]  =     1
                        UpperCAmelCase_     :				int  =     0
                        UpperCAmelCase_     :				List[Any]  =     1

                   # make sure weights are tied in PyTorch
                   pt_model.tie_weights()

                   with torch.no_grad():
                        UpperCAmelCase_     :				Optional[Any]  =     pt_model(**_SCREAMING_SNAKE_CASE    ).to_tuple()

                   UpperCAmelCase_     :				Any  =     fx_model(**_SCREAMING_SNAKE_CASE    ).to_tuple()
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,len(_SCREAMING_SNAKE_CASE    )  ,'''Output lengths differ between Flax and PyTorch'''    )
                   for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                        self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2    )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                        fx_model.save_pretrained(_SCREAMING_SNAKE_CASE    )
                        UpperCAmelCase_     :				int  =     pt_model_class.from_pretrained(_SCREAMING_SNAKE_CASE  ,from_flax=_SCREAMING_SNAKE_CASE    )

                   with torch.no_grad():
                        UpperCAmelCase_     :				int  =     pt_model_loaded(**_SCREAMING_SNAKE_CASE    ).to_tuple()

                   self.assertEqual(
                       len(_SCREAMING_SNAKE_CASE    )  ,len(_SCREAMING_SNAKE_CASE    )  ,'''Output lengths differ between Flax and PyTorch'''    )
                   for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                        self.assert_almost_equals(fx_output[:, -1]  ,pt_output[:, -1].numpy()  ,4e-2    )





    @tooslow
    def 				a__ ( self    )					->						str:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_     :				List[str]  =     model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''    )
              UpperCAmelCase_     :				List[Any]  =     model(np.ones((1, 1)    )    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )





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 PoolFormerImageProcessor

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0.9  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						Optional[int]:
         UpperCAmelCase_     :				int  =     size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_     :				List[str]  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_     :				Dict  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     min_resolution
         UpperCAmelCase_     :				Tuple  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize_and_center_crop
         UpperCAmelCase_     :				Tuple  =     size
         UpperCAmelCase_     :				List[str]  =     crop_pct
         UpperCAmelCase_     :				List[str]  =     crop_size
         UpperCAmelCase_     :				Any  =     do_normalize
         UpperCAmelCase_     :				str  =     image_mean
         UpperCAmelCase_     :				List[Any]  =     image_std





    def 				a__ ( self    )					->						str:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = PoolFormerImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				str  =     PoolFormerImageProcessingTester(self    )





    @property
    def 				a__ ( self    )					->						Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_resize_and_center_crop'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''size'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''crop_pct'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_normalize'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_mean'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_std'''    )    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 30}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 30, '''width''': 30}    )

         UpperCAmelCase_     :				Optional[Any]  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42  ,crop_size=84    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 84, '''width''': 84}    )





    def 				a__ ( self    )					->						Optional[int]:
         pass





    def 				a__ ( self    )					->						Dict:
         # Initialize image_processing
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				int  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,Image.Image    )

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

         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				Union[str, Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





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

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





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

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)