





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






      def __init__(      self      :      Any		,   _A      :      Optional[int]		,   _A      :      Optional[int]=7		,   _A      :      Optional[Any]=3		,   _A      :      List[Any]=18		,   _A      :      Dict=30		,   _A      :      Any=400		,   _A      :      List[str]=True		,   _A      :      Any=None		,   _A      :      Union[str, Any]=True		,   _A      :      Union[str, Any]=False		,   _A      :      Any=True		,   _A      :      str=True		,   _A      :      Dict=[0.5, 0.5, 0.5]		,   _A      :      Dict=[0.5, 0.5, 0.5]		,   )							->    Union[str, Any]:







       """simple docstring"""


       snake_case_		:			int											=	parent
       snake_case_		:			Optional[Any]											=	batch_size
       snake_case_		:			Tuple											=	num_channels
       snake_case_		:			Dict											=	image_size
       snake_case_		:			str											=	min_resolution
       snake_case_		:			List[str]											=	max_resolution
       snake_case_		:			Tuple											=	do_resize
       snake_case_		:			Tuple											=	size if size is not None else {"""height""": 18, """width""": 20}
       snake_case_		:			Optional[Any]											=	do_thumbnail
       snake_case_		:			int											=	do_align_axis
       snake_case_		:			List[str]											=	do_pad
       snake_case_		:			List[str]											=	do_normalize
       snake_case_		:			Any											=	image_mean
       snake_case_		:			List[Any]											=	image_std






      def UpperCAmelCase_				(      self      :      Optional[int]	)							->    int:







       """simple docstring"""


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
      __magic_name__:			Tuple       =       DonutImageProcessor if is_vision_available() else None






      def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    int:







       """simple docstring"""


       snake_case_		:			Optional[int]											=	DonutImageProcessingTester(self	)






      @property
      def UpperCAmelCase_				(      self      :      Any	)							->    str:







       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()






      def UpperCAmelCase_				(      self      :      Tuple	)							->    Dict:







       """simple docstring"""


       snake_case_		:			List[Any]											=	self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(A__		,   'do_resize'	)	)
       self.assertTrue(hasattr(A__		,   'size'	)	)
       self.assertTrue(hasattr(A__		,   'do_thumbnail'	)	)
       self.assertTrue(hasattr(A__		,   'do_align_long_axis'	)	)
       self.assertTrue(hasattr(A__		,   'do_pad'	)	)
       self.assertTrue(hasattr(A__		,   'do_normalize'	)	)
       self.assertTrue(hasattr(A__		,   'image_mean'	)	)
       self.assertTrue(hasattr(A__		,   'image_std'	)	)






      def UpperCAmelCase_				(      self      :      Tuple	)							->    List[str]:







       """simple docstring"""


       snake_case_		:			str											=	self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size		,   {'height': 18, 'width': 20}	)

       snake_case_		:			int											=	self.image_processing_class.from_dict(self.image_processor_dict		,   size=42	)
       self.assertEqual(image_processor.size		,   {'height': 42, 'width': 42}	)

       # Previous config had dimensions in (width, height) order
       snake_case_		:			Tuple											=	self.image_processing_class.from_dict(self.image_processor_dict		,   size=(42, 84)	)
       self.assertEqual(image_processor.size		,   {'height': 84, 'width': 42}	)






      def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[str]:







       """simple docstring"""


       pass






      @is_flaky()
      def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







       """simple docstring"""


       snake_case_		:			int											=	self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       snake_case_		:			Union[str, Any]											=	prepare_image_inputs(self.image_processor_tester		,   equal_resolution=A__	)
       for image in image_inputs:
        self.assertIsInstance(A__		,   Image.Image	)

       # Test not batched input
       snake_case_		:			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.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )

       # Test batched
       snake_case_		:			Union[str, 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,
               self.image_processor_tester.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )






      @is_flaky()
      def UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[Any]:







       """simple docstring"""


       snake_case_		:			Union[str, Any]											=	self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       snake_case_		:			List[str]											=	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
       snake_case_		:			Union[str, Any]											=	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.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )

       # Test batched
       snake_case_		:			Optional[int]											=	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,
               self.image_processor_tester.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )






      @is_flaky()
      def UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[int]:







       """simple docstring"""


       snake_case_		:			Any											=	self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       snake_case_		:			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
       snake_case_		:			Optional[Any]											=	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.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )

       # Test batched
       snake_case_		:			List[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,
               self.image_processor_tester.size['height'],
               self.image_processor_tester.size['width'],
           )		,   )








import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






						@property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	{
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							snake_case_		:			Tuple											=	self.dummy_input
							return init_dict, inputs_dict






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_						,snake_case_		:			str											=	PriorTransformer.from_pretrained(
							    'hf-internal-testing/prior-dummy'		,   output_loading_info=_A	)
							self.assertIsNotNone(_A	)
							self.assertEqual(len(loading_info['missing_keys']	)		,   0	)

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

							assert hidden_states is not None, "Make sure output is not None"






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[int]											=	self.prepare_init_args_and_inputs_for_common()

							snake_case_		:			Tuple											=	self.model_class(**_A	)
							snake_case_		:			Any											=	inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case_		:			List[str]											=	[*signature.parameters.keys()]

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
							snake_case_		:			str											=	model.to(_A	)

							if hasattr(_A		,   'set_default_attn_processor'	):
								model.set_default_attn_processor()

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							snake_case_		:			str											=	torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
							self.assertTrue(torch_all_close(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
						        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
						        # fmt: on
						    ]	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'		,   subfolder='prior'	)
							model.to(_A	)
							snake_case_		:			Optional[Any]											=	self.get_dummy_seed_input(seed=_A	)

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

							assert list(sample.shape	) == [1, 768]

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

							assert torch_all_close(_A		,   _A		,   atol=1E-3	)

