


import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:

							class   A__							:




													@staticmethod
													def 							__UpperCamelCase			(    *_a   : str			,      **_a   : Tuple					)							->       Tuple:


																	"""simple docstring"""





																	pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class   A__							(						unittest.TestCase					):
						UpperCAmelCase     =  MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						UpperCAmelCase     =  TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




						def 							__UpperCamelCase			(    self   : Any			,      _a   : Optional[Any]			,      _a   : Optional[int]			,      _a   : List[Any]					)							->       Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ImageClassificationPipeline(model=_a			,      image_processor=_a			,      top_k=2					)
										_SCREAMING_SNAKE_CASE     					=[
										    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
										    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										]
										return image_classifier, examples




						def 							__UpperCamelCase			(    self   : Dict			,      _a   : Any			,      _a   : Any					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)

										self.assertEqual(
										    _a			,      [
										        {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										        {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										    ]			,      )

										import datasets

										_SCREAMING_SNAKE_CASE     					=datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,      '''image'''			,      split='''test'''					)

										# Accepts URL + PIL.Image + lists
										_SCREAMING_SNAKE_CASE     					=image_classifier(
										    [
										        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
										        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										        # RGBA
										        dataset[0]['''file'''],
										        # LA
										        dataset[1]['''file'''],
										        # L
										        dataset[2]['''file'''],
										    ]					)
										self.assertEqual(
										    _a			,      [
										        [
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										        ],
										        [
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										        ],
										        [
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										        ],
										        [
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										        ],
										        [
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										            {'''score''': ANY(_a					), '''label''': ANY(_a					)},
										        ],
										    ]			,      )




						@require_torch
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''hf-internal-testing/tiny-random-vit'''
										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model=_a					)

										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,      )

										_SCREAMING_SNAKE_CASE     					=image_classifier(
										    [
										        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										    ]			,      top_k=2			,      )
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										    ]			,      )




						@require_tf
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''hf-internal-testing/tiny-random-vit'''
										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model=_a			,      framework='''tf'''					)

										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,      )

										_SCREAMING_SNAKE_CASE     					=image_classifier(
										    [
										        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
										    ]			,      top_k=2			,      )
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										        [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
										    ]			,      )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=PreTrainedTokenizer()

										# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
										_SCREAMING_SNAKE_CASE     					=pipeline(
										    '''image-classification'''			,      model='''hf-internal-testing/tiny-random-vit'''			,      tokenizer=_a					)

										self.assertIs(image_classifier.tokenizer			,      _a					)




						@slow
						@require_torch
						def 							__UpperCamelCase			(    self   : Any					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model='''deepmind/vision-perceiver-conv'''					)
										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
										        {'''score''': 0.3_21, '''label''': '''tiger cat'''},
										        {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
										        {'''score''': 0.01_37, '''label''': '''crib, cot'''},
										        {'''score''': 0.0_07, '''label''': '''radiator'''},
										    ]			,      )

										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model='''deepmind/vision-perceiver-fourier'''					)
										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
										        {'''score''': 0.13_09, '''label''': '''tiger cat'''},
										        {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
										        {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
										        {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
										    ]			,      )

										_SCREAMING_SNAKE_CASE     					=pipeline('''image-classification'''			,      model='''deepmind/vision-perceiver-learned'''					)
										_SCREAMING_SNAKE_CASE     					=image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
										self.assertEqual(
										    nested_simplify(_a			,      decimals=4					)			,      [
										        {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
										        {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
										        {'''score''': 0.18_56, '''label''': '''tiger cat'''},
										        {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
										        {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
										    ]			,      )



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   A__							(						UpperCamelCase__				,       unittest.TestCase					):
						UpperCAmelCase     =  KandinskyPriorPipeline
						UpperCAmelCase     =  ["prompt"]
						UpperCAmelCase     =  ["prompt", "negative_prompt"]
						UpperCAmelCase     =  [
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase     =  False




						@property
						def 							__UpperCamelCase			(    self   : Tuple					)							->       Tuple:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase			(    self   : Tuple					)							->       int:


										"""simple docstring"""





										return self.time_input_dim




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


										"""simple docstring"""





										return self.time_input_dim * 4




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return 100




						@property
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
										return tokenizer




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       Dict:


										"""simple docstring"""





										torch.manual_seed(0					)
										_SCREAMING_SNAKE_CASE     					=CLIPTextConfig(
										    bos_token_id=0			,      eos_token_id=2			,      hidden_size=self.text_embedder_hidden_size			,      projection_dim=self.text_embedder_hidden_size			,      intermediate_size=37			,      layer_norm_eps=1E-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=1000			,      )
										return CLIPTextModelWithProjection(_a					)




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Any:


										"""simple docstring"""





										torch.manual_seed(0					)

										_SCREAMING_SNAKE_CASE     					={
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 12,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										_SCREAMING_SNAKE_CASE     					=PriorTransformer(**_a					)
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										_SCREAMING_SNAKE_CASE     					=nn.Parameter(torch.ones(model.clip_std.shape					)					)
										return model




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


										"""simple docstring"""





										torch.manual_seed(0					)
										_SCREAMING_SNAKE_CASE     					=CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size			,      image_size=224			,      projection_dim=self.text_embedder_hidden_size			,      intermediate_size=37			,      num_attention_heads=4			,      num_channels=3			,      num_hidden_layers=5			,      patch_size=14			,      )

										_SCREAMING_SNAKE_CASE     					=CLIPVisionModelWithProjection(_a					)
										return model




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=CLIPImageProcessor(
										    crop_size=224			,      do_center_crop=_a			,      do_normalize=_a			,      do_resize=_a			,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,      resample=3			,      size=224			,      )

										return image_processor




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.dummy_prior
										_SCREAMING_SNAKE_CASE     					=self.dummy_image_encoder
										_SCREAMING_SNAKE_CASE     					=self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE     					=self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE     					=self.dummy_image_processor

										_SCREAMING_SNAKE_CASE     					=UnCLIPScheduler(
										    variance_type='''fixed_small_log'''			,      prediction_type='''sample'''			,      num_train_timesteps=1000			,      clip_sample=_a			,      clip_sample_range=10.0			,      )

										_SCREAMING_SNAKE_CASE     					={
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''scheduler''': scheduler,
										    '''image_processor''': image_processor,
										}

										return components




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Tuple			,      _a   : str=0					)							->       Any:


										"""simple docstring"""





										if str(_a					).startswith('''mps'''					):
														_SCREAMING_SNAKE_CASE     					=torch.manual_seed(_a					)
										else:
														_SCREAMING_SNAKE_CASE     					=torch.Generator(device=_a					).manual_seed(_a					)
										_SCREAMING_SNAKE_CASE     					={
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs




						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''cpu'''

										_SCREAMING_SNAKE_CASE     					=self.get_dummy_components()

										_SCREAMING_SNAKE_CASE     					=self.pipeline_class(**_a					)
										_SCREAMING_SNAKE_CASE     					=pipe.to(_a					)

										pipe.set_progress_bar_config(disable=_a					)

										_SCREAMING_SNAKE_CASE     					=pipe(**self.get_dummy_inputs(_a					)					)
										_SCREAMING_SNAKE_CASE     					=output.image_embeds

										_SCREAMING_SNAKE_CASE     					=pipe(
										    **self.get_dummy_inputs(_a					)			,      return_dict=_a			,      )[0]

										_SCREAMING_SNAKE_CASE     					=image[0, -10:]
										_SCREAMING_SNAKE_CASE     					=image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										_SCREAMING_SNAKE_CASE     					=np.array(
										    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2




						@skip_mps
						def 							__UpperCamelCase			(    self   : List[str]					)							->       List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE     					=True
										_SCREAMING_SNAKE_CASE     					=False

										self._test_inference_batch_single_identical(
										    test_max_difference=_a			,      relax_max_difference=_a			,      test_mean_pixel_difference=_a			,      )




						@skip_mps
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE     					=False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=_a			,      test_mean_pixel_difference=_a			,      )