


import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

							from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class   A__							(						unittest.TestCase					):




						def __init__(    self   : List[Any]			,      _a   : Optional[Any]			,      _a   : int=3			,      _a   : int=32			,      _a   : str=3			,      _a   : List[str]=10			,      _a   : Optional[Any]=[10, 20, 30, 40]			,      _a   : Tuple=[1, 1, 2, 1]			,      _a   : Optional[Any]=True			,      _a   : Optional[int]=True			,      _a   : Any="relu"			,      _a   : Any=3			,      _a   : Tuple=None			,      )							->       Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=parent
										_SCREAMING_SNAKE_CASE     					=batch_size
										_SCREAMING_SNAKE_CASE     					=image_size
										_SCREAMING_SNAKE_CASE     					=num_channels
										_SCREAMING_SNAKE_CASE     					=embeddings_size
										_SCREAMING_SNAKE_CASE     					=hidden_sizes
										_SCREAMING_SNAKE_CASE     					=depths
										_SCREAMING_SNAKE_CASE     					=is_training
										_SCREAMING_SNAKE_CASE     					=use_labels
										_SCREAMING_SNAKE_CASE     					=hidden_act
										_SCREAMING_SNAKE_CASE     					=num_labels
										_SCREAMING_SNAKE_CASE     					=scope
										_SCREAMING_SNAKE_CASE     					=len(_a					)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										_SCREAMING_SNAKE_CASE     					=self.get_config()

										return config, pixel_values




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


										"""simple docstring"""





										return ResNetConfig(
										    num_channels=self.num_channels			,      embeddings_size=self.embeddings_size			,      hidden_sizes=self.hidden_sizes			,      depths=self.depths			,      hidden_act=self.hidden_act			,      num_labels=self.num_labels			,      image_size=self.image_size			,      )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=FlaxResNetModel(config=_a					)
										_SCREAMING_SNAKE_CASE     					=model(_a					)

										# Output shape (b, c, h, w)
										self.parent.assertEqual(
										    result.last_hidden_state.shape			,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,      )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.num_labels
										_SCREAMING_SNAKE_CASE     					=FlaxResNetForImageClassification(config=_a					)
										_SCREAMING_SNAKE_CASE     					=model(_a					)
										self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_labels)					)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=config_and_inputs
										_SCREAMING_SNAKE_CASE     					={'''pixel_values''': pixel_values}
										return config, inputs_dict

@require_flax
class   A__							(						UpperCamelCase__				,       unittest.TestCase					):
						UpperCAmelCase     =  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

						UpperCAmelCase     =  False
						UpperCAmelCase     =  False
						UpperCAmelCase     =  False




						def 							__UpperCamelCase			(    self   : str					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=FlaxResNetModelTester(self					)
										_SCREAMING_SNAKE_CASE     					=ConfigTester(self			,      config_class=_a			,      has_text_modality=_a					)




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


										"""simple docstring"""





										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




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


										"""simple docstring"""





										return




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a					)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_a					)




						@unittest.skip(reason='''ResNet does not use inputs_embeds'''					)
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       List[Any]:


										"""simple docstring"""





										pass




						@unittest.skip(reason='''ResNet does not support input and output embeddings'''					)
						def 							__UpperCamelCase			(    self   : Dict					)							->       Dict:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase			(    self   : Any					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     					=model_class(_a					)
														_SCREAMING_SNAKE_CASE     					=inspect.signature(model.__call__					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_SCREAMING_SNAKE_CASE     					=[*signature.parameters.keys()]

														_SCREAMING_SNAKE_CASE     					=['''pixel_values''']
														self.assertListEqual(arg_names[:1]			,      _a					)




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


										"""simple docstring"""





										def check_hidden_states_output(_a   : Optional[Any]			,      _a   : Tuple			,      _a   : Optional[Any]					):
														_SCREAMING_SNAKE_CASE     					=model_class(_a					)

														_SCREAMING_SNAKE_CASE     					=model(**self._prepare_for_class(_a			,      _a					)					)

														_SCREAMING_SNAKE_CASE     					=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_SCREAMING_SNAKE_CASE     					=self.model_tester.num_stages
														self.assertEqual(len(_a					)			,      expected_num_stages + 1					)




						@unittest.skip(reason='''ResNet does not use feedforward chunking'''					)
						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Dict:


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

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

																		@jax.jit
																		def model_jitted(_a   : Optional[Any]			,      **_a   : Any					):
																						return model(pixel_values=_a			,      **_a					)

																		with self.subTest('''JIT Enabled'''					):
																						_SCREAMING_SNAKE_CASE     					=model_jitted(**_a					).to_tuple()

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

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







def   lowerCamelCase(				):
				_SCREAMING_SNAKE_CASE     					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image


@require_flax
class   A__							(						unittest.TestCase					):




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


										"""simple docstring"""





										return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''					) if is_vision_available() else None




						@slow
						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''					)

										_SCREAMING_SNAKE_CASE     					=self.default_image_processor
										_SCREAMING_SNAKE_CASE     					=prepare_img()
										_SCREAMING_SNAKE_CASE     					=image_processor(images=_a			,      return_tensors='''np'''					)

										_SCREAMING_SNAKE_CASE     					=model(**_a					)

										# verify the logits
										_SCREAMING_SNAKE_CASE     					=(1, 1000)
										self.assertEqual(outputs.logits.shape			,      _a					)

										_SCREAMING_SNAKE_CASE     					=jnp.array([-11.10_69, -9.78_77, -8.37_77]					)

										self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,      _a			,      atol=1E-4					)					)



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			,      )