
import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 			A__	(     unittest.TestCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=32	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=10	,		_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]	,		_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE="relu"	,		_SCREAMING_SNAKE_CASE=3	,		_SCREAMING_SNAKE_CASE=None	,		):
			__lowerCAmelCase      :						List[str]						      =				parent
			__lowerCAmelCase      :						Tuple						      =				batch_size
			__lowerCAmelCase      :						int						      =				image_size
			__lowerCAmelCase      :						Any						      =				num_channels
			__lowerCAmelCase      :						Optional[int]						      =				embeddings_size
			__lowerCAmelCase      :						Optional[int]						      =				hidden_sizes
			__lowerCAmelCase      :						str						      =				depths
			__lowerCAmelCase      :						Tuple						      =				is_training
			__lowerCAmelCase      :						List[str]						      =				use_labels
			__lowerCAmelCase      :						List[str]						      =				hidden_act
			__lowerCAmelCase      :						Tuple						      =				num_labels
			__lowerCAmelCase      :						Tuple						      =				scope
			__lowerCAmelCase      :						List[str]						      =				len(UpperCamelCase__			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

			__lowerCAmelCase      :						Any						      =				self.get_config()

			return config, pixel_values

		def 							__lowerCamelCase							(				self			):
			return RegNetConfig(
			    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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				FlaxRegNetModel(config=UpperCamelCase__			)
			__lowerCAmelCase      :						str						      =				model(UpperCamelCase__			)

			# 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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						int						      =				self.num_labels
			__lowerCAmelCase      :						List[str]						      =				FlaxRegNetForImageClassification(config=UpperCamelCase__			)
			__lowerCAmelCase      :						Any						      =				model(UpperCamelCase__			)
			self.parent.assertEqual(result.logits.shape	,		(self.batch_size, self.num_labels)			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.prepare_config_and_inputs()
			__lowerCAmelCase      :						Tuple						      =				config_and_inputs
			__lowerCAmelCase      :						int						      =				{'''pixel_values''': pixel_values}
			return config, inputs_dict




@require_flax
class 			A__	(     __SCREAMING_SNAKE_CASE	,					unittest.TestCase):
		A_						:	Optional[int]           =     (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

		A_						:	List[str]           =     False
		A_						:	Optional[Any]           =     False
		A_						:	str           =     False

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				FlaxRegNetModelTester(self			)
			__lowerCAmelCase      :						Union[str, Any]						      =				ConfigTester(self	,		config_class=UpperCamelCase__	,		has_text_modality=UpperCamelCase__			)

		def 							__lowerCamelCase							(				self			):
			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 							__lowerCamelCase							(				self			):
			return

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCamelCase__			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__			)

		@unittest.skip(reason='RegNet does not use inputs_embeds'			)
		def 							__lowerCamelCase							(				self			):
			pass

		@unittest.skip(reason='RegNet does not support input and output embeddings'			)
		def 							__lowerCamelCase							(				self			):
			pass

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase      :						Union[str, Any]						      =				model_class(UpperCamelCase__			)
				__lowerCAmelCase      :						Union[str, Any]						      =				inspect.signature(model.__call__			)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__lowerCAmelCase      :						int						      =				[*signature.parameters.keys()]

				__lowerCAmelCase      :						Optional[Any]						      =				['''pixel_values''']
				self.assertListEqual(arg_names[:1]	,		UpperCamelCase__			)

		def 							__lowerCamelCase							(				self			):







			def check_hidden_states_output(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase      :						Union[str, Any]						      =				model_class(UpperCamelCase__			)

				__lowerCAmelCase      :						Any						      =				model(**self._prepare_for_class(UpperCamelCase__	,		UpperCamelCase__			)			)

				__lowerCAmelCase      :						Optional[Any]						      =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

				__lowerCAmelCase      :						Optional[int]						      =				self.model_tester.num_stages
				self.assertEqual(len(UpperCamelCase__			)	,		expected_num_stages + 1			)

			__lowerCAmelCase      :						Optional[Any]						      =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase      :						Tuple						      =				True
				check_hidden_states_output(UpperCamelCase__	,		UpperCamelCase__	,		UpperCamelCase__			)

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				__lowerCAmelCase      :						List[str]						      =				True

				check_hidden_states_output(UpperCamelCase__	,		UpperCamelCase__	,		UpperCamelCase__			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				with self.subTest(model_class.__name__			):
					__lowerCAmelCase      :						Any						      =				self._prepare_for_class(UpperCamelCase__	,		UpperCamelCase__			)
					__lowerCAmelCase      :						Optional[Any]						      =				model_class(UpperCamelCase__			)

					@jax.jit
					def model_jitted(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
						return model(pixel_values=UpperCamelCase__	,		**UpperCamelCase__			)

					with self.subTest('JIT Enabled'			):
						__lowerCAmelCase      :						Optional[int]						      =				model_jitted(**UpperCamelCase__			).to_tuple()

					with self.subTest('JIT Disabled'			):
						with jax.disable_jit():
							__lowerCAmelCase      :						Tuple						      =				model_jitted(**UpperCamelCase__			).to_tuple()

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



def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						List[str]						      =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
	return image




@require_flax
class 			A__	(     unittest.TestCase):

		@cached_property
		def 							__lowerCamelCase							(				self			):
			return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'			) if is_vision_available() else None







		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'			)

			__lowerCAmelCase      :						Any						      =				self.default_image_processor
			__lowerCAmelCase      :						Any						      =				prepare_img()
			__lowerCAmelCase      :						Union[str, Any]						      =				image_processor(images=UpperCamelCase__	,		return_tensors='np'			)

			__lowerCAmelCase      :						List[str]						      =				model(**UpperCamelCase__			)

			# verify the logits
			__lowerCAmelCase      :						Optional[int]						      =				(1, 10_00)
			self.assertEqual(outputs.logits.shape	,		UpperCamelCase__			)

			__lowerCAmelCase      :						Dict						      =				jnp.array([-0.4180, -1.5051, -3.4836]			)

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

"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference




class 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	Optional[int]           =     ShapEPipeline
		A_						:	str           =     ['prompt']
		A_						:	Any           =     ['prompt']
		A_						:	List[Any]           =     [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		A_						:	Optional[int]           =     False

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return self.time_input_dim * 4

		@property
		def 							__lowerCamelCase							(				self			):
			return 8

		@property
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
			return tokenizer

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						str						      =				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=10_00	,		)
			return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE			)

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)

			__lowerCAmelCase      :						Optional[Any]						      =				{
			    'num_attention_heads': 2,
			    'attention_head_dim': 16,
			    'embedding_dim': self.time_input_dim,
			    'num_embeddings': 32,
			    'embedding_proj_dim': self.text_embedder_hidden_size,
			    'time_embed_dim': self.time_embed_dim,
			    'num_layers': 1,
			    'clip_embed_dim': self.time_input_dim * 2,
			    'additional_embeddings': 0,
			    'time_embed_act_fn': 'gelu',
			    'norm_in_type': 'layer',
			    'encoder_hid_proj_type': None,
			    'added_emb_type': None,
			}

			__lowerCAmelCase      :						int						      =				PriorTransformer(**_SCREAMING_SNAKE_CASE			)
			return model

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)

			__lowerCAmelCase      :						Dict						      =				{
			    'param_shapes': (
			        (self.renderer_dim, 93),
			        (self.renderer_dim, 8),
			        (self.renderer_dim, 8),
			        (self.renderer_dim, 8),
			    ),
			    'd_latent': self.time_input_dim,
			    'd_hidden': self.renderer_dim,
			    'n_output': 12,
			    'background': (
			        0.1,
			        0.1,
			        0.1,
			    ),
			}
			__lowerCAmelCase      :						Union[str, Any]						      =				ShapERenderer(**_SCREAMING_SNAKE_CASE			)
			return model

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.dummy_prior
			__lowerCAmelCase      :						str						      =				self.dummy_text_encoder
			__lowerCAmelCase      :						List[Any]						      =				self.dummy_tokenizer
			__lowerCAmelCase      :						str						      =				self.dummy_renderer

			__lowerCAmelCase      :						List[Any]						      =				HeunDiscreteScheduler(
			    beta_schedule='exp'	,		num_train_timesteps=10_24	,		prediction_type='sample'	,		use_karras_sigmas=_SCREAMING_SNAKE_CASE	,		clip_sample=_SCREAMING_SNAKE_CASE	,		clip_sample_range=1.0	,		)
			__lowerCAmelCase      :						int						      =				{
			    'prior': prior,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'renderer': renderer,
			    'scheduler': scheduler,
			}

			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						int						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Dict						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				{
			    'prompt': 'horse',
			    'generator': generator,
			    'num_inference_steps': 1,
			    'frame_size': 32,
			    'output_type': 'np',
			}
			return inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'cpu'

			__lowerCAmelCase      :						int						      =				self.get_dummy_components()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				pipe.to(_SCREAMING_SNAKE_CASE			)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Optional[Any]						      =				output.images[0]
			__lowerCAmelCase      :						Optional[int]						      =				image[0, -3:, -3:, -1]

			assert image.shape == (20, 32, 32, 3)

			__lowerCAmelCase      :						str						      =				np.array(
			    [
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			    ]			)

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

		def 							__lowerCamelCase							(				self			):
			# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
			self._test_inference_batch_consistent(batch_sizes=[1, 2]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				torch_device == 'cpu'
			__lowerCAmelCase      :						str						      =				True

			self._test_inference_batch_single_identical(
			    batch_size=2	,		test_max_difference=_SCREAMING_SNAKE_CASE	,		relax_max_difference=_SCREAMING_SNAKE_CASE	,		)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.get_dummy_components()
			__lowerCAmelCase      :						List[Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						int						      =				1
			__lowerCAmelCase      :						List[Any]						      =				2

			__lowerCAmelCase      :						int						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)

			for key in inputs.keys():
				if key in self.batch_params:
					__lowerCAmelCase      :						Dict						      =				batch_size * [inputs[key]]

			__lowerCAmelCase      :						Any						      =				pipe(**_SCREAMING_SNAKE_CASE	,		num_images_per_prompt=_SCREAMING_SNAKE_CASE			)[0]

			assert images.shape[0] == batch_size * num_images_per_prompt




@slow
@require_torch_gpu
class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/shap_e/test_shap_e_np_out.npy'			)
			__lowerCAmelCase      :						Dict						      =				ShapEPipeline.from_pretrained('openai/shap-e'			)
			__lowerCAmelCase      :						int						      =				pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(0			)

			__lowerCAmelCase      :						List[str]						      =				pipe(
			    'a shark'	,		generator=_SCREAMING_SNAKE_CASE	,		guidance_scale=15.0	,		num_inference_steps=64	,		frame_size=64	,		output_type='np'	,		).images[0]

			assert images.shape == (20, 64, 64, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)