

'''simple docstring'''

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      ):







				"""simple docstring"""



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




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

										__lowerCAmelCase							       =				self.get_config()

										return config, pixel_values




				def 					_snake_case  (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 					_snake_case  (self       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				FlaxRegNetModel(config=__lowercase		)
										__lowerCAmelCase							       =				model(__lowercase		)

										# 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 					_snake_case  (self       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				self.num_labels
										__lowerCAmelCase							       =				FlaxRegNetForImageClassification(config=__lowercase		)
										__lowerCAmelCase							       =				model(__lowercase		)
										self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)		)




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


@require_flax
class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Any	=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				__UpperCamelCase    :		Tuple	=	False
				__UpperCamelCase    :		Tuple	=	False
				__UpperCamelCase    :		Dict	=	False




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				FlaxRegNetModelTester(self		)
										__lowerCAmelCase							       =				ConfigTester(self       ,			config_class=__lowercase       ,			has_text_modality=__lowercase		)




				def 					_snake_case  (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 					_snake_case  (self		):
										return




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowercase		)




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




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




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

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

																__lowerCAmelCase							       =				['''pixel_values''']
																self.assertListEqual(arg_names[:1]       ,			__lowercase		)




				def 					_snake_case  (self		):
										def check_hidden_states_output(__lowercase       ,			__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				model_class(__lowercase		)

																__lowerCAmelCase							       =				model(**self._prepare_for_class(__lowercase       ,			__lowercase		)		)

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

																__lowerCAmelCase							       =				self.model_tester.num_stages
																self.assertEqual(len(__lowercase		)       ,			expected_num_stages + 1		)

										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

																check_hidden_states_output(__lowercase       ,			__lowercase       ,			__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																						__lowerCAmelCase							       =				self._prepare_for_class(__lowercase       ,			__lowercase		)
																						__lowerCAmelCase							       =				model_class(__lowercase		)

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

																						with self.subTest('''JIT Enabled'''		):
																												__lowerCAmelCase							       =				model_jitted(**__lowercase		).to_tuple()

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

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

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




@require_flax
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



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




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

										__lowerCAmelCase							       =				self.default_image_processor
										__lowerCAmelCase							       =				prepare_img()
										__lowerCAmelCase							       =				image_processor(images=__lowercase       ,			return_tensors='''np'''		)

										__lowerCAmelCase							       =				model(**__lowercase		)

										# verify the logits
										__lowerCAmelCase							       =				(1, 10_00)
										self.assertEqual(outputs.logits.shape       ,			__lowercase		)

										__lowerCAmelCase							       =				jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]		)

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





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__lowerCAmelCase							       =				samples.cpu().permute(0       ,			2       ,			3       ,			1		).float().numpy()

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

