


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        lowerCamelCase      (     unittest.TestCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__=3							,    lowercase__=3_2							,    lowercase__=3							,    lowercase__=1_0							,    lowercase__=[1_0, 2_0, 3_0, 4_0]							,    lowercase__=[1, 1, 2, 1]							,    lowercase__=True							,    lowercase__=True							,    lowercase__="relu"							,    lowercase__=3							,    lowercase__=None							,    ):
							__UpperCAmelCase :       Any  						=  parent
							__UpperCAmelCase :       Dict  						=  batch_size
							__UpperCAmelCase :       int  						=  image_size
							__UpperCAmelCase :       Any  						=  num_channels
							__UpperCAmelCase :       List[Any]  						=  embeddings_size
							__UpperCAmelCase :       int  						=  hidden_sizes
							__UpperCAmelCase :       Any  						=  depths
							__UpperCAmelCase :       List[str]  						=  is_training
							__UpperCAmelCase :       Any  						=  use_labels
							__UpperCAmelCase :       Optional[Any]  						=  hidden_act
							__UpperCAmelCase :       Optional[Any]  						=  num_labels
							__UpperCAmelCase :       Optional[Any]  						=  scope
							__UpperCAmelCase :       str  						=  len(lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							__UpperCAmelCase :       Optional[Any]  						=  self.get_config()

							return config, pixel_values


	def    A(						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    A(						self							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       List[str]  						=  FlaxRegNetModel(config=lowercase__)
							__UpperCAmelCase :       Union[str, Any]  						=  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 // 3_2, self.image_size // 3_2)							,    )


	def    A(						self							,    lowercase__							,    lowercase__):
							__UpperCAmelCase :       Any  						=  self.num_labels
							__UpperCAmelCase :       Any  						=  FlaxRegNetForImageClassification(config=lowercase__)
							__UpperCAmelCase :       Optional[int]  						=  model(lowercase__)
							self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels))


	def    A(						self):
							__UpperCAmelCase :       List[str]  						=  self.prepare_config_and_inputs()
							__UpperCAmelCase						,			__UpperCAmelCase :       int  						=  config_and_inputs
							__UpperCAmelCase :       int  						=  {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_flax
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Union[str, Any]        =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	_lowerCAmelCase    :		Any        =	False
	_lowerCAmelCase    :		Optional[Any]        =	False
	_lowerCAmelCase    :		Optional[int]        =	False


	def    A(						self):
							__UpperCAmelCase :       Dict  						=  FlaxRegNetModelTester(self)
							__UpperCAmelCase :       Dict  						=  ConfigTester(self							,    config_class=lowercase__							,    has_text_modality=lowercase__)


	def    A(						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    A(						self):
							return


	def    A(						self):
							__UpperCAmelCase :       List[str]  						=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  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    A(						self):
							pass


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


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase :       int  						=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__UpperCAmelCase :       Tuple  						=  model_class(lowercase__)
													__UpperCAmelCase :       Optional[int]  						=  inspect.signature(model.__call__)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__UpperCAmelCase :       Optional[Any]  						=  [*signature.parameters.keys()]

													__UpperCAmelCase :       str  						=  ['''pixel_values''']
													self.assertListEqual(arg_names[:1]							,    lowercase__)


	def    A(						self):
							def check_hidden_states_output(lowercase__							,    lowercase__							,    lowercase__):
													__UpperCAmelCase :       Any  						=  model_class(lowercase__)

													__UpperCAmelCase :       List[str]  						=  model(**self._prepare_for_class(lowercase__							,    lowercase__))

													__UpperCAmelCase :       int  						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__UpperCAmelCase :       str  						=  self.model_tester.num_stages
													self.assertEqual(len(lowercase__)							,    expected_num_stages + 1)

							__UpperCAmelCase						,			__UpperCAmelCase :       Optional[int]  						=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__UpperCAmelCase :       int  						=  True
													check_hidden_states_output(lowercase__							,    lowercase__							,    lowercase__)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__UpperCAmelCase :       Optional[int]  						=  True

													check_hidden_states_output(lowercase__							,    lowercase__							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__):
																			__UpperCAmelCase :       Any  						=  self._prepare_for_class(lowercase__							,    lowercase__)
																			__UpperCAmelCase :       Any  						=  model_class(lowercase__)

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

																			with self.subTest('''JIT Enabled'''):
																									__UpperCAmelCase :       Optional[Any]  						=  model_jitted(**lowercase__).to_tuple()

																			with self.subTest('''JIT Disabled'''):
																									with jax.disable_jit():
																															__UpperCAmelCase :       Dict  						=  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 						__SCREAMING_SNAKE_CASE       (					)		->    List[Any]:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
						return image





@require_flax
class        lowerCamelCase      (     unittest.TestCase   ):


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


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

							__UpperCAmelCase :       Optional[int]  						=  self.default_image_processor
							__UpperCAmelCase :       Union[str, Any]  						=  prepare_img()
							__UpperCAmelCase :       Union[str, Any]  						=  image_processor(images=lowercase__							,    return_tensors='''np''')

							__UpperCAmelCase :       Optional[int]  						=  model(**lowercase__)

							# verify the logits
							__UpperCAmelCase :       str  						=  (1, 1_0_0_0)
							self.assertEqual(outputs.logits.shape							,    lowercase__)

							__UpperCAmelCase :       Union[str, Any]  						=  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))





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        lowerCamelCase      (     _UpperCamelCase   ):


	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
							__UpperCAmelCase :       List[str]  						=  {}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(''','''):
																									__UpperCAmelCase :       Dict  						=  int(lowercase__)
													__UpperCAmelCase :       Tuple  						=  dict(sorted(self.labels.items()))


	def    A(						self							,    lowercase__):

							if not isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Dict  						=  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__ = 5_0							,    lowercase__ = "pil"							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       List[str]  						=  len(lowercase__)
							__UpperCAmelCase :       str  						=  self.transformer.config.sample_size
							__UpperCAmelCase :       List[str]  						=  self.transformer.config.in_channels

							__UpperCAmelCase :       Union[str, Any]  						=  randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)							,    generator=lowercase__							,    device=self.device							,    dtype=self.transformer.dtype							,    )
							__UpperCAmelCase :       Optional[Any]  						=  torch.cat([latents] * 2) if guidance_scale > 1 else latents

							__UpperCAmelCase :       Union[str, Any]  						=  torch.tensor(lowercase__							,    device=self.device).reshape(-1)
							__UpperCAmelCase :       Dict  						=  torch.tensor([1_0_0_0] * batch_size							,    device=self.device)
							__UpperCAmelCase :       int  						=  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:
																			__UpperCAmelCase :       List[str]  						=  latent_model_input[: len(lowercase__) // 2]
																			__UpperCAmelCase :       Optional[Any]  						=  torch.cat([half, half]							,    dim=0)
													__UpperCAmelCase :       Optional[Any]  						=  self.scheduler.scale_model_input(lowercase__							,    lowercase__)

													__UpperCAmelCase :       Any  						=  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+)
																			__UpperCAmelCase :       List[str]  						=  latent_model_input.device.type == '''mps'''
																			if isinstance(lowercase__							,    lowercase__):
																									__UpperCAmelCase :       Tuple  						=  torch.floataa if is_mps else torch.floataa
																			else:
																									__UpperCAmelCase :       Dict  						=  torch.intaa if is_mps else torch.intaa
																			__UpperCAmelCase :       List[str]  						=  torch.tensor([timesteps]							,    dtype=lowercase__							,    device=latent_model_input.device)
													elif len(timesteps.shape) == 0:
																			__UpperCAmelCase :       List[str]  						=  timesteps[None].to(latent_model_input.device)
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													__UpperCAmelCase :       Optional[int]  						=  timesteps.expand(latent_model_input.shape[0])
													# predict noise model_output
													__UpperCAmelCase :       Any  						=  self.transformer(
													    lowercase__							,    timestep=lowercase__							,    class_labels=lowercase__).sample

													# perform guidance
													if guidance_scale > 1:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  torch.split(lowercase__							,    len(lowercase__) // 2							,    dim=0)

																			__UpperCAmelCase :       List[str]  						=  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			__UpperCAmelCase :       str  						=  torch.cat([half_eps, half_eps]							,    dim=0)

																			__UpperCAmelCase :       Any  						=  torch.cat([eps, rest]							,    dim=1)

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  torch.split(lowercase__							,    lowercase__							,    dim=1)
													else:
																			__UpperCAmelCase :       Any  						=  noise_pred

													# compute previous image: x_t -> x_t-1
													__UpperCAmelCase :       Dict  						=  self.scheduler.step(lowercase__							,    lowercase__							,    lowercase__).prev_sample

							if guidance_scale > 1:
													__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  latent_model_input.chunk(2							,    dim=0)
							else:
													__UpperCAmelCase :       List[Any]  						=  latent_model_input

							__UpperCAmelCase :       List[str]  						=  1 / self.vae.config.scaling_factor * latents
							__UpperCAmelCase :       Optional[int]  						=  self.vae.decode(lowercase__).sample

							__UpperCAmelCase :       List[str]  						=  (samples / 2 + 0.5).clamp(0							,    1)

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

							if output_type == "pil":
													__UpperCAmelCase :       Optional[int]  						=  self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=lowercase__)

