

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						,					A_						,					A_=3						,					A_=32						,					A_=3						,					A_=10						,					A_=[10, 20, 30, 40]						,					A_=[1, 1, 2, 1]						,					A_=True						,					A_=True						,					A_="relu"						,					A_=3						,					A_=None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    parent
										_UpperCAmelCase       :		List[Any] 		=    batch_size
										_UpperCAmelCase       :		List[str] 		=    image_size
										_UpperCAmelCase       :		int 		=    num_channels
										_UpperCAmelCase       :		List[Any] 		=    embeddings_size
										_UpperCAmelCase       :		List[str] 		=    hidden_sizes
										_UpperCAmelCase       :		int 		=    depths
										_UpperCAmelCase       :		Optional[Any] 		=    is_training
										_UpperCAmelCase       :		Union[str, Any] 		=    use_labels
										_UpperCAmelCase       :		str 		=    hidden_act
										_UpperCAmelCase       :		Optional[int] 		=    num_labels
										_UpperCAmelCase       :		Optional[Any] 		=    scope
										_UpperCAmelCase       :		List[str] 		=    len(A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										_UpperCAmelCase       :		Dict 		=    self.get_config()

										return config, pixel_values


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										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        _UpperCAmelCase				(       self						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    FlaxRegNetModel(config=A_	)
										_UpperCAmelCase       :		Any 		=    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						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self.num_labels
										_UpperCAmelCase       :		Optional[int] 		=    FlaxRegNetForImageClassification(config=A_	)
										_UpperCAmelCase       :		Dict 		=    model(A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.prepare_config_and_inputs()
										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    config_and_inputs
										_UpperCAmelCase       :		str 		=    {"pixel_values": pixel_values}
										return config, inputs_dict







@require_flax
class 				a  (	UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

						_lowercase    						=			False
						_lowercase    						=			False
						_lowercase    						=			False


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    FlaxRegNetModelTester(self	)
										_UpperCAmelCase       :		Dict 		=    ConfigTester(self						,					config_class=A_						,					has_text_modality=A_	)


						def        _UpperCAmelCase				(       self	):







										'''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	):







										'''simple docstring'''


										return


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_	)


						@unittest.skip(reason="RegNet does not use inputs_embeds"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@unittest.skip(reason="RegNet does not support input and output embeddings"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

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

														_UpperCAmelCase       :		List[Any] 		=    ["pixel_values"]
														self.assertListEqual(arg_names[:1]						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										def check_hidden_states_output(A_						,					A_						,					A_	):
														_UpperCAmelCase       :		int 		=    model_class(A_	)

														_UpperCAmelCase       :		Any 		=    model(**self._prepare_for_class(A_						,					A_	)	)

														_UpperCAmelCase       :		Union[str, Any] 		=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_UpperCAmelCase       :		int 		=    self.model_tester.num_stages
														self.assertEqual(len(A_	)						,					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       :		List[Any] 		=    True
														check_hidden_states_output(A_						,					A_						,					A_	)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_UpperCAmelCase       :		Tuple 		=    True

														check_hidden_states_output(A_						,					A_						,					A_	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    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(A_						,					A_	)
																		_UpperCAmelCase       :		Tuple 		=    model_class(A_	)

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

																		with self.subTest("JIT Enabled"	):
																						_UpperCAmelCase       :		Any 		=    model_jitted(**A_	).to_tuple()

																		with self.subTest("JIT Disabled"	):
																						with jax.disable_jit():
																										_UpperCAmelCase       :		Optional[int] 		=    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 							__SCREAMING_SNAKE_CASE							(				)   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[Any] 		=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
				return image





@require_flax
class 				a  (	unittest.TestCase				):


						@cached_property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"	) if is_vision_available() else None





						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"	)

										_UpperCAmelCase       :		Optional[int] 		=    self.default_image_processor
										_UpperCAmelCase       :		int 		=    prepare_img()
										_UpperCAmelCase       :		Dict 		=    image_processor(images=A_						,					return_tensors="np"	)

										_UpperCAmelCase       :		Tuple 		=    model(**A_	)

										# verify the logits
										_UpperCAmelCase       :		List[Any] 		=    (1, 1000)
										self.assertEqual(outputs.logits.shape						,					A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    jnp.array([-0.41_80, -1.50_51, -3.48_36]	)

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





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_        =      'scheduler_config.json'

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			1
						_lowercase    						=			2
						_lowercase    						=			3
						_lowercase    						=			4
						_lowercase    						=			5







@dataclass
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42







class 				a  :
						_lowercase    						=			SCHEDULER_CONFIG_NAME
						_lowercase    						=			["dtype"]
						_lowercase    						=			[]
						_lowercase    						=			True


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_ = None						,					A_ = None						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    cls.load_config(
										    pretrained_model_name_or_path=A_						,					subfolder=A_						,					return_unused_kwargs=A_						,					**A_						,					)
										_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    cls.from_config(A_						,					return_unused_kwargs=A_						,					**A_	)

										if hasattr(A_						,					"create_state"	) and getattr(A_						,					"has_state"						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False						,					**A_	):







										'''simple docstring'''


										self.save_config(save_directory=A_						,					push_to_hub=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._get_compatibles()





						@classmethod
						def        _UpperCAmelCase				(       cls	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    list(set([cls.__name__] + cls._compatibles	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    importlib.import_module(__name__.split("."	)[0]	)
										_UpperCAmelCase       :		Dict 		=    [
										    getattr(A_						,					A_	) for c in compatible_classes_str if hasattr(A_						,					A_	)
										]
										return compatible_classes







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  Tuple[int] )   ->       jnp.ndarray:
				assert len(lowerCAmelCase ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase ) - x.ndim) )  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple=0.999  ,      lowerCAmelCase:  int=jnp.floataa )   ->       jnp.ndarray:

				def alpha_bar(lowerCAmelCase:  Union[str, Any] ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

				_UpperCAmelCase       :		str 		=    []
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Optional[int] 		=    i / num_diffusion_timesteps
								_UpperCAmelCase       :		str 		=    (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCAmelCase ) / alpha_bar(lowerCAmelCase )  ,      lowerCAmelCase ) )
				return jnp.array(lowerCAmelCase  ,      dtype=lowerCAmelCase )

@flax.struct.dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			42


						@classmethod
						def        _UpperCAmelCase				(       cls						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    scheduler.config

										if config.trained_betas is not None:
														_UpperCAmelCase       :		List[Any] 		=    jnp.asarray(config.trained_betas						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
														_UpperCAmelCase       :		List[Any] 		=    jnp.linspace(config.beta_start						,					config.beta_end						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_UpperCAmelCase       :		List[str] 		=    (
														    jnp.linspace(
														        config.beta_start**0.5						,					config.beta_end**0.5						,					config.num_train_timesteps						,					dtype=scheduler.dtype	)
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_UpperCAmelCase       :		str 		=    betas_for_alpha_bar(config.num_train_timesteps						,					dtype=scheduler.dtype	)
										else:
														raise NotImplementedError(
														    f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'	)

										_UpperCAmelCase       :		Optional[int] 		=    1.0 - betas

										_UpperCAmelCase       :		int 		=    jnp.cumprod(A_						,					axis=0	)

										return cls(
										    alphas=A_						,					betas=A_						,					alphas_cumprod=A_						,					)







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[int] 		=    state.alphas_cumprod

				_UpperCAmelCase       :		Optional[Any] 		=    alphas_cumprod[timesteps] ** 0.5
				_UpperCAmelCase       :		str 		=    sqrt_alpha_prod.flatten()
				_UpperCAmelCase       :		List[Any] 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				_UpperCAmelCase       :		Optional[int] 		=    (1 - alphas_cumprod[timesteps]) ** 0.5
				_UpperCAmelCase       :		List[Any] 		=    sqrt_one_minus_alpha_prod.flatten()
				_UpperCAmelCase       :		int 		=    broadcast_to_shape_from_left(lowerCAmelCase  ,      original_samples.shape )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       List[Any]:
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[Any] 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples





def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  CommonSchedulerState  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray  ,      lowerCAmelCase:  jnp.ndarray )   ->       Dict:
				_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    get_sqrt_alpha_prod(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity


