

'''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 					_snake_case		(unittest.TestCase):






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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

				UpperCAmelCase_     :      List[str]						=  self.get_config()

				return config, pixel_values






			def 		UpperCamelCase__							(  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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      int						=  FlaxRegNetModel(config=_snake_case     )
				UpperCAmelCase_     :      int						=  model(_snake_case     )

				# 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			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      str						=  self.num_labels
				UpperCAmelCase_     :      int						=  FlaxRegNetForImageClassification(config=_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  model(_snake_case     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)     )






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




@require_flax
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Union[str, Any]        =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

			__A  :	Any        =False
			__A  :	List[str]        =False
			__A  :	Union[str, Any]        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  FlaxRegNetModelTester(self     )
				UpperCAmelCase_     :      Tuple						=  ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case     )






			def 		UpperCamelCase__							(  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 		UpperCamelCase__							(  self     ):
				return






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case     )






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






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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCAmelCase_     :      Union[str, Any]						=  ["pixel_values"]
					self.assertListEqual(arg_names[:1]			,_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				def check_hidden_states_output(_snake_case			,_snake_case			,_snake_case     ):
					UpperCAmelCase_     :      str						=  model_class(_snake_case     )

					UpperCAmelCase_     :      Any						=  model(**self._prepare_for_class(_snake_case			,_snake_case     )     )

					UpperCAmelCase_     :      Dict						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					UpperCAmelCase_     :      Tuple						=  self.model_tester.num_stages
					self.assertEqual(len(_snake_case     )			,expected_num_stages + 1     )

				UpperCAmelCase_					,					UpperCAmelCase_     :      int						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_     :      Tuple						=  True
					check_hidden_states_output(_snake_case			,_snake_case			,_snake_case     )

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

					check_hidden_states_output(_snake_case			,_snake_case			,_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__     ):
						UpperCAmelCase_     :      Optional[Any]						=  self._prepare_for_class(_snake_case			,_snake_case     )
						UpperCAmelCase_     :      Union[str, Any]						=  model_class(_snake_case     )

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

						with self.subTest("JIT Enabled"     ):
							UpperCAmelCase_     :      Tuple						=  model_jitted(**_snake_case     ).to_tuple()

						with self.subTest("JIT Disabled"     ):
							with jax.disable_jit():
								UpperCAmelCase_     :      Optional[int]						=  model_jitted(**_snake_case     ).to_tuple()

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




def  a__       (    )     ->					str:


	"""simple docstring"""





	UpperCAmelCase_     :      List[Any]						=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
	return image





@require_flax
class 					_snake_case		(unittest.TestCase):






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






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"     )

				UpperCAmelCase_     :      Dict						=  self.default_image_processor
				UpperCAmelCase_     :      List[Any]						=  prepare_img()
				UpperCAmelCase_     :      Tuple						=  image_processor(images=_snake_case			,return_tensors="np"     )

				UpperCAmelCase_     :      int						=  model(**_snake_case     )

				# verify the logits
				UpperCAmelCase_     :      Tuple						=  (1, 10_00)
				self.assertEqual(outputs.logits.shape			,_snake_case     )

				UpperCAmelCase_     :      Any						=  jnp.array([-0.4180, -1.5051, -3.4836]     )

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






"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `max_length` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': (
				            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
				            'to the `num_beams` value of the model configuration.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



