

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 :       Union[str, Any],lowercase_ :       str,lowercase_ :       Optional[int]=3,lowercase_ :       Optional[Any]=3_2,lowercase_ :       str=3,lowercase_ :       List[str]=1_0,lowercase_ :       List[Any]=[1_0, 2_0, 3_0, 4_0],lowercase_ :       Dict=[1, 1, 2, 1],lowercase_ :       List[str]=True,lowercase_ :       Tuple=True,lowercase_ :       Optional[Any]="relu",lowercase_ :       Tuple=3,lowercase_ :       Any=None,)->    Tuple:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   num_channels
        A__          =   embeddings_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_act
        A__          =   num_labels
        A__          =   scope
        A__          =   len(lowercase_		)


    def 						snake_case__     (      self :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A__          =   self.get_config()

        return config, pixel_values


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''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 						snake_case__     (      self :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   FlaxRegNetModel(config=lowercase_		)
        A__          =   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 						snake_case__     (      self :       Any,lowercase_ :       int,lowercase_ :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   FlaxRegNetForImageClassification(config=lowercase_		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       Tuple		)->    str:





        '''simple docstring'''







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




@require_flax
class  A						(							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Tuple		)->    None:





        '''simple docstring'''







        A__          =   FlaxRegNetModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    List[Any]:





        '''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 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        return


    def 						snake_case__     (      self :       str		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   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 :       int		)->    Optional[Any]:





        '''simple docstring'''







        pass


    @unittest.skip(reason='RegNet does not support input and output embeddings'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       int		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        def check_hidden_states_output(lowercase_ :       List[str],lowercase_ :       List[Any],lowercase_ :       Optional[int]		):
            A__          =   model_class(lowercase_		)

            A__          =   model(**self._prepare_for_class(lowercase_,lowercase_		)		)

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

            A__          =   self.model_tester.num_stages
            self.assertEqual(len(lowercase_		),expected_num_stages + 1		)

        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   True
            check_hidden_states_output(lowercase_,lowercase_,lowercase_		)

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

            check_hidden_states_output(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                A__          =   self._prepare_for_class(lowercase_,lowercase_		)
                A__          =   model_class(lowercase_		)

                @jax.jit
                def model_jitted(lowercase_ :       int,**lowercase_ :       Dict		):
                    return model(pixel_values=lowercase_,**lowercase_		)

                with self.subTest('JIT Enabled'		):
                    A__          =   model_jitted(**lowercase_		).to_tuple()

                with self.subTest('JIT Disabled'		):
                    with jax.disable_jit():
                        A__          =   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 						_snake_case(    )							->      Dict:





    '''simple docstring'''






    A__          =   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 :       Dict		)->    Optional[int]:





        '''simple docstring'''







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


    @slow
    def 						snake_case__     (      self :       Union[str, Any]		)->    str:





        '''simple docstring'''







        A__          =   FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='np'		)

        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   jnp.array([-0.4_180, -1.5_051, -3.4_836]		)

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XGLMForCausalLM''',
					    '''XGLMModel''',
					    '''XGLMPreTrainedModel''',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxXGLMForCausalLM''',
					    '''FlaxXGLMModel''',
					    '''FlaxXGLMPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXGLMForCausalLM''',
					    '''TFXGLMModel''',
					    '''TFXGLMPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)