'''simple docstring'''




import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor

class   _lowercase(     unittest.TestCase				):



   """simple docstring"""





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





   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

    __UpperCAmelCase         =					self.get_config()

    return config, pixel_values





   def 			snake_case	(							self:    int	):
    return ResNetConfig(
        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:    Optional[int]			,a:    Dict			,a:    Any	):
    __UpperCAmelCase         =					FlaxResNetModel(config=a	)
    __UpperCAmelCase         =					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 			snake_case	(							self:    int			,a:    Tuple			,a:    str	):
    __UpperCAmelCase         =					self.num_labels
    __UpperCAmelCase         =					FlaxResNetForImageClassification(config=a	)
    __UpperCAmelCase         =					model(a	)
    self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)	)





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




@require_flax
class   _lowercase(     _lowerCamelCase   ,unittest.TestCase				):



   """simple docstring"""





   __lowerCamelCase    =  (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

   __lowerCamelCase    =  False
   __lowerCamelCase    =  False
   __lowerCamelCase    =  False





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					FlaxResNetModelTester(self	)
    __UpperCAmelCase         =					ConfigTester(self			,config_class=a			,has_text_modality=a	)





   def 			snake_case	(							self:    List[Any]	):
    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:    Any	):
    return





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a	)





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*a	)





   @unittest.skip(reason='ResNet does not use inputs_embeds'	)
   def 			snake_case	(							self:    Any	):
    pass





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





   def 			snake_case	(							self:    Any	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

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





   def 			snake_case	(							self:    List[str]	):
    def check_hidden_states_output(a:    Tuple			,a:    int			,a:    str	):
     __UpperCAmelCase         =					model_class(a	)

     __UpperCAmelCase         =					model(**self._prepare_for_class(a			,a	)	)

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

     __UpperCAmelCase         =					self.model_tester.num_stages
     self.assertEqual(len(a	)			,expected_num_stages + 1	)





   @unittest.skip(reason='ResNet does not use feedforward chunking'	)
   def 			snake_case	(							self:    Dict	):
    pass





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

      @jax.jit
      def model_jitted(a:    List[str]			,**a:    Optional[Any]	):
       return model(pixel_values=a			,**a	)

      with self.subTest('JIT Enabled'	):
       __UpperCAmelCase         =					model_jitted(**a	).to_tuple()

      with self.subTest('JIT Disabled'	):
       with jax.disable_jit():
        __UpperCAmelCase         =					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 						__snake_case (       ):
 __UpperCAmelCase         =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
 return image

@require_flax
class   _lowercase(     unittest.TestCase				):



   """simple docstring"""





   @cached_property
   def 			snake_case	(							self:    int	):
    return AutoImageProcessor.from_pretrained('microsoft/resnet-50'	) if is_vision_available() else None





   @slow
   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'	)

    __UpperCAmelCase         =					self.default_image_processor
    __UpperCAmelCase         =					prepare_img()
    __UpperCAmelCase         =					image_processor(images=a			,return_tensors='np'	)

    __UpperCAmelCase         =					model(**a	)

    # verify the logits
    __UpperCAmelCase         =					(1, 1000)
    self.assertEqual(outputs.logits.shape			,a	)

    __UpperCAmelCase         =					jnp.array([-11.1069, -9.7877, -8.3777]	)

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




'''simple docstring'''




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,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



