
'''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   a_  (unittest.TestCase    ):




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




       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  List[Any]   				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         _lowerCAmelCase       :  Optional[int]   				=					self.get_config()

         return config, pixel_values




       def 							__UpperCamelCase       (     self  ):
         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 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_  ):
         _lowerCAmelCase       :  Union[str, Any]   				=					FlaxResNetModel(config=snake_case_  )
         _lowerCAmelCase       :  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 // 3_2, self.image_size // 3_2)   ,   )




       def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_  ):
         _lowerCAmelCase       :  int   				=					self.num_labels
         _lowerCAmelCase       :  Any   				=					FlaxResNetForImageClassification(config=snake_case_  )
         _lowerCAmelCase       :  Any   				=					model(snake_case_  )
         self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)  )







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



@require_flax
class   a_  (_a							,			unittest.TestCase    ):
       __lowerCAmelCase    :				Optional[int]    =      (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       __lowerCAmelCase    :				Tuple    =      False
       __lowerCAmelCase    :				str    =      False
       __lowerCAmelCase    :				int    =      False




       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  int   				=					FlaxResNetModelTester(self  )
         _lowerCAmelCase       :  Any   				=					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  ):
         _lowerCAmelCase       :  List[Any]   				=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_  )




       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  Any   				=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case_  )




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




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




       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase				,					_lowerCAmelCase       :  Union[str, Any]   				=					self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowerCAmelCase       :  Any   				=					["""pixel_values"""]
           self.assertListEqual(arg_names[:1]   ,   snake_case_  )




       def 							__UpperCamelCase       (     self  ):
         def check_hidden_states_output(snake_case_   ,   snake_case_   ,   snake_case_  ):
           _lowerCAmelCase       :  Any   				=					model_class(snake_case_  )

           _lowerCAmelCase       :  Dict   				=					model(**self._prepare_for_class(snake_case_   ,   snake_case_  )  )

           _lowerCAmelCase       :  Optional[int]   				=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowerCAmelCase       :  str   				=					self.model_tester.num_stages
           self.assertEqual(len(snake_case_  )   ,   expected_num_stages + 1  )




       @unittest.skip(reason="""ResNet does not use feedforward chunking"""  )
       def 							__UpperCamelCase       (     self  ):
         pass







       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase				,					_lowerCAmelCase       :  Optional[Any]   				=					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
             _lowerCAmelCase       :  Any   				=					self._prepare_for_class(snake_case_   ,   snake_case_  )
             _lowerCAmelCase       :  int   				=					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"""  ):
               _lowerCAmelCase       :  int   				=					model_jitted(**snake_case_  ).to_tuple()

             with self.subTest("""JIT Disabled"""  ):
               with jax.disable_jit():
                 _lowerCAmelCase       :  Dict   				=					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 	_UpperCAmelCase				(  )    ->						Union[str, Any]:
  _lowerCAmelCase       :  Tuple   				=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
  return image



@require_flax
class   a_  (unittest.TestCase    ):




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







       @slow
       def 							__UpperCamelCase       (     self  ):
         _lowerCAmelCase       :  int   				=					FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""  )

         _lowerCAmelCase       :  List[str]   				=					self.default_image_processor
         _lowerCAmelCase       :  str   				=					prepare_img()
         _lowerCAmelCase       :  int   				=					image_processor(images=snake_case_   ,   return_tensors="""np"""  )

         _lowerCAmelCase       :  Union[str, Any]   				=					model(**snake_case_  )

         # verify the logits
         _lowerCAmelCase       :  int   				=					(1, 1_0_0_0)
         self.assertEqual(outputs.logits.shape   ,   snake_case_  )

         _lowerCAmelCase       :  str   				=					jnp.array([-11.1069, -9.7877, -8.3777]  )

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




'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase_     			=       {
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_     			=       [
         """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTSanJapaneseForConditionalGeneration""",
         """GPTSanJapaneseModel""",
         """GPTSanJapanesePreTrainedModel""",
     ]
     UpperCamelCase_     			=       [
         """GPTSanJapaneseTokenizer""",
     ]


if TYPE_CHECKING:
     from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
     from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptsan_japanese import (
              GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTSanJapaneseForConditionalGeneration,
              GPTSanJapaneseModel,
              GPTSanJapanesePreTrainedModel,
          )
          from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
     import sys

     UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


