

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase						:   List[Any]										=    {'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase						:   Tuple										=    [
           '''UMT5EncoderModel''',
           '''UMT5ForConditionalGeneration''',
           '''UMT5ForQuestionAnswering''',
           '''UMT5Model''',
           '''UMT5PreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

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









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     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




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

       def UpperCAmelCase_						(						self       :						Dict							)			->    Union[str, Any]:
             snake_case__					=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             snake_case__					=   self.get_config()

             return config, pixel_values

       def UpperCAmelCase_						(						self       :						List[str]							)			->    List[Any]:
             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       :						str    ,		lowerCAmelCase__       :						List[str]    ,		lowerCAmelCase__       :						Optional[Any]							)			->    int:
             snake_case__					=   FlaxResNetModel(config=lowerCAmelCase__							)
             snake_case__					=   model(lowerCAmelCase__							)

             # 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       :						Optional[int]    ,		lowerCAmelCase__       :						Tuple    ,		lowerCAmelCase__       :						Optional[int]							)			->    Optional[int]:
             snake_case__					=   self.num_labels
             snake_case__					=   FlaxResNetForImageClassification(config=lowerCAmelCase__							)
             snake_case__					=   model(lowerCAmelCase__							)
             self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)							)

       def UpperCAmelCase_						(						self       :						int							)			->    List[Any]:
             snake_case__					=   self.prepare_config_and_inputs()
             snake_case__	,			snake_case__					=   config_and_inputs
             snake_case__					=   {"""pixel_values""": pixel_values}
             return config, inputs_dict



@require_flax
class     SCREAMING_SNAKE_CASE__     (						__a ,	unittest.TestCase		):





       '''simple docstring'''




       UpperCamelCase__ :     Any				    =       (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       UpperCamelCase__ :     int				    =       False
       UpperCamelCase__ :     int				    =       False
       UpperCamelCase__ :     int				    =       False

       def UpperCAmelCase_						(						self       :						Dict							)			->    None:
             snake_case__					=   FlaxResNetModelTester(self							)
             snake_case__					=   ConfigTester(self    ,		config_class=lowerCAmelCase__    ,		has_text_modality=lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						int							)			->    Union[str, 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 UpperCAmelCase_						(						self       :						Tuple							)			->    int:
             return

       def UpperCAmelCase_						(						self       :						Any							)			->    Dict:
             snake_case__					=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    str:
             snake_case__					=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)

       @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    Optional[Any]:
             pass

       @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
       def UpperCAmelCase_						(						self       :						Tuple							)			->    Dict:
             pass

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

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

                   snake_case__					=   ["""pixel_values"""]
                   self.assertListEqual(arg_names[:1]    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    Union[str, Any]:
             def check_hidden_states_output(lowerCAmelCase__       :						Dict    ,		lowerCAmelCase__       :						Tuple    ,		lowerCAmelCase__       :						str							):
                   snake_case__					=   model_class(lowerCAmelCase__							)

                   snake_case__					=   model(**self._prepare_for_class(lowerCAmelCase__    ,		lowerCAmelCase__							)							)

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

                   snake_case__					=   self.model_tester.num_stages
                   self.assertEqual(len(lowerCAmelCase__							)    ,		expected_num_stages + 1							)

       @unittest.skip(reason="""ResNet does not use feedforward chunking"""							)
       def UpperCAmelCase_						(						self       :						int							)			->    Optional[int]:
             pass

       def UpperCAmelCase_						(						self       :						List[Any]							)			->    Any:
             snake_case__	,			snake_case__					=   self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__							):
                         snake_case__					=   self._prepare_for_class(lowerCAmelCase__    ,		lowerCAmelCase__							)
                         snake_case__					=   model_class(lowerCAmelCase__							)

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

                         with self.subTest("""JIT Enabled"""							):
                               snake_case__					=   model_jitted(**lowerCAmelCase__							).to_tuple()

                         with self.subTest("""JIT Disabled"""							):
                               with jax.disable_jit():
                                     snake_case__					=   model_jitted(**lowerCAmelCase__							).to_tuple()

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





def 			_lowercase    (      ):
      snake_case__					=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      return image
@require_flax
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




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

       @slow
       def UpperCAmelCase_						(						self       :						str							)			->    str:
             snake_case__					=   FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""							)

             snake_case__					=   self.default_image_processor
             snake_case__					=   prepare_img()
             snake_case__					=   image_processor(images=lowerCAmelCase__    ,		return_tensors="""np"""							)

             snake_case__					=   model(**lowerCAmelCase__							)

             # verify the logits
             snake_case__					=   (1, 1000)
             self.assertEqual(outputs.logits.shape    ,		lowerCAmelCase__							)

             snake_case__					=   jnp.array([-11.1_069, -9.7_877, -8.3_777]							)

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






