


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 							_UpperCamelCase					(	unittest.TestCase     ):

       def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: List[str]=3					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=32					,   _SCREAMING_SNAKE_CASE: List[str]=3					,   _SCREAMING_SNAKE_CASE: Any=10					,   _SCREAMING_SNAKE_CASE: str=[10, 20, 30, 40]					,   _SCREAMING_SNAKE_CASE: Any=[1, 1, 2, 1]					,   _SCREAMING_SNAKE_CASE: Any=True					,   _SCREAMING_SNAKE_CASE: str=True					,   _SCREAMING_SNAKE_CASE: Dict="relu"					,   _SCREAMING_SNAKE_CASE: Tuple=3					,   _SCREAMING_SNAKE_CASE: List[Any]=None					,   )       -> Any:




              """simple docstring"""


              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 lowercase (		self: List[str]		)       -> str:




              """simple docstring"""


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

              UpperCamelCase_          =  self.get_config()

              return config, pixel_values

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




              """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 lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  FlaxRegNetModel(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 lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> int:




              """simple docstring"""


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



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




              """simple docstring"""


              UpperCamelCase_          =  self.prepare_config_and_inputs()
              UpperCamelCase_       , UpperCamelCase_          =  config_and_inputs
              UpperCamelCase_          =  {"pixel_values": pixel_values}
              return config, inputs_dict



@require_flax
class 							_UpperCamelCase					(	__lowercase  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Dict         =   (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       _UpperCamelCase				:					Tuple         =   False
       _UpperCamelCase				:					Union[str, Any]         =   False
       _UpperCamelCase				:					Tuple         =   False

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  FlaxRegNetModelTester(self		)
              UpperCamelCase_          =  ConfigTester(self					,   config_class=_A					,   has_text_modality=_A		)

       def lowercase (		self: Any		)       -> 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 lowercase (		self: List[str]		)       -> Tuple:




              """simple docstring"""


              return

       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_A		)

       def lowercase (		self: str		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*_A		)

       @unittest.skip(reason="RegNet does not use inputs_embeds"		)
       def lowercase (		self: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              pass

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




              """simple docstring"""


              pass

       def lowercase (		self: Tuple		)       -> List[Any]:




              """simple docstring"""


              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 lowercase (		self: Optional[Any]		)       -> str:




              """simple docstring"""


              def check_hidden_states_output(_SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]		):
                     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		)

              UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase_          =  True
                     check_hidden_states_output(_A					,   _A					,   _A		)

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

                     check_hidden_states_output(_A					,   _A					,   _A		)



       def lowercase (		self: Optional[int]		)       -> Any:




              """simple docstring"""


              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(_SCREAMING_SNAKE_CASE: Any					,   **_SCREAMING_SNAKE_CASE: Dict		):
                                   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  lowerCAmelCase_ (							)     ->		List[Any]:
       UpperCamelCase_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image







@require_flax
class 							_UpperCamelCase					(	unittest.TestCase     ):

       @cached_property
       def lowercase (		self: str		)       -> List[Any]:




              """simple docstring"""


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



       @slow
       def lowercase (		self: str		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"		)

              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([-0.41_80, -1.50_51, -3.48_36]		)

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








import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'T5Config'




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		jnp.ndarray:
       UpperCamelCase_          =  jnp.zeros_like(UpperCamelCase_  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]  )
       UpperCamelCase_          =  shifted_input_ids.at[:, 0].set(UpperCamelCase_  )

       UpperCamelCase_          =  jnp.where(shifted_input_ids == -100					,		UpperCamelCase_					,		UpperCamelCase_  )
       return shifted_input_ids



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Union[str, Any]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   '''mt5'''
       _UpperCamelCase				:					Optional[int]         =   MTaConfig



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   '''mt5'''
       _UpperCamelCase				:					List[Any]         =   MTaConfig

