


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     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





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




     """simple docstring"""
     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 					lowerCAmelCase__  (     self       :       str  )   -> List[Any]:




     """simple docstring"""
     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 					lowerCAmelCase__  (     self       :       List[str]  )   -> int:




     """simple docstring"""
     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 					lowerCAmelCase__  (     self       :       Optional[int]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Optional[int]  )   -> Tuple:




     """simple docstring"""
     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 // 3_2, self.image_size // 3_2)    ,				)



  def 					lowerCAmelCase__  (     self       :       List[Any]    ,				_lowerCAmelCase       :       List[str]    ,				_lowerCAmelCase       :       Dict  )   -> List[str]:




     """simple docstring"""
     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 					lowerCAmelCase__  (     self       :       Union[str, Any]  )   -> List[Any]:




     """simple docstring"""
     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     __lowerCAmelCase     ( a ,  unittest.TestCase					):






  """simple docstring"""





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

  _SCREAMING_SNAKE_CASE		    =       False
  _SCREAMING_SNAKE_CASE		    =       False
  _SCREAMING_SNAKE_CASE		    =       False



  def 					lowerCAmelCase__  (     self       :       str  )   -> None:




     """simple docstring"""
     snake_case_					    =					FlaxResNetModelTester(self  )
     snake_case_					    =					ConfigTester(self    ,				config_class=_lowerCAmelCase    ,				has_text_modality=_lowerCAmelCase  )



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




     """simple docstring"""
     return



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




     """simple docstring"""
     snake_case_					    =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> str:




     """simple docstring"""
     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 					lowerCAmelCase__  (     self       :       Dict  )   -> Union[str, Any]:




     """simple docstring"""
     pass



  @unittest.skip(reason="ResNet does not support input and output embeddings"  )
  def 					lowerCAmelCase__  (     self       :       int  )   -> List[Any]:




     """simple docstring"""
     pass



  def 					lowerCAmelCase__  (     self       :       str  )   -> Optional[int]:




     """simple docstring"""
     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 					lowerCAmelCase__  (     self       :       int  )   -> Tuple:




     """simple docstring"""
     def check_hidden_states_output(_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       Dict    ,				_lowerCAmelCase       :       int  ):
        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 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     pass



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




     """simple docstring"""
     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       :       Dict    ,				**_lowerCAmelCase       :       Tuple  ):
              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 			_lowerCAmelCase						(							)->int:
   '''simple docstring'''






   snake_case_					    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
   return image



@require_flax
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  @cached_property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Union[str, Any]:




     """simple docstring"""
     return AutoImageProcessor.from_pretrained("microsoft/resnet-50"  ) if is_vision_available() else None



  @slow
  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     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, 1_0_0_0)
     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  )  )








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'longt5'
  _SCREAMING_SNAKE_CASE		    =       ['past_key_values']
  _SCREAMING_SNAKE_CASE		    =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

     snake_case_					    =					self.feed_forward_proj.split("-"  )
     snake_case_					    =					act_info[-1]
     snake_case_					    =					act_info[0] == "gated"

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"  )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_					    =					"gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase    ,				eos_token_id=_lowerCAmelCase    ,				is_encoder_decoder=_lowerCAmelCase    ,				**_lowerCAmelCase    ,				)






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase    ,				direction="inputs"  )

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




