

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   lowercase_   (  unittest.TestCase					):



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



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _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				:    Optional[int]		)   ->					List[Any]:
        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 						UpperCamelCase_		(      self				:    str      , A__				:    Optional[int]      , A__				:    int		)   ->					Tuple:
        _snake_case						=							FlaxRegNetModel(config=A__		)
        _snake_case						=							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 						UpperCamelCase_		(      self				:    Any      , A__				:    Tuple      , A__				:    List[str]		)   ->					int:
        _snake_case						=							self.num_labels
        _snake_case						=							FlaxRegNetForImageClassification(config=A__		)
        _snake_case						=							model(A__		)
        self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)		)







      def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _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   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		List[Any]										=    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      UpperCamelCase_      :		List[Any]										=    False
      UpperCamelCase_      :		int										=    False
      UpperCamelCase_      :		str										=    False



      def 						UpperCamelCase_		(      self				:    Any		)   ->					None:
        _snake_case						=							FlaxRegNetModelTester(self		)
        _snake_case						=							ConfigTester(self      , config_class=A__      , has_text_modality=A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[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				:    Any		)   ->					Optional[int]:
        return



      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[Any]:
        _snake_case						=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
        _snake_case						=							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 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[int]:
        pass



      @unittest.skip(reason='''RegNet does not support input and output embeddings'''		)
      def 						UpperCamelCase_		(      self				:    str		)   ->					List[str]:
        pass



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					List[str]:
        _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(A__		)
          _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]      , A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[int]:
        def check_hidden_states_output(A__				:    Optional[int]      , A__				:    Optional[int]      , A__				:    Optional[Any]		):
          _snake_case						=							model_class(A__		)

          _snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)

          _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(A__		)      , expected_num_stages + 1		)

        _snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _snake_case						=							True
          check_hidden_states_output(A__      , A__      , A__		)

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

          check_hidden_states_output(A__      , A__      , A__		)







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					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(A__      , A__		)
            _snake_case						=							model_class(A__		)

            @jax.jit
            def model_jitted(A__				:    Any      , **A__				:    Tuple		):
              return model(pixel_values=A__      , **A__		)

            with self.subTest('''JIT Enabled'''		):
              _snake_case						=							model_jitted(**A__		).to_tuple()

            with self.subTest('''JIT Disabled'''		):
              with jax.disable_jit():
                _snake_case						=							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_()							->     int:



  """simple docstring"""






  _snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
  return image



@require_flax
class   lowercase_   (  unittest.TestCase					):



      @cached_property
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''		) if is_vision_available() else None







      @slow
      def 						UpperCamelCase_		(      self				:    Any		)   ->					Dict:
        _snake_case						=							FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''		)

        _snake_case						=							self.default_image_processor
        _snake_case						=							prepare_img()
        _snake_case						=							image_processor(images=A__      , return_tensors='''np'''		)

        _snake_case						=							model(**A__		)

        # verify the logits
        _snake_case						=							(1, 1000)
        self.assertEqual(outputs.logits.shape      , A__		)

        _snake_case						=							jnp.array([-0.4180, -1.5051, -3.4836]		)

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




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

