
"""simple docstring"""

import string
from math import logaa




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					document.translate(
        str.maketrans('' ,				'' ,				string.punctuation ) ).replace('\n' ,				'' )
    UpperCAmelCase									=					document_without_punctuation.split(' ' )  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()] )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[Any] ) ->      tuple[int, int]:






    """simple docstring"""




    UpperCAmelCase									=					corpus.lower().translate(
        str.maketrans('' ,				'' ,				string.punctuation ) )  # strip all punctuation and replace it with ''
    UpperCAmelCase									=					corpus_without_punctuation.split('\n' )
    UpperCAmelCase									=					term.lower()
    return (len([doc for doc in docs if term in doc] ), len(_A ))




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Union[str, Any]=False ) ->      float:






    """simple docstring"""




    if smoothing:
        if n == 0:
            raise ValueError('log10(0) is undefined.' )
        return round(1 + logaa(n / (1 + df) ) ,				3 )

    if df == 0:
        raise ZeroDivisionError('df must be > 0' )
    elif n == 0:
        raise ValueError('log10(0) is undefined.' )
    return round(logaa(n / df ) ,				3 )







def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					List[str] ) ->      float:






    """simple docstring"""




    return round(tf * idf ,				3 )





"""simple docstring"""

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   ,       lowercase_   ,       lowercase_=3   ,       lowercase_=3_2   ,       lowercase_=3   ,       lowercase_=1_0   ,       lowercase_=[1_0, 2_0, 3_0, 4_0]   ,       lowercase_=[1, 1, 2, 1]   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_="relu"   ,       lowercase_=3   ,       lowercase_=None   ,       )						->					str:
          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(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          UpperCAmelCase									=					self.get_config()

          return config, pixel_values




      def     a_      (							self							)						->					Optional[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     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          UpperCAmelCase									=					FlaxRegNetModel(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)

          # 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     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.num_labels
          UpperCAmelCase									=					FlaxRegNetForImageClassification(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'pixel_values': pixel_values}
          return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	False
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	False
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	False




      def     a_      (							self							)						->					None:
          UpperCAmelCase									=					FlaxRegNetModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_							)




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




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_							)




      @unittest.skip(reason='RegNet does not use inputs_embeds'							)
      def     a_      (							self							)						->					List[str]:
          pass




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




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase									=					model_class(lowercase_							)
              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]   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          def check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					model_class(lowercase_							)

              UpperCAmelCase									=					model(**self._prepare_for_class(lowercase_   ,       lowercase_							)							)

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

              UpperCAmelCase									=					self.model_tester.num_stages
              self.assertEqual(len(lowercase_							)   ,       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(lowercase_   ,       lowercase_   ,       lowercase_							)

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

              check_hidden_states_output(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          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(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

                  @jax.jit
                  def model_jitted(lowercase_   ,       **lowercase_							):
                      return model(pixel_values=lowercase_   ,       **lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

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




def     lowercase__						(					) ->      Tuple:






    """simple docstring"""




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







@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




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




      @slow
      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'							)

          UpperCAmelCase									=					self.default_image_processor
          UpperCAmelCase									=					prepare_img()
          UpperCAmelCase									=					image_processor(images=lowercase_   ,       return_tensors='np'							)

          UpperCAmelCase									=					model(**lowercase_							)

          # verify the logits
          UpperCAmelCase									=					(1, 1_0_0_0)
          self.assertEqual(outputs.logits.shape   ,       lowercase_							)

          UpperCAmelCase									=					jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

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



