


'''simple docstring'''







import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import EfficientNetForImageClassification, EfficientNetModel
   from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor


class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=13   ,							SCREAMING_SNAKE_CASE    :							List[str]=32   ,							SCREAMING_SNAKE_CASE    :							Any=3   ,							SCREAMING_SNAKE_CASE    :							Tuple=[3, 3, 5]   ,							SCREAMING_SNAKE_CASE    :							str=[32, 16, 24]   ,							SCREAMING_SNAKE_CASE    :							int=[16, 24, 40]   ,							SCREAMING_SNAKE_CASE    :							Any=[1, 1, 2]   ,							SCREAMING_SNAKE_CASE    :							int=[1, 1, 2]   ,							SCREAMING_SNAKE_CASE    :							List[Any]=[1, 6, 6]   ,							SCREAMING_SNAKE_CASE    :							str=True   ,							SCREAMING_SNAKE_CASE    :							Any=True   ,							SCREAMING_SNAKE_CASE    :							Any=37   ,							SCREAMING_SNAKE_CASE    :							str="gelu"   ,							SCREAMING_SNAKE_CASE    :							int=10   ,							):
           _A  :			Optional[int]											=      parent
           _A  :			Optional[int]											=      batch_size
           _A  :			str											=      image_size
           _A  :			str											=      num_channels
           _A  :			Optional[int]											=      kernel_sizes
           _A  :			Tuple											=      in_channels
           _A  :			Any											=      out_channels
           _A  :			int											=      strides
           _A  :			Any											=      num_block_repeats
           _A  :			int											=      expand_ratios
           _A  :			Optional[int]											=      is_training
           _A  :			Optional[int]											=      hidden_act
           _A  :			Tuple											=      num_labels
           _A  :			Union[str, Any]											=      use_labels


    def      A					(						self    :							Union[str, Any]):
           _A  :			int											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A  :			List[Any]											=      None
           if self.use_labels:
                  _A  :			Optional[int]											=      ids_tensor([self.batch_size]   ,							self.num_labels)

           _A  :			List[str]											=      self.get_config()
           return config, pixel_values, labels


    def      A					(						self    :							List[Any]):
           return EfficientNetConfig(
               num_channels=self.num_channels   ,							kernel_sizes=self.kernel_sizes   ,							in_channels=self.in_channels   ,							out_channels=self.out_channels   ,							strides=self.strides   ,							num_block_repeats=self.num_block_repeats   ,							expand_ratios=self.expand_ratios   ,							hidden_act=self.hidden_act   ,							num_labels=self.num_labels   ,							)


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			List[str]											=      EfficientNetModel(config=SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			int											=      model(SCREAMING_SNAKE_CASE)
           # expected last hidden states: B, C, H // 4, W // 4
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,							(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)   ,							)


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           _A  :			Any											=      EfficientNetForImageClassification(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			Tuple											=      model(SCREAMING_SNAKE_CASE   ,							labels=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels))


    def      A					(						self    :							Union[str, Any]):
           _A  :			Tuple											=      self.prepare_config_and_inputs()
           _A						,				_A						,				_A  :			List[Any]											=      config_and_inputs
           _A  :			Union[str, Any]											=      {'pixel_values': pixel_values}
           return config, inputs_dict




@require_torch
class 				__lowerCamelCase				( a_       ,      a_       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
    a									=					(
        {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
        if is_torch_available()
        else {}
    )

    a									=					False
    a									=					False
    a									=					False
    a									=					False
    a									=					False


    def      A					(						self    :							Tuple):
           _A  :			Optional[Any]											=      EfficientNetModelTester(self)
           _A  :			Optional[int]											=      ConfigTester(
               self   ,							config_class=SCREAMING_SNAKE_CASE   ,							has_text_modality=SCREAMING_SNAKE_CASE   ,							hidden_size=37)


    def      A					(						self    :							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    :							Any):
           return


    @unittest.skip(reason='EfficientNet does not use inputs_embeds')
    def      A					(						self    :							Dict):
           pass


    @unittest.skip(reason='EfficientNet does not support input and output embeddings')
    def      A					(						self    :							Optional[Any]):
           pass


    @unittest.skip(reason='EfficientNet does not use feedforward chunking')
    def      A					(						self    :							Dict):
           pass


    def      A					(						self    :							Tuple):
           _A						,				_A  :			List[str]											=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A  :			Union[str, Any]											=      model_class(SCREAMING_SNAKE_CASE)
                  _A  :			Optional[int]											=      inspect.signature(model.forward)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A  :			List[str]											=      [*signature.parameters.keys()]

                  _A  :			str											=      ['pixel_values']
                  self.assertListEqual(arg_names[:1]   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple):
           _A  :			str											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str):
           def check_hidden_states_output(SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							str):
                  _A  :			str											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()

                  with torch.no_grad():
                         _A  :			Any											=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE))

                  _A  :			str											=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
                  _A  :			Union[str, Any]											=      sum(config.num_block_repeats) * 4
                  self.assertEqual(len(SCREAMING_SNAKE_CASE)   ,							SCREAMING_SNAKE_CASE)

                  # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:])   ,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,							)

           _A						,				_A  :			str											=      self.model_tester.prepare_config_and_inputs_for_common()

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

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  _A  :			int											=      True

                  check_hidden_states_output(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Union[str, Any]):
           _A  :			Any											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
    def      A					(						self    :							Optional[Any]):
           pass


    @slow
    def      A					(						self    :							List[str]):
           for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A  :			str											=      EfficientNetModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)


    @is_pipeline_test
    @require_vision
    @slow
    def      A					(						self    :							Optional[int]):
           super().test_pipeline_image_classification()






def    lowerCAmelCase__ (       ):
       _A  :			Dict											=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image


@require_torch
@require_vision
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    @cached_property
    def      A					(						self    :							int):
           return AutoImageProcessor.from_pretrained('google/efficientnet-b7') if is_vision_available() else None


    @slow
    def      A					(						self    :							Dict):
           _A  :			Optional[int]											=      EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7').to(SCREAMING_SNAKE_CASE)

           _A  :			List[Any]											=      self.default_image_processor
           _A  :			Optional[Any]											=      prepare_img()
           _A  :			List[str]											=      image_processor(images=SCREAMING_SNAKE_CASE   ,							return_tensors='pt').to(SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
                  _A  :			Union[str, Any]											=      model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A  :			List[Any]											=      torch.Size((1, 1000))
           self.assertEqual(outputs.logits.shape   ,							SCREAMING_SNAKE_CASE)

           _A  :			Any											=      torch.tensor([0.0001, 0.0002, 0.0002]).to(SCREAMING_SNAKE_CASE)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,							SCREAMING_SNAKE_CASE   ,							atol=1e-4))










'''simple docstring'''







import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


A   :  List[Any]       		=				{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
A   :  Optional[int]       		=				set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))






def    lowerCAmelCase__ (       lowerCamelCase  :    str						,lowerCamelCase  :    Dict=0						,lowerCamelCase  :    List[Any]=None     ):
       return [" ".join(x.split(' '     )[begin:end]     ) for x in answers if len(x.split(' '     )     ) > 1]






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict						,lowerCamelCase  :    Tuple=False     ):
       if make_sub_answers:
              # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
              # *e.g.* if the correct answer contains a prefix such as "the", or "a"
              _A  :			str											=      (
                  given_answers + get_sub_answers(lowerCamelCase						,begin=1     ) + get_sub_answers(lowerCamelCase						,end=-1     )
              )
       _A  :			List[Any]											=      []
       for answer in given_answers:
              _A  :			List[str]											=      answer.replace('_'						,' '     ).lower()
              _A  :			Any											=      ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias     )
              answers.append(' '.join(alias.split()     ).strip()     )
       return set(lowerCamelCase     )






def    lowerCAmelCase__ (       lowerCamelCase  :    List[Any]						,lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    List[str]=1						,lowerCamelCase  :    Optional[int]=100     ):
       _A						,				_A  :			Dict											=      jax.lax.top_k(lowerCamelCase						,lowerCamelCase     )
       _A						,				_A  :			Dict											=      jax.lax.top_k(lowerCamelCase						,lowerCamelCase     )

       _A  :			List[str]											=      best_end_idx[:, None] - best_start_idx[None, :]
       _A  :			Any											=      jnp.logical_or(widths < 0						,widths > max_size     )
       _A  :			Any											=      (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
       _A  :			List[Any]											=      jnp.argmax(lowerCamelCase     ).item()

       return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict     ):
       _A  :			Optional[Any]											=      sample['question']['text']
       _A  :			Any											=      sample['document']['tokens']['token']
       _A  :			List[str]											=      sample['document']['tokens']['is_html']
       _A  :			str											=      sample['annotations']['long_answer']
       _A  :			int											=      sample['annotations']['short_answers']

       _A  :			Optional[int]											=      ' '.join([context[i] for i in range(len(lowerCamelCase     )     ) if not is_html[i]]     )

       # 0 - No ; 1 - Yes
       for answer in sample["annotations"]["yes_no_answer"]:
              if answer == 0 or answer == 1:
                     return {
                         "question": question,
                         "context": context_string,
                         "short": [],
                         "long": [],
                         "category": "no" if answer == 0 else "yes",
                     }

       _A  :			Tuple											=      []
       for s in short_answers:
              short_targets.extend(s['text']     )
       _A  :			Dict											=      list(set(lowerCamelCase     )     )

       _A  :			List[str]											=      []
       for s in long_answers:
              if s["start_token"] == -1:
                     continue
              _A  :			Tuple											=      context[s['start_token'] : s['end_token']]
              _A  :			Optional[Any]											=      is_html[s['start_token'] : s['end_token']]
              _A  :			Optional[int]											=      ' '.join([answer[i] for i in range(len(lowerCamelCase     )     ) if not html[i]]     )
              if new_answer not in long_targets:
                     long_targets.append(lowerCamelCase     )

       _A  :			List[str]											=      'long_short' if len(short_targets + long_targets     ) > 0 else 'null'

       return {
           "question": question,
           "context": context_string,
           "short": short_targets,
           "long": long_targets,
           "category": category,
       }






def    lowerCAmelCase__ (       ):
       _A  :			Optional[Any]											=      load_from_disk('natural-questions-validation'     )
       _A  :			int											=      dataset.map(lowerCamelCase     ).remove_columns(['annotations', 'document', 'id']     )
       print(lowerCamelCase     )

       _A  :			int											=      dataset.filter(lambda lowerCamelCase     : (len(x['question']     ) + len(x['context']     )) < 4 * 4096     )
       _A  :			Dict											=      short_validation_dataset.filter(lambda lowerCamelCase     : x["category"] != "null"     )
       short_validation_dataset

       _A  :			Tuple											=      'vasudevgupta/flax-bigbird-natural-questions'
       _A  :			List[str]											=      FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase     )
       _A  :			str											=      BigBirdTokenizerFast.from_pretrained(lowerCamelCase     )

       @jax.jit
       def forward(*lowerCamelCase  :    Optional[Any]						,**lowerCamelCase  :    Any     ):
              _A						,				_A						,				_A  :			Tuple											=      model(*lowerCamelCase						,**lowerCamelCase     )
              return start_logits, end_logits, jnp.argmax(lowerCamelCase						,axis=-1     )

       def evaluate(lowerCamelCase  :    Union[str, Any]     ):
              # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
              _A  :			List[str]											=      tokenizer(
                  example['question']						,example['context']						,return_tensors='np'						,max_length=4096						,padding='max_length'						,truncation=lowerCamelCase						,)

              _A						,				_A						,				_A  :			Tuple											=      forward(**lowerCamelCase     )

              _A  :			List[Any]											=      CATEGORY_MAPPING[category.item()]

              _A  :			List[str]											=      example['long'] + example['short']
              if example["category"] in ["yes", "no", "null"]:
                     _A  :			Optional[int]											=      [example['category']]
              _A  :			List[Any]											=      example['category'] != 'null'
              # Now target can be: "yes", "no", "null", "list of long & short answers"

              if predicted_category in ["yes", "no", "null"]:
                     _A  :			Tuple											=      [predicted_category]
                     _A  :			Union[str, Any]											=      example['output'] == example['targets']
                     _A  :			Tuple											=      predicted_category != 'null'
                     return example

              _A  :			Dict											=      38 if predicted_category == 'short' else 1024
              _A						,				_A  :			List[Any]											=      get_best_valid_start_end_idx(
                  start_scores[0]						,end_scores[0]						,top_k=8						,max_size=lowerCamelCase     )

              _A  :			Optional[Any]											=      inputs['input_ids'][0].tolist()
              _A  :			Union[str, Any]											=      [tokenizer.decode(input_ids[start_score : end_score + 1]     )]

              _A  :			Union[str, Any]											=      expand_to_aliases(example['targets']						,make_sub_answers=lowerCamelCase     )
              _A  :			str											=      expand_to_aliases(example['output']     )

              # some preprocessing to both prediction and answer
              _A  :			Tuple											=      {''.join(a.split()     ) for a in answers}
              _A  :			Optional[Any]											=      {''.join(p.split()     ) for p in predictions}
              _A  :			Tuple											=      {s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

              # if there is a common element, it's a exact match
              _A  :			Tuple											=      len(list(answers & predictions     )     ) > 0
              _A  :			Any											=      predicted_category != 'null' and len(lowerCamelCase     ) > 0

              return example

       _A  :			str											=      short_validation_dataset.map(lowerCamelCase     )

       _A  :			Optional[int]											=      len(lowerCamelCase     )
       _A  :			str											=      len(short_validation_dataset.filter(lambda lowerCamelCase     : x["match"] == 1     )     )
       print('EM score:'						,(matched / total) * 100						,'%'     )


if __name__ == "__main__":
   main()






