





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
    )
    from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor

class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				Dict    ,      a__	:				Dict    ,      a__	:				Dict=13    ,      a__	:				Any=30    ,      a__	:				List[Any]=2    ,      a__	:				Optional[Any]=3    ,      a__	:				Any=True    ,      a__	:				str=True    ,      a__	:				int=32    ,      a__	:				Optional[int]=2    ,      a__	:				Union[str, Any]=4    ,      a__	:				Dict=37    ,      a__	:				Any="gelu"    ,      a__	:				Tuple=0.1    ,      a__	:				Union[str, Any]=0.1    ,      a__	:				List[str]=10    ,      a__	:				Dict=0.0_2    ,      a__	:				str=3    ,      a__	:				Dict=None    ,      a__	:				Any=2    ,      ):





         """simple docstring"""



         __snake_case								=       parent
         __snake_case								=       batch_size
         __snake_case								=       image_size
         __snake_case								=       patch_size
         __snake_case								=       num_channels
         __snake_case								=       is_training
         __snake_case								=       use_labels
         __snake_case								=       hidden_size
         __snake_case								=       num_hidden_layers
         __snake_case								=       num_attention_heads
         __snake_case								=       intermediate_size
         __snake_case								=       hidden_act
         __snake_case								=       hidden_dropout_prob
         __snake_case								=       attention_probs_dropout_prob
         __snake_case								=       type_sequence_label_size
         __snake_case								=       initializer_range
         __snake_case								=       scope
         __snake_case								=       encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         __snake_case								=       (image_size // patch_size) ** 2
         __snake_case								=       num_patches + 2


      def        a			(self	:				Any				):





         """simple docstring"""



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

         __snake_case								=       None
         if self.use_labels:
            __snake_case								=       ids_tensor([self.batch_size]    ,      self.type_sequence_label_size				)

         __snake_case								=       self.get_config()

         return config, pixel_values, labels


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         return DeiTConfig(
             image_size=self.image_size    ,      patch_size=self.patch_size    ,      num_channels=self.num_channels    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      is_decoder=_SCREAMING_SNAKE_CASE    ,      initializer_range=self.initializer_range    ,      encoder_stride=self.encoder_stride    ,      )


      def        a			(self	:				List[Any]    ,      a__	:				Tuple    ,      a__	:				Dict    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       TFDeiTModel(config=_SCREAMING_SNAKE_CASE				)
         __snake_case								=       model(_SCREAMING_SNAKE_CASE				)
         self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)				)


      def        a			(self	:				int    ,      a__	:				Union[str, Any]    ,      a__	:				int    ,      a__	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE				)
         __snake_case								=       model(_SCREAMING_SNAKE_CASE				)
         self.parent.assertEqual(
             result.reconstruction.shape    ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

         # test greyscale images
         __snake_case								=       1
         __snake_case								=       TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE				)

         __snake_case								=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         __snake_case								=       model(_SCREAMING_SNAKE_CASE				)
         self.parent.assertEqual(result.reconstruction.shape    ,      (self.batch_size, 1, self.image_size, self.image_size)				)


      def        a			(self	:				Optional[int]    ,      a__	:				Dict    ,      a__	:				Dict    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.type_sequence_label_size
         __snake_case								=       TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)
         __snake_case								=       model(_SCREAMING_SNAKE_CASE    ,      labels=_SCREAMING_SNAKE_CASE				)
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)				)

         # test greyscale images
         __snake_case								=       1
         __snake_case								=       TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)

         __snake_case								=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
         __snake_case								=       model(_SCREAMING_SNAKE_CASE    ,      labels=_SCREAMING_SNAKE_CASE				)
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



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






@require_tf
class 		SCREAMING_SNAKE_CASE__    (	__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase  ):
      A_						:    str							=   (
          (
              TFDeiTModel,
              TFDeiTForImageClassification,
              TFDeiTForImageClassificationWithTeacher,
              TFDeiTForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      A_						:    Optional[int]							=   (
          {
              'feature-extraction': TFDeiTModel,
              'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
          }
          if is_tf_available()
          else {}
      )

      A_						:    str							=   False
      A_						:    int							=   False
      A_						:    Union[str, Any]							=   False
      A_						:    Any							=   False


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       TFDeiTModelTester(self				)
         __snake_case								=       ConfigTester(self    ,      config_class=_SCREAMING_SNAKE_CASE    ,      has_text_modality=_SCREAMING_SNAKE_CASE    ,      hidden_size=37				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         self.config_tester.run_common_tests()


      @unittest.skip(reason='''DeiT does not use inputs_embeds'''				)
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         pass


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case								=       model_class(_SCREAMING_SNAKE_CASE				)
            self.assertIsInstance(model.get_input_embeddings()    ,      (tf.keras.layers.Layer)				)
            __snake_case								=       model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE    ,      tf.keras.layers.Dense				)				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case								=       model_class(_SCREAMING_SNAKE_CASE				)
            __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]    ,      _SCREAMING_SNAKE_CASE				)


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)


      def        a			(self	:				List[Any]    ,      a__	:				Dict    ,      a__	:				Tuple    ,      a__	:				Union[str, Any]=False				):





         """simple docstring"""



         __snake_case								=       super()._prepare_for_class(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE    ,      return_labels=_SCREAMING_SNAKE_CASE				)

         if return_labels:
            if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call				).parameters:
               del inputs_dict["labels"]

         return inputs_dict


      @slow
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case								=       TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
            self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)






def lowerCamelCase__   (							)				->    Union[str, Any]:
   __snake_case								=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
   return image







@require_tf
@require_vision
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      @cached_property
      def        a			(self	:				Tuple				):





         """simple docstring"""



         return (
             DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''				)
             if is_vision_available()
             else None
         )


      @slow
      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''				)

         __snake_case								=       self.default_image_processor
         __snake_case								=       prepare_img()
         __snake_case								=       image_processor(images=_SCREAMING_SNAKE_CASE    ,      return_tensors='''tf'''				)

         # forward pass
         __snake_case								=       model(**_SCREAMING_SNAKE_CASE				)

         # verify the logits
         __snake_case								=       tf.TensorShape((1, 1000)				)
         self.assertEqual(outputs.logits.shape    ,      _SCREAMING_SNAKE_CASE				)

         __snake_case								=       tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]				)

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








from __future__ import annotations

import collections
import pprint
from pathlib import Path






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return "".join(sorted(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    list[str]:
   return word_by_signature[signature(snake_case_	)]


snake_case_           =      Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
snake_case_           =      sorted({word.strip().lower() for word in data.splitlines()})

snake_case_           =      collections.defaultdict(list)
for word in word_list:
    word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
    snake_case_           =      {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

    with open('anagrams.txt', 'w') as file:
        file.write('all_anagrams = \n ')
        file.write(pprint.pformat(all_anagrams))

