

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
      import tensorflow as tf

      from transformers import TFBlipTextModel
      from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class    _a :




      """simple docstring"""

      def __init__(					self				:							Tuple		,       UpperCAmelCase				:							str		,       UpperCAmelCase				:							Any=12		,       UpperCAmelCase				:							List[str]=7		,       UpperCAmelCase				:							Tuple=True		,       UpperCAmelCase				:							Optional[int]=True		,       UpperCAmelCase				:							Optional[int]=True		,       UpperCAmelCase				:							Dict=99		,       UpperCAmelCase				:							Union[str, Any]=32		,       UpperCAmelCase				:							Union[str, Any]=32		,       UpperCAmelCase				:							Optional[Any]=2		,       UpperCAmelCase				:							int=4		,       UpperCAmelCase				:							Dict=37		,       UpperCAmelCase				:							Optional[int]=0.1		,       UpperCAmelCase				:							str=0.1		,       UpperCAmelCase				:							Tuple=512		,       UpperCAmelCase				:							Union[str, Any]=0.02		,       UpperCAmelCase				:							List[str]=0		,       UpperCAmelCase				:							Dict=None		,       ):
            A_		   =							parent
            A_		   =							batch_size
            A_		   =							seq_length
            A_		   =							is_training
            A_		   =							use_input_mask
            A_		   =							use_labels
            A_		   =							vocab_size
            A_		   =							hidden_size
            A_		   =							projection_dim
            A_		   =							num_hidden_layers
            A_		   =							num_attention_heads
            A_		   =							intermediate_size
            A_		   =							dropout
            A_		   =							attention_dropout
            A_		   =							max_position_embeddings
            A_		   =							initializer_range
            A_		   =							scope
            A_		   =							bos_token_id





      def 						__A     (					self				:							int     ):
            A_		   =							ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size     )

            A_		   =							None
            if self.use_input_mask:
                  A_		   =							random_attention_mask([self.batch_size, self.seq_length]     )

            if input_mask is not None:
                  A_		   =							input_mask.numpy()
                  A_		   =							input_mask.shape
                  A_		   =							np.random.randint(1		,       seq_length - 1		,       size=(batch_size,)     )
                  for batch_idx, start_index in enumerate(__UpperCAmelCase     ):
                        A_		   =							1
                        A_		   =							0

            A_		   =							self.get_config()

            return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase     )





      def 						__A     (					self				:							Tuple     ):
            return BlipTextConfig(
                vocab_size=self.vocab_size		,       hidden_size=self.hidden_size		,       projection_dim=self.projection_dim		,       num_hidden_layers=self.num_hidden_layers		,       num_attention_heads=self.num_attention_heads		,       intermediate_size=self.intermediate_size		,       dropout=self.dropout		,       attention_dropout=self.attention_dropout		,       max_position_embeddings=self.max_position_embeddings		,       initializer_range=self.initializer_range		,       bos_token_id=self.bos_token_id		,       )





      def 						__A     (					self				:							Union[str, Any]		,       UpperCAmelCase				:							Union[str, Any]		,       UpperCAmelCase				:							Dict		,       UpperCAmelCase				:							List[Any]     ):
            A_		   =							TFBlipTextModel(config=__UpperCAmelCase     )
            A_		   =							model(__UpperCAmelCase		,       attention_mask=__UpperCAmelCase		,       training=__UpperCAmelCase     )
            A_		   =							model(__UpperCAmelCase		,       training=__UpperCAmelCase     )
            self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)     )
            self.parent.assertEqual(result.pooler_output.shape		,       (self.batch_size, self.hidden_size)     )







      def 						__A     (					self				:							Optional[int]     ):
            A_		   =							self.prepare_config_and_inputs()
            A_		   =							config_and_inputs
            A_		   =							{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict




@require_tf
class    _a (       _a			,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase    :       int							=    (TFBlipTextModel,) if is_tf_available() else ()
      _lowerCamelCase    :       str							=    False
      _lowerCamelCase    :       List[Any]							=    False
      _lowerCamelCase    :       Optional[Any]							=    False





      def 						__A     (					self				:							Dict     ):
            A_		   =							BlipTextModelTester(self     )
            A_		   =							ConfigTester(self		,       config_class=__UpperCAmelCase		,       hidden_size=37     )





      def 						__A     (					self				:							int     ):
            self.config_tester.run_common_tests()





      def 						__A     (					self				:							List[Any]     ):
            A_		   =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__UpperCAmelCase     )





      def 						__A     (					self				:							int     ):
            pass





      def 						__A     (					self				:							Dict     ):
            pass





      @unittest.skip(reason="Blip does not use inputs_embeds"     )
      def 						__A     (					self				:							List[str]     ):
            pass





      @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"     )
      def 						__A     (					self				:							List[Any]     ):
            pass





      @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"     )
      def 						__A     (					self				:							Dict     ):
            pass





      @slow
      def 						__A     (					self				:							str     ):
            for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_		   =							TFBlipTextModel.from_pretrained(__UpperCAmelCase     )
                  self.assertIsNotNone(__UpperCAmelCase     )







      def 						__A     (					self				:							Optional[Any]		,       UpperCAmelCase				:							int=True     ):
            super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase     )



"""simple docstring"""







def      lowercase			(				A_       )->      bool:
  '''simple docstring'''







  if not all(x.isalpha() for x in string       ):
    raise ValueError("String must only contain alphabetic characters."       )

  a						:		Tuple      							=			sorted(string.lower()       )
  return len(A_       ) == len(set(A_       )       )


if __name__ == "__main__":
 __lowercase    					=      input("""Enter a string """).strip()

 __lowercase    					=      is_isogram(input_str)
 print(f'''{input_str} is {'an' if isogram else 'not an'} isogram.''')






