






"""simple docstring"""



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 			lowerCAmelCase__			:
   def __init__(		self      :	List[Any]							,			snake_case__      :	str							,			snake_case__      :	Any=1_2							,			snake_case__      :	Dict=7							,			snake_case__      :	str=True							,			snake_case__      :	str=True							,			snake_case__      :	List[Any]=True							,			snake_case__      :	int=9_9							,			snake_case__      :	int=3_2							,			snake_case__      :	int=3_2							,			snake_case__      :	Union[str, Any]=2							,			snake_case__      :	Union[str, Any]=4							,			snake_case__      :	Any=3_7							,			snake_case__      :	Optional[int]=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	str=5_1_2							,			snake_case__      :	Union[str, Any]=0.02							,			snake_case__      :	List[Any]=0							,			snake_case__      :	List[str]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							parent
        UpperCAmelCase__						:    Dict							=							batch_size
        UpperCAmelCase__						:    Any							=							seq_length
        UpperCAmelCase__						:    str							=							is_training
        UpperCAmelCase__						:    int							=							use_input_mask
        UpperCAmelCase__						:    int							=							use_labels
        UpperCAmelCase__						:    str							=							vocab_size
        UpperCAmelCase__						:    Tuple							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							projection_dim
        UpperCAmelCase__						:    Tuple							=							num_hidden_layers
        UpperCAmelCase__						:    Any							=							num_attention_heads
        UpperCAmelCase__						:    List[Any]							=							intermediate_size
        UpperCAmelCase__						:    Tuple							=							dropout
        UpperCAmelCase__						:    str							=							attention_dropout
        UpperCAmelCase__						:    int							=							max_position_embeddings
        UpperCAmelCase__						:    List[Any]							=							initializer_range
        UpperCAmelCase__						:    Union[str, Any]							=							scope
        UpperCAmelCase__						:    Dict							=							bos_token_id
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size							)

        UpperCAmelCase__						:    Tuple							=							None
        if self.use_input_mask:
             UpperCAmelCase__						:    int							=							random_attention_mask([self.batch_size, self.seq_length]							)

        if input_mask is not None:
             UpperCAmelCase__						:    Optional[int]							=							input_mask.numpy()
             UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							input_mask.shape
             UpperCAmelCase__						:    List[Any]							=							np.random.randint(1							,			seq_length - 1							,			size=(batch_size,)							)
             for batch_idx, start_index in enumerate(snake_case__							):
                  UpperCAmelCase__						:    Optional[int]							=							1
                  UpperCAmelCase__						:    Optional[int]							=							0

        UpperCAmelCase__						:    Union[str, Any]							=							self.get_config()

        return config, input_ids, tf.convert_to_tensor(snake_case__							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        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      :	Tuple							,			snake_case__      :	Tuple							,			snake_case__      :	Tuple							,			snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							TFBlipTextModel(config=snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			attention_mask=snake_case__							,			training=snake_case__							)
        UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			training=snake_case__							)
        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      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.prepare_config_and_inputs()
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							config_and_inputs
        UpperCAmelCase__						:    Any							=							{"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_tf
class 			lowerCAmelCase__			(    __magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(TFBlipTextModel,) if is_tf_available() else ()
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							BlipTextModelTester(self							)
        UpperCAmelCase__						:    Dict							=							ConfigTester(self							,			config_class=snake_case__							,			hidden_size=3_7							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        pass
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        pass
   @unittest.skip(reason="Blip does not use inputs_embeds"							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        pass
   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        pass
   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        pass
   @slow
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase__						:    int							=							TFBlipTextModel.from_pretrained(snake_case__							)
             self.assertIsNotNone(snake_case__							)




   def       __a							(		self      :	Dict							,			snake_case__      :	int=True							):


        '''simple docstring'''
        super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__							)










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


