

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      __lowercase			:

  def __init__(  self		:			str					,A		:			Optional[int]					,A		:			Dict=12					,A		:			str=7					,A		:			List[str]=True					,A		:			str=True					,A		:			Tuple=True					,A		:			int=99					,A		:			Any=32					,A		:			int=32					,A		:			List[str]=2					,A		:			int=4					,A		:			Union[str, Any]=37					,A		:			Optional[Any]=0.1					,A		:			Optional[Any]=0.1					,A		:			Tuple=512					,A		:			Any=0.0_2					,A		:			List[Any]=0					,A		:			Optional[int]=None					,):






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

  def    __lowercase       (  self		:			List[str]    ):






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

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

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

   UpperCAmelCase__					:  List[str]					   =					self.get_config()

   return config, input_ids, tf.convert_to_tensor(UpperCamelCase_    )

  def    __lowercase       (  self		:			int    ):






   '''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    __lowercase       (  self		:			List[Any]					,A		:			Any					,A		:			Any					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					TFBlipTextModel(config=UpperCamelCase_    )
   UpperCAmelCase__					:  List[str]					   =					model(UpperCamelCase_					,attention_mask=UpperCamelCase_					,training=UpperCamelCase_    )
   UpperCAmelCase__					:  Union[str, Any]					   =					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    __lowercase       (  self		:			Tuple    ):






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







@require_tf
class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       (TFBlipTextModel,) if is_tf_available() else ()
  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					BlipTextModelTester(self    )
   UpperCAmelCase__					:  Tuple					   =					ConfigTester(self					,config_class=UpperCamelCase_					,hidden_size=37    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   self.config_tester.run_common_tests()

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase_    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   pass

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






   '''simple docstring'''
   pass

  @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""    )
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   pass

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






   '''simple docstring'''
   pass

  @slow
  def    __lowercase       (  self		:			List[str]    ):






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




  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Tuple=True    ):






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








"""simple docstring"""




import random
from typing import Any
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Dict					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__					:  Tuple					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					data[b], data[a]
 return data


if __name__ == "__main__":
      __UpperCAmelCase =				[0, 1, 2, 3, 4, 5, 6, 7]
      __UpperCAmelCase =				['python', 'says', 'hello', '!']
      print('Fisher-Yates Shuffle:')
      print('List', integers, strings)
      print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))





