





'''simple docstring'''






from math import factorial







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				float:



			"""simple docstring"""


			if successes > trials:
						raise ValueError("""successes must be lower or equal to trials""" )
			if trials < 0 or successes < 0:
						raise ValueError("""the function is defined for non-negative integers""" )
			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ) or not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise ValueError("""the function is defined for non-negative integers""" )
			if not 0 < prob < 1:
						raise ValueError("""prob has to be in range of 1 - 0""" )
			__snake_case					:      int							      =     (prob**successes) * ((1 - prob) ** (trials - successes))
			# Calculate the binomial coefficient: n! / k!(n-k)!
			__snake_case					:      List[str]							      =     float(factorial(_lowerCamelCase ) )
			coefficient /= factorial(_lowerCamelCase ) * factorial(trials - successes )
			return probability * coefficient


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	print("Probability of 2 successes out of 4 trails")
	print("with probability of 0.75 is:", end=" ")
	print(binomial_distribution(2, 4, 0.75))



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	:	Optional[Any]  ,  lowercase__	:	int  ,  lowercase__	:	List[str]=12  ,  lowercase__	:	Optional[int]=7  ,  lowercase__	:	List[Any]=True  ,  lowercase__	:	str=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	List[str]=99  ,  lowercase__	:	Optional[int]=32  ,  lowercase__	:	Tuple=32  ,  lowercase__	:	Dict=2  ,  lowercase__	:	Optional[int]=4  ,  lowercase__	:	str=37  ,  lowercase__	:	Optional[Any]=0.1  ,  lowercase__	:	List[str]=0.1  ,  lowercase__	:	Union[str, Any]=5_12  ,  lowercase__	:	List[str]=0.0_2  ,  lowercase__	:	Tuple=0  ,  lowercase__	:	List[Any]=None  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_input_mask
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							projection_dim
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							dropout
										_lowerCAmelCase							=							attention_dropout
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							bos_token_id





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size						)

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

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

										_lowerCAmelCase							=							self.get_config()

										return config, input_ids, tf.convert_to_tensor(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							TFBlipTextModel(config=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  training=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  training=lowercase__						)
										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   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							self.prepare_config_and_inputs()
										_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							config_and_inputs
										_lowerCAmelCase							=							{'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict







@require_tf
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(TFBlipTextModel,) if is_tf_available() else ()
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							BlipTextModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  hidden_size=37						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										self.config_tester.run_common_tests()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										pass





			@unittest.skip(reason='Blip does not use inputs_embeds'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										pass





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





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





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							TFBlipTextModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Any=True						):
										super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase__						)






