"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase       =  logging.get_logger(__name__)

UpperCAmelCase       =  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class      UpperCAmelCase_       (						_lowercase):
							snake_case__        =      '''megatron-bert'''






							def __init__(		self :      Dict     ,						__UpperCamelCase :      List[Any]=2_9056     ,						__UpperCamelCase :      List[Any]=1024     ,						__UpperCamelCase :      Tuple=24     ,						__UpperCamelCase :      Optional[Any]=16     ,						__UpperCamelCase :      Union[str, Any]=4096     ,						__UpperCamelCase :      Union[str, Any]="gelu"     ,						__UpperCamelCase :      List[Any]=0.1     ,						__UpperCamelCase :      Any=0.1     ,						__UpperCamelCase :      Optional[Any]=512     ,						__UpperCamelCase :      Optional[Any]=2     ,						__UpperCamelCase :      Any=0.0_2     ,						__UpperCamelCase :      List[str]=1E-12     ,						__UpperCamelCase :      Dict=0     ,						__UpperCamelCase :      Union[str, Any]="absolute"     ,						__UpperCamelCase :      Union[str, Any]=True     ,						**__UpperCamelCase :      Any     ,						)       ->       Optional[int]:
												super().__init__(pad_token_id=__UpperCamelCase     ,						**__UpperCamelCase  )

												_UpperCamelCase      						=	vocab_size
												_UpperCamelCase      						=	hidden_size
												_UpperCamelCase      						=	num_hidden_layers
												_UpperCamelCase      						=	num_attention_heads
												_UpperCamelCase      						=	hidden_act
												_UpperCamelCase      						=	intermediate_size
												_UpperCamelCase      						=	hidden_dropout_prob
												_UpperCamelCase      						=	attention_probs_dropout_prob
												_UpperCamelCase      						=	max_position_embeddings
												_UpperCamelCase      						=	type_vocab_size
												_UpperCamelCase      						=	initializer_range
												_UpperCamelCase      						=	layer_norm_eps
												_UpperCamelCase      						=	position_embedding_type
												_UpperCamelCase      						=	use_cache





'''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 			UpperCamelCase_ :





	def __init__(  self		,		A		,		A=12		,		A=7		,		A=True		,		A=True		,		A=True		,		A=99		,		A=32		,		A=32		,		A=2		,		A=4		,		A=37		,		A=0.1		,		A=0.1		,		A=512		,		A=0.0_2		,		A=0		,		A=None		,		)			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		parent
								UpperCAmelCase     :		str							     =		batch_size
								UpperCAmelCase     :		Union[str, Any]							     =		seq_length
								UpperCAmelCase     :		Optional[Any]							     =		is_training
								UpperCAmelCase     :		int							     =		use_input_mask
								UpperCAmelCase     :		List[Any]							     =		use_labels
								UpperCAmelCase     :		Dict							     =		vocab_size
								UpperCAmelCase     :		str							     =		hidden_size
								UpperCAmelCase     :		List[Any]							     =		projection_dim
								UpperCAmelCase     :		Tuple							     =		num_hidden_layers
								UpperCAmelCase     :		Dict							     =		num_attention_heads
								UpperCAmelCase     :		Optional[Any]							     =		intermediate_size
								UpperCAmelCase     :		Any							     =		dropout
								UpperCAmelCase     :		List[Any]							     =		attention_dropout
								UpperCAmelCase     :		Optional[Any]							     =		max_position_embeddings
								UpperCAmelCase     :		Tuple							     =		initializer_range
								UpperCAmelCase     :		Optional[Any]							     =		scope
								UpperCAmelCase     :		Union[str, Any]							     =		bos_token_id





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Union[str, Any]							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		str							     =		random_attention_mask([self.batch_size, self.seq_length]    )

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

								UpperCAmelCase     :		int							     =		self.get_config()

								return config, input_ids, tf.convert_to_tensor(A    )





	def  _lowercase(  self    )			->			int:
								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		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		TFBlipTextModel(config=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		model(A		,		attention_mask=A		,		training=A    )
								UpperCAmelCase     :		int							     =		model(A		,		training=A    )
								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    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		self.prepare_config_and_inputs()
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		config_and_inputs
								UpperCAmelCase     :		Optional[Any]							     =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_tf
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = (TFBlipTextModel,) if is_tf_available() else ()
	lowercase          = False
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Union[str, Any]							     =		BlipTextModelTester(self    )
								UpperCAmelCase     :		List[str]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=37    )





	def  _lowercase(  self    )			->			Tuple:
								self.config_tester.run_common_tests()





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A    )





	def  _lowercase(  self    )			->			List[str]:
								pass





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	@unittest.skip(reason="""Blip does not use inputs_embeds"""    )
	def  _lowercase(  self    )			->			Union[str, Any]:
								pass





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





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





	@slow
	def  _lowercase(  self    )			->			Dict:
								for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase     :		Any							     =		TFBlipTextModel.from_pretrained(A    )
															self.assertIsNotNone(A    )






	def  _lowercase(  self		,		A=True    )			->			str:
								super().test_pt_tf_model_equivalence(allow_missing_keys=A    )

