




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


__a							     =					logging.get_logger(__name__)

__a							     =					{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''xglm'''
    lowerCAmelCase         = ['''past_key_values''']

    lowerCAmelCase         = {
        '''num_attention_heads''': '''attention_heads''',
        '''hidden_size''': '''d_model''',
        '''num_hidden_layers''': '''num_layers''',
    }





    def __init__( self  ,_SCREAMING_SNAKE_CASE=256_008  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=1_024  ,_SCREAMING_SNAKE_CASE=4_096  ,_SCREAMING_SNAKE_CASE=24  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=2  ,**_SCREAMING_SNAKE_CASE  ,)					->						Any:
         UpperCAmelCase_     :				Optional[int]  =     vocab_size
         UpperCAmelCase_     :				List[Any]  =     max_position_embeddings
         UpperCAmelCase_     :				Dict  =     d_model
         UpperCAmelCase_     :				Union[str, Any]  =     ffn_dim
         UpperCAmelCase_     :				Dict  =     num_layers
         UpperCAmelCase_     :				Dict  =     attention_heads
         UpperCAmelCase_     :				Any  =     activation_function
         UpperCAmelCase_     :				Optional[int]  =     dropout
         UpperCAmelCase_     :				Tuple  =     attention_dropout
         UpperCAmelCase_     :				str  =     activation_dropout
         UpperCAmelCase_     :				Union[str, Any]  =     layerdrop
         UpperCAmelCase_     :				Tuple  =     init_std
         UpperCAmelCase_     :				int  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase_     :				str  =     use_cache

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE  ,bos_token_id=_SCREAMING_SNAKE_CASE  ,eos_token_id=_SCREAMING_SNAKE_CASE  ,decoder_start_token_id=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)




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			:



	def __init__(    self				:     int					,						snake_case_				:     Dict					,						snake_case_				:     List[str]=12					,						snake_case_				:     Optional[int]=7					,						snake_case_				:     Optional[Any]=True					,						snake_case_				:     int=True					,						snake_case_				:     int=True					,						snake_case_				:     Any=99					,						snake_case_				:     List[Any]=32					,						snake_case_				:     List[Any]=32					,						snake_case_				:     Any=2					,						snake_case_				:     Optional[Any]=4					,						snake_case_				:     Optional[Any]=37					,						snake_case_				:     Tuple=0.1					,						snake_case_				:     str=0.1					,						snake_case_				:     Any=5_12					,						snake_case_				:     Optional[Any]=0.0_2					,						snake_case_				:     List[Any]=0					,						snake_case_				:     Union[str, Any]=None					,						)->				List[Any]:
						__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 		UpperCamelCase				(    self				:     List[Any])->				Optional[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(snake_case_):
																__lowerCAmelCase						 =1
																__lowerCAmelCase						 =0

						__lowerCAmelCase						 =self.get_config()

						return config, input_ids, tf.convert_to_tensor(snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				str:
						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 		UpperCamelCase				(    self				:     Union[str, Any]					,						snake_case_				:     Tuple					,						snake_case_				:     List[Any]					,						snake_case_				:     List[Any])->				List[str]:
						__lowerCAmelCase						 =TFBlipTextModel(config=snake_case_)
						__lowerCAmelCase						 =model(snake_case_					,						attention_mask=snake_case_					,						training=snake_case_)
						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     Dict)->				Tuple:
						__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     __a			(  SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(TFBlipTextModel,) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False



	def 		UpperCamelCase				(    self				:     Optional[Any])->				Any:
						__lowerCAmelCase						 =BlipTextModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=snake_case_					,						hidden_size=37)



	def 		UpperCamelCase				(    self				:     List[Any])->				List[str]:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase				(    self				:     List[str])->				Dict:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						pass



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



	@unittest.skip(reason="""Blip does not use inputs_embeds""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				int:
						pass



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



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



	@slow
	def 		UpperCamelCase				(    self				:     int)->				str:
						for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase						 =TFBlipTextModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     List[str]=True)->				int:
						super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case_)




