



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       a__   :					Union[str, Any] 						=      "platform"

       import jax
       import jax.numpy as jnp

       from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
           FlaxBlenderbotSmallForConditionalGeneration,
           FlaxBlenderbotSmallModel,
           shift_tokens_right,
       )

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__=None    ,	A__=None    ,	A__=None    ,	A__=None    ,	A__=None    ,	A__=None    ,	):
      if attention_mask is None:
            lowercase__  =	np.where(input_ids != config.pad_token_id    ,	1    ,	0						)
      if decoder_attention_mask is None:
            lowercase__  =	np.where(decoder_input_ids != config.pad_token_id    ,	1    ,	0						)
      if head_mask is None:
            lowercase__  =	np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            lowercase__  =	np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            lowercase__  =	np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      Any      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      str=13      ,					lowerCAmelCase	:      Optional[int]=7      ,					lowerCAmelCase	:      str=True      ,					lowerCAmelCase	:      Optional[int]=False      ,					lowerCAmelCase	:      Tuple=99      ,					lowerCAmelCase	:      str=16      ,					lowerCAmelCase	:      Any=2      ,					lowerCAmelCase	:      Tuple=4      ,					lowerCAmelCase	:      Optional[Any]=4      ,					lowerCAmelCase	:      List[str]="gelu"      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      List[Any]=0.1      ,					lowerCAmelCase	:      Dict=32      ,					lowerCAmelCase	:      Optional[Any]=2      ,					lowerCAmelCase	:      Tuple=1      ,					lowerCAmelCase	:      Optional[Any]=0      ,					lowerCAmelCase	:      int=0.02      ,					)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	batch_size
         lowercase__  =	seq_length
         lowercase__  =	is_training
         lowercase__  =	use_labels
         lowercase__  =	vocab_size
         lowercase__  =	hidden_size
         lowercase__  =	num_hidden_layers
         lowercase__  =	num_attention_heads
         lowercase__  =	intermediate_size
         lowercase__  =	hidden_act
         lowercase__  =	hidden_dropout_prob
         lowercase__  =	attention_probs_dropout_prob
         lowercase__  =	max_position_embeddings
         lowercase__  =	eos_token_id
         lowercase__  =	pad_token_id
         lowercase__  =	bos_token_id
         lowercase__  =	initializer_range


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,					self.vocab_size)      ,					3      ,					self.vocab_size)
         lowercase__  =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,					dtype=np.intaa))      ,					-1)

         lowercase__  =	shift_tokens_right(_lowerCamelCase      ,					1      ,					2)

         lowercase__  =	BlenderbotSmallConfig(
             vocab_size=self.vocab_size      ,					d_model=self.hidden_size      ,					encoder_layers=self.num_hidden_layers      ,					decoder_layers=self.num_hidden_layers      ,					encoder_attention_heads=self.num_attention_heads      ,					decoder_attention_heads=self.num_attention_heads      ,					encoder_ffn_dim=self.intermediate_size      ,					decoder_ffn_dim=self.intermediate_size      ,					dropout=self.hidden_dropout_prob      ,					attention_dropout=self.attention_probs_dropout_prob      ,					max_position_embeddings=self.max_position_embeddings      ,					eos_token_id=self.eos_token_id      ,					bos_token_id=self.bos_token_id      ,					pad_token_id=self.pad_token_id      ,					initializer_range=self.initializer_range      ,					use_cache=_lowerCamelCase      ,					)
         lowercase__  =	prepare_blenderbot_inputs_dict(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)
         return config, inputs_dict


   def       UpperCAmelCase		(    self	:      List[str])						->					Dict:




         """simple docstring"""

         lowercase__  =	self.prepare_config_and_inputs()
         return config, inputs_dict


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[str])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	20
         lowercase__  =	model_class_name(_lowerCamelCase)

         lowercase__  =	model.encode(inputs_dict['input_ids'])

         lowercase__  =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         lowercase__  =	model.init_cache(decoder_input_ids.shape[0]      ,					_lowerCamelCase      ,					_lowerCamelCase)
         lowercase__  =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,					dtype='i4')

         lowercase__  =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,					)
         lowercase__  =	model.decode(
             decoder_input_ids[:, :-1]      ,					_lowerCamelCase      ,					decoder_attention_mask=_lowerCamelCase      ,					past_key_values=_lowerCamelCase      ,					decoder_position_ids=_lowerCamelCase      ,					)

         lowercase__  =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,					dtype='i4')
         lowercase__  =	model.decode(
             decoder_input_ids[:, -1:]      ,					_lowerCamelCase      ,					decoder_attention_mask=_lowerCamelCase      ,					past_key_values=outputs_cache.past_key_values      ,					decoder_position_ids=_lowerCamelCase      ,					)

         lowercase__  =	model.decode(_lowerCamelCase      ,					_lowerCamelCase)

         lowercase__  =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
         self.parent.assertTrue(diff < 1E-3      ,					msg=f'''Max diff is {diff}''')



   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      int)						->					Any:




         """simple docstring"""

         lowercase__  =	20
         lowercase__  =	model_class_name(_lowerCamelCase)

         lowercase__  =	model.encode(inputs_dict['input_ids'])

         lowercase__  =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         lowercase__  =	jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
             ]      ,					axis=-1      ,					)

         lowercase__  =	model.init_cache(decoder_input_ids.shape[0]      ,					_lowerCamelCase      ,					_lowerCamelCase)
         lowercase__  =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,					)

         lowercase__  =	model.decode(
             decoder_input_ids[:, :-1]      ,					_lowerCamelCase      ,					decoder_attention_mask=_lowerCamelCase      ,					past_key_values=_lowerCamelCase      ,					decoder_position_ids=_lowerCamelCase      ,					)
         lowercase__  =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,					dtype='i4')
         lowercase__  =	model.decode(
             decoder_input_ids[:, -1:]      ,					_lowerCamelCase      ,					past_key_values=outputs_cache.past_key_values      ,					decoder_attention_mask=_lowerCamelCase      ,					decoder_position_ids=_lowerCamelCase      ,					)

         lowercase__  =	model.decode(_lowerCamelCase      ,					_lowerCamelCase      ,					decoder_attention_mask=_lowerCamelCase)

         lowercase__  =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
         self.parent.assertTrue(diff < 1E-3      ,					msg=f'''Max diff is {diff}''')



@require_flax
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     99


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					int:




         """simple docstring"""

         lowercase__  =	np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]      ,					dtype=np.intaa      ,					)

         lowercase__  =	input_ids.shape[0]
         lowercase__  =	BlenderbotSmallConfig(
             vocab_size=self.vocab_size      ,					d_model=24      ,					encoder_layers=2      ,					decoder_layers=2      ,					encoder_attention_heads=2      ,					decoder_attention_heads=2      ,					encoder_ffn_dim=32      ,					decoder_ffn_dim=32      ,					max_position_embeddings=48      ,					eos_token_id=2      ,					pad_token_id=1      ,					bos_token_id=0      ,					)
         return config, input_ids, batch_size


   def       UpperCAmelCase		(    self	:      Any)						->					int:




         """simple docstring"""

         lowercase__  =	self._get_config_and_data()
         lowercase__  =	FlaxBlenderbotSmallForConditionalGeneration(_lowerCamelCase)
         lowercase__  =	lm_model(input_ids=_lowerCamelCase)
         lowercase__  =	(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape      ,					_lowerCamelCase)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Tuple:




         """simple docstring"""

         lowercase__  =	BlenderbotSmallConfig(
             vocab_size=self.vocab_size      ,					d_model=14      ,					encoder_layers=2      ,					decoder_layers=2      ,					encoder_attention_heads=2      ,					decoder_attention_heads=2      ,					encoder_ffn_dim=8      ,					decoder_ffn_dim=8      ,					max_position_embeddings=48      ,					)
         lowercase__  =	FlaxBlenderbotSmallForConditionalGeneration(_lowerCamelCase)
         lowercase__  =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,					dtype=np.intaa)
         lowercase__  =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,					dtype=np.intaa)
         lowercase__  =	lm_model(input_ids=_lowerCamelCase      ,					decoder_input_ids=_lowerCamelCase)
         lowercase__  =	(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['logits'].shape      ,					_lowerCamelCase)



   def       UpperCAmelCase		(    self	:      List[str])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,					dtype=np.intaa)
         lowercase__  =	shift_tokens_right(_lowerCamelCase      ,					1      ,					2)
         lowercase__  =	np.equal(_lowerCamelCase      ,					1).astype(np.floataa).sum()
         lowercase__  =	np.equal(_lowerCamelCase      ,					1).astype(np.floataa).sum()
         self.assertEqual(shifted.shape      ,					input_ids.shape)
         self.assertEqual(_lowerCamelCase      ,					n_pad_before - 1)
         self.assertTrue(np.equal(shifted[:, 0]      ,					2).all())



@require_flax
class    UpperCAmelCase__(				A__  ,				unittest.TestCase  ,				A__						):

   '''simple docstring'''




   A     :	List[str]												=     True
   A     :	Union[str, Any]												=     (
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A     :	int												=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


   def       UpperCAmelCase		(    self	:      List[Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	FlaxBlenderbotSmallModelTester(self)


   def       UpperCAmelCase		(    self	:      List[str])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                     lowercase__  =	self._prepare_for_class(_lowerCamelCase      ,					_lowerCamelCase)
                     lowercase__  =	model_class(_lowerCamelCase)

                     @jax.jit
                     def encode_jitted(lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Dict=None      ,					**lowerCAmelCase	:      Optional[int]):
                           return model.encode(input_ids=_lowerCamelCase      ,					attention_mask=_lowerCamelCase)

                     with self.subTest('JIT Enabled'):
                           lowercase__  =	encode_jitted(**_lowerCamelCase).to_tuple()

                     with self.subTest('JIT Disabled'):
                           with jax.disable_jit():
                                 lowercase__  =	encode_jitted(**_lowerCamelCase).to_tuple()

                     self.assertEqual(len(_lowerCamelCase)      ,					len(_lowerCamelCase))
                     for jitted_output, output in zip(_lowerCamelCase      ,					_lowerCamelCase):
                           self.assertEqual(jitted_output.shape      ,					output.shape)


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                     lowercase__  =	model_class(_lowerCamelCase)
                     lowercase__  =	model.encode(inputs_dict['input_ids']      ,					inputs_dict['attention_mask'])

                     lowercase__  =	{
                         'decoder_input_ids': inputs_dict['decoder_input_ids'],
                         'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                         'encoder_outputs': encoder_outputs,
                     }

                     @jax.jit
                     def decode_jitted(lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[int]):
                           return model.decode(
                               decoder_input_ids=_lowerCamelCase      ,					decoder_attention_mask=_lowerCamelCase      ,					encoder_outputs=_lowerCamelCase      ,					)

                     with self.subTest('JIT Enabled'):
                           lowercase__  =	decode_jitted(**_lowerCamelCase).to_tuple()

                     with self.subTest('JIT Disabled'):
                           with jax.disable_jit():
                                 lowercase__  =	decode_jitted(**_lowerCamelCase).to_tuple()

                     self.assertEqual(len(_lowerCamelCase)      ,					len(_lowerCamelCase))
                     for jitted_output, output in zip(_lowerCamelCase      ,					_lowerCamelCase):
                           self.assertEqual(jitted_output.shape      ,					output.shape)



   @slow
   def       UpperCAmelCase		(    self	:      Tuple)						->					Any:




         """simple docstring"""

         for model_class_name in self.all_model_classes:
               lowercase__  =	model_class_name.from_pretrained('facebook/blenderbot_small-90M')
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               lowercase__  =	np.ones((1, 1)) * model.config.eos_token_id
               lowercase__  =	model(_lowerCamelCase)
               self.assertIsNotNone(_lowerCamelCase)


"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_vision_model'''



     def __init__(							self :int      ,  _lowerCamelCase :Optional[int]=1_408      ,  _lowerCamelCase :int=6_144      ,  _lowerCamelCase :str=39      ,  _lowerCamelCase :Tuple=16      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :int=14      ,  _lowerCamelCase :int="gelu"      ,  _lowerCamelCase :Optional[Any]=1E-6      ,  _lowerCamelCase :str=0.0      ,  _lowerCamelCase :Optional[Any]=1E-1_0      ,  _lowerCamelCase :str=True      ,  **_lowerCamelCase :Optional[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Dict   	=hidden_size
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[int]   	=num_hidden_layers
      UpperCamelCase_     :					Union[str, Any]   	=num_attention_heads
      UpperCamelCase_     :					Optional[Any]   	=patch_size
      UpperCamelCase_     :					List[Any]   	=image_size
      UpperCamelCase_     :					Dict   	=initializer_range
      UpperCamelCase_     :					List[Any]   	=attention_dropout
      UpperCamelCase_     :					List[str]   	=layer_norm_eps
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=qkv_bias




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					Tuple   	=config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls      ,  'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
           f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

      return cls.from_dict(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_qformer'''



     def __init__(							self :Dict      ,  _lowerCamelCase :List[Any]=30_522      ,  _lowerCamelCase :Optional[int]=768      ,  _lowerCamelCase :Tuple=12      ,  _lowerCamelCase :List[str]=12      ,  _lowerCamelCase :int=3_072      ,  _lowerCamelCase :Tuple="gelu"      ,  _lowerCamelCase :Dict=0.1      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Optional[int]=512      ,  _lowerCamelCase :List[Any]=0.02      ,  _lowerCamelCase :int=1E-1_2      ,  _lowerCamelCase :str=0      ,  _lowerCamelCase :Dict="absolute"      ,  _lowerCamelCase :Optional[int]=2      ,  _lowerCamelCase :Union[str, Any]=1_408      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(pad_token_id=_lowerCamelCase      ,  **_lowerCamelCase   )

      UpperCamelCase_     :					Optional[Any]   	=vocab_size
      UpperCamelCase_     :					Tuple   	=hidden_size
      UpperCamelCase_     :					Union[str, Any]   	=num_hidden_layers
      UpperCamelCase_     :					List[str]   	=num_attention_heads
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[Any]   	=hidden_dropout_prob
      UpperCamelCase_     :					List[str]   	=attention_probs_dropout_prob
      UpperCamelCase_     :					List[str]   	=max_position_embeddings
      UpperCamelCase_     :					List[str]   	=initializer_range
      UpperCamelCase_     :					str   	=layer_norm_eps
      UpperCamelCase_     :					int   	=position_embedding_type
      UpperCamelCase_     :					Tuple   	=cross_attention_frequency
      UpperCamelCase_     :					List[Any]   	=encoder_hidden_size




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					List[Any]   	=config_dict['qformer_config']

      if "model_type" in config_dict and hasattr(cls      ,  'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
           f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

      return cls.from_dict(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip'''
     UpperCAmelCase__         =      True



     def __init__(							self :str      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Union[str, Any]=32      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      if vision_config is None:
       UpperCamelCase_     :					Optional[int]   	={}
       logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'   )

      if qformer_config is None:
       UpperCamelCase_     :					Union[str, Any]   	={}
       logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'   )

      if text_config is None:
       UpperCamelCase_     :					str   	={}
       logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'   )

      UpperCamelCase_     :					Any   	=InstructBlipVisionConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=InstructBlipQFormerConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					str   	=text_config['model_type'] if 'model_type' in text_config else 'opt'
      UpperCamelCase_     :					int   	=CONFIG_MAPPING[text_model_type](**_lowerCamelCase   )

      UpperCamelCase_     :					Tuple   	=self.text_config.tie_word_embeddings
      UpperCamelCase_     :					Any   	=self.text_config.is_encoder_decoder

      UpperCamelCase_     :					List[Any]   	=num_query_tokens
      UpperCamelCase_     :					Tuple   	=self.vision_config.hidden_size
      UpperCamelCase_     :					Tuple   	=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      UpperCamelCase_     :					int   	=1.0
      UpperCamelCase_     :					List[str]   	=0.02



     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :InstructBlipVisionConfig      ,  _lowerCamelCase :InstructBlipQFormerConfig      ,  _lowerCamelCase :PretrainedConfig      ,  **_lowerCamelCase :Dict      ,  ):







      '''simple docstring'''



      return cls(
          vision_config=vision_config.to_dict()      ,  qformer_config=qformer_config.to_dict()      ,  text_config=text_config.to_dict()      ,  **_lowerCamelCase      ,  )




     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=copy.deepcopy(self.__dict__   )
      UpperCamelCase_     :					Dict   	=self.vision_config.to_dict()
      UpperCamelCase_     :					Tuple   	=self.qformer_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.text_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.__class__.model_type
      return output
