




'''simple docstring'''

import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
						import jax

						from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
						from transformers.models.bert.modeling_flax_bert import FlaxBertModel
						from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel



@require_flax
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			@slow
			def 							A (      self						:    Dict							):



						"""simple docstring"""




						for model_name in ["bert-base-cased", "bert-large-uncased"]:
									with self.subTest(A_							):
												__snake_case			=		AutoConfig.from_pretrained(A_							)
												self.assertIsNotNone(A_							)
												self.assertIsInstance(A_      ,		A_							)

												__snake_case			=		FlaxAutoModel.from_pretrained(A_							)
												self.assertIsNotNone(A_							)
												self.assertIsInstance(A_      ,		A_							)





			@slow
			def 							A (      self						:    int							):



						"""simple docstring"""




						for model_name in ["roberta-base", "roberta-large"]:
									with self.subTest(A_							):
												__snake_case			=		AutoConfig.from_pretrained(A_							)
												self.assertIsNotNone(A_							)
												self.assertIsInstance(A_      ,		A_							)

												__snake_case			=		FlaxAutoModel.from_pretrained(A_							)
												self.assertIsNotNone(A_							)
												self.assertIsInstance(A_      ,		A_							)





			@slow
			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						for model_name in ["bert-base-cased", "bert-large-uncased"]:
									__snake_case			=		AutoTokenizer.from_pretrained(A_							)
									__snake_case			=		FlaxBertModel.from_pretrained(A_							)
									__snake_case			=		tokenizer("Do you support jax jitted function?"      ,		return_tensors=TensorType.JAX							)

									@jax.jit
									def eval(**a_						:    Any							):
												return model(**A_							)

									eval(**A_							).block_until_ready()





			@slow
			def 							A (      self						:    int							):



						"""simple docstring"""




						for model_name in ["roberta-base", "roberta-large"]:
									__snake_case			=		AutoTokenizer.from_pretrained(A_							)
									__snake_case			=		FlaxRobertaModel.from_pretrained(A_							)
									__snake_case			=		tokenizer("Do you support jax jitted function?"      ,		return_tensors=TensorType.JAX							)

									@jax.jit
									def eval(**a_						:    Dict							):
												return model(**A_							)

									eval(**A_							).block_until_ready()





			def 							A (      self						:    Any							):



						"""simple docstring"""




						with self.assertRaisesRegex(
						    A_      ,		"bert-base is not a local folder and is not a valid model identifier"							):
									__snake_case			=		FlaxAutoModel.from_pretrained("bert-base"							)





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						with self.assertRaisesRegex(
						    A_      ,		r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"							):
									__snake_case			=		FlaxAutoModel.from_pretrained(A_      ,		revision="aaaaaa"							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						with self.assertRaisesRegex(
						    A_      ,		"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"      ,		):
									__snake_case			=		FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"							)






			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						with self.assertRaisesRegex(A_      ,		"Use `from_pt=True` to load this model"							):
									__snake_case			=		FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"							)







"""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


A__ :       List[Any]				= logging.get_logger(__name__)

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


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_vision_model'''
       def __init__(  self			,					A_=1408			,					A_=6144			,					A_=39			,					A_=16			,					A_=224			,					A_=14			,					A_="gelu"			,					A_=1E-6			,					A_=0.0			,					A_=1E-10			,					A_=True			,					**A_			,					)  ->						Tuple:





         """simple docstring"""




         super().__init__(**A_							)

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      str   			=  cls.get_config_dict(A_			,					**A_							)

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Dict   			=  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(A_			,					**A_							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_qformer'''
       def __init__(  self			,					A_=3_0522			,					A_=768			,					A_=12			,					A_=12			,					A_=3072			,					A_="gelu"			,					A_=0.1			,					A_=0.1			,					A_=512			,					A_=0.02			,					A_=1E-12			,					A_=0			,					A_="absolute"			,					A_=2			,					A_=1408			,					**A_			,					)  ->						Optional[int]:





         """simple docstring"""




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

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      Tuple   			=  cls.get_config_dict(A_			,					**A_							)

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Tuple   			=  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(A_			,					**A_							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip'''
       UpperCamelCase_ =		True
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_=32			,					**A_							)  ->						str:





         """simple docstring"""




         super().__init__(**A_							)

         if vision_config is None:
           _lowercase:      str   			=  {}
           logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''							)

         if qformer_config is None:
           _lowercase:      List[str]   			=  {}
           logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''							)

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

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

         _lowercase:      Tuple   			=  self.text_config.tie_word_embeddings
         _lowercase:      Dict   			=  self.text_config.is_encoder_decoder

         _lowercase:      List[Any]   			=  num_query_tokens
         _lowercase:      Any   			=  self.vision_config.hidden_size
         _lowercase:      List[Any]   			=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         _lowercase:      Union[str, Any]   			=  1.0
         _lowercase:      Dict   			=  0.02
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					A_			,					**A_			,					)  ->						str:





         """simple docstring"""




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



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





         """simple docstring"""




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