





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


_snake_case    :       str		       =    logging.get_logger(__name__)

_snake_case    :       Union[str, Any]		       =    {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""instructblip_vision_model"""






  def __init__(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						str=1_4_0_8   ,    lowerCAmelCase_					:						List[str]=6_1_4_4   ,    lowerCAmelCase_					:						Any=3_9   ,    lowerCAmelCase_					:						int=1_6   ,    lowerCAmelCase_					:						Optional[int]=2_2_4   ,    lowerCAmelCase_					:						Union[str, Any]=1_4   ,    lowerCAmelCase_					:						str="gelu"   ,    lowerCAmelCase_					:						Tuple=1e-6   ,    lowerCAmelCase_					:						List[str]=0.0   ,    lowerCAmelCase_					:						Tuple=1e-10   ,    lowerCAmelCase_					:						List[Any]=True   ,    **lowerCAmelCase_					:						Dict   ,    )				->		str:
     super().__init__(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      hidden_size
     __lowerCAmelCase				      =      intermediate_size
     __lowerCAmelCase				      =      num_hidden_layers
     __lowerCAmelCase				      =      num_attention_heads
     __lowerCAmelCase				      =      patch_size
     __lowerCAmelCase				      =      image_size
     __lowerCAmelCase				      =      initializer_range
     __lowerCAmelCase				      =      attention_dropout
     __lowerCAmelCase				      =      layer_norm_eps
     __lowerCAmelCase				      =      hidden_act
     __lowerCAmelCase				      =      qkv_bias






  @classmethod
  def 							lowercase						(							cls					:						List[str]   ,    lowerCAmelCase_					:						Union[str, os.PathLike]   ,    **lowerCAmelCase_					:						str	)				->		"PretrainedConfig":
     cls._set_token_in_kwargs(lowerCAmelCase_	)

     __lowerCAmelCase      ,      __lowerCAmelCase				      =      cls.get_config_dict(lowerCAmelCase_   ,    **lowerCAmelCase_	)

     # get the vision config dict if we are loading from InstructBlipConfig
     if config_dict.get('model_type'	) == "instructblip":
        __lowerCAmelCase				      =      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 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""instructblip_qformer"""






  def __init__(							self					:						Tuple   ,    lowerCAmelCase_					:						Optional[Any]=3_0_5_2_2   ,    lowerCAmelCase_					:						Any=7_6_8   ,    lowerCAmelCase_					:						Optional[int]=1_2   ,    lowerCAmelCase_					:						Tuple=1_2   ,    lowerCAmelCase_					:						Tuple=3_0_7_2   ,    lowerCAmelCase_					:						List[str]="gelu"   ,    lowerCAmelCase_					:						Optional[int]=0.1   ,    lowerCAmelCase_					:						str=0.1   ,    lowerCAmelCase_					:						List[str]=5_1_2   ,    lowerCAmelCase_					:						Optional[int]=0.02   ,    lowerCAmelCase_					:						Optional[Any]=1e-12   ,    lowerCAmelCase_					:						Any=0   ,    lowerCAmelCase_					:						Any="absolute"   ,    lowerCAmelCase_					:						Any=2   ,    lowerCAmelCase_					:						List[Any]=1_4_0_8   ,    **lowerCAmelCase_					:						Optional[Any]   ,    )				->		int:
     super().__init__(pad_token_id=lowerCAmelCase_   ,    **lowerCAmelCase_	)

     __lowerCAmelCase				      =      vocab_size
     __lowerCAmelCase				      =      hidden_size
     __lowerCAmelCase				      =      num_hidden_layers
     __lowerCAmelCase				      =      num_attention_heads
     __lowerCAmelCase				      =      hidden_act
     __lowerCAmelCase				      =      intermediate_size
     __lowerCAmelCase				      =      hidden_dropout_prob
     __lowerCAmelCase				      =      attention_probs_dropout_prob
     __lowerCAmelCase				      =      max_position_embeddings
     __lowerCAmelCase				      =      initializer_range
     __lowerCAmelCase				      =      layer_norm_eps
     __lowerCAmelCase				      =      position_embedding_type
     __lowerCAmelCase				      =      cross_attention_frequency
     __lowerCAmelCase				      =      encoder_hidden_size






  @classmethod
  def 							lowercase						(							cls					:						int   ,    lowerCAmelCase_					:						Union[str, os.PathLike]   ,    **lowerCAmelCase_					:						Tuple	)				->		"PretrainedConfig":
     cls._set_token_in_kwargs(lowerCAmelCase_	)

     __lowerCAmelCase      ,      __lowerCAmelCase				      =      cls.get_config_dict(lowerCAmelCase_   ,    **lowerCAmelCase_	)

     # get the qformer config dict if we are loading from InstructBlipConfig
     if config_dict.get('model_type'	) == "instructblip":
        __lowerCAmelCase				      =      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 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""instructblip"""
  a_              =					True






  def __init__(							self					:						Dict   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						int=None   ,    lowerCAmelCase_					:						Dict=None   ,    lowerCAmelCase_					:						Optional[Any]=3_2   ,    **lowerCAmelCase_					:						Tuple	)				->		int:
     super().__init__(**lowerCAmelCase_	)

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

     if qformer_config is None:
        __lowerCAmelCase				      =      {}
        logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'	)

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

     __lowerCAmelCase				      =      InstructBlipVisionConfig(**lowerCAmelCase_	)
     __lowerCAmelCase				      =      InstructBlipQFormerConfig(**lowerCAmelCase_	)
     __lowerCAmelCase				      =      text_config['model_type'] if 'model_type' in text_config else 'opt'
     __lowerCAmelCase				      =      CONFIG_MAPPING[text_model_type](**lowerCAmelCase_	)

     __lowerCAmelCase				      =      self.text_config.tie_word_embeddings
     __lowerCAmelCase				      =      self.text_config.is_encoder_decoder

     __lowerCAmelCase				      =      num_query_tokens
     __lowerCAmelCase				      =      self.vision_config.hidden_size
     __lowerCAmelCase				      =      self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     __lowerCAmelCase				      =      1.0
     __lowerCAmelCase				      =      0.02






  @classmethod
  def 							lowercase						(							cls					:						Dict   ,    lowerCAmelCase_					:						InstructBlipVisionConfig   ,    lowerCAmelCase_					:						InstructBlipQFormerConfig   ,    lowerCAmelCase_					:						PretrainedConfig   ,    **lowerCAmelCase_					:						Tuple   ,    )				->		Union[str, Any]:
     return cls(
         vision_config=vision_config.to_dict()   ,    qformer_config=qformer_config.to_dict()   ,    text_config=text_config.to_dict()   ,    **lowerCAmelCase_   ,    )






  def 							lowercase						(							self					:						Dict	)				->		Dict:
     __lowerCAmelCase				      =      copy.deepcopy(self.__dict__	)
     __lowerCAmelCase				      =      self.vision_config.to_dict()
     __lowerCAmelCase				      =      self.qformer_config.to_dict()
     __lowerCAmelCase				      =      self.text_config.to_dict()
     __lowerCAmelCase				      =      self.__class__.model_type
     return output





'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    __magic_name__				(						lowerCAmelCase   ,unittest.TestCase							):
       UpperCAmelCase		 =CodeGenTokenizer
       UpperCAmelCase		 =CodeGenTokenizerFast
       UpperCAmelCase		 =True
       UpperCAmelCase		 ={"add_prefix_space": True}
       UpperCAmelCase		 =False






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          _UpperCAmelCase  :  Optional[Any]				       =[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              '\u0120',
              '\u0120l',
              '\u0120n',
              '\u0120lo',
              '\u0120low',
              'er',
              '\u0120lowest',
              '\u0120newer',
              '\u0120wider',
              '<unk>',
              '<|endoftext|>',
          ]
          _UpperCAmelCase  :  Dict				       =dict(zip(snake_case    ,				range(len(snake_case))))
          _UpperCAmelCase  :  List[Any]				       =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
          _UpperCAmelCase  :  List[Any]				       ={'unk_token': '<unk>'}

          _UpperCAmelCase  :  List[Any]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file'])
          _UpperCAmelCase  :  List[str]				       =os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write(json.dumps(snake_case) + '\n')
          with open(self.merges_file    ,				'w'    ,				encoding='utf-8') as fp:
             fp.write('\n'.join(snake_case))






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    str:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''
          kwargs.update(self.special_tokens_map)
          return CodeGenTokenizerFast.from_pretrained(self.tmpdirname    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'
          _UpperCAmelCase  :  int				       ='lower newer'
          return input_text, output_text






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =CodeGenTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map)
          _UpperCAmelCase  :  str				       ='lower newer'
          _UpperCAmelCase  :  Union[str, Any]				       =['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
          _UpperCAmelCase  :  Any				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          _UpperCAmelCase  :  Any				       =tokens + [tokenizer.unk_token]
          _UpperCAmelCase  :  List[str]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






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




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase  :  Tuple				       =self.get_tokenizer()
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)

          _UpperCAmelCase  :  Union[str, Any]				       ='lower newer'

          # Testing tokenization
          _UpperCAmelCase  :  List[str]				       =tokenizer.tokenize(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids without special tokens
          _UpperCAmelCase  :  str				       =tokenizer.encode(snake_case    ,				add_special_tokens=snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  Optional[int]				       =rust_tokenizer.encode(snake_case    ,				add_special_tokens=snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing conversion to ids with special tokens
          _UpperCAmelCase  :  Dict				       =self.get_rust_tokenizer(add_prefix_space=snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer.encode(snake_case    ,				add_prefix_space=snake_case)
          _UpperCAmelCase  :  int				       =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case    ,				snake_case)

          # Testing the unknown token
          _UpperCAmelCase  :  List[str]				       =tokens + [rust_tokenizer.unk_token]
          _UpperCAmelCase  :  Union[str, Any]				       =[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case)    ,				snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Any:




          '''simple docstring'''
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass






       def      lowerCAmelCase						(     self    ,				snake_case=1_5)		->    Optional[int]:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase  :  Optional[Any]				       =self.rust_tokenizer_class.from_pretrained(snake_case    ,				**snake_case)

                # Simple input
                _UpperCAmelCase  :  List[str]				       ='This is a simple input'
                _UpperCAmelCase  :  Optional[Any]				       =['This is a simple input 1', 'This is a simple input 2']
                _UpperCAmelCase  :  int				       =('This is a simple input', 'This is a pair')
                _UpperCAmelCase  :  Union[str, Any]				       =[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Simple input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(snake_case    ,				tokenizer_r.encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length')

                # Pair input
                self.assertRaises(
                    snake_case    ,				tokenizer_r.batch_encode_plus    ,				snake_case    ,				max_length=snake_case    ,				padding='max_length'    ,				)






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




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='<pad>')

          # Simple input
          _UpperCAmelCase  :  List[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input looooooooong', 'This is a simple input']
          _UpperCAmelCase  :  List[str]				       =('This is a simple input', 'This is a pair')
          _UpperCAmelCase  :  Optional[int]				       =[
              ('This is a simple input loooooong', 'This is a simple input'),
              ('This is a simple pair loooooong', 'This is a simple pair'),
          ]

          _UpperCAmelCase  :  List[Any]				       =tokenizer.pad_token_id

          _UpperCAmelCase  :  Union[str, Any]				       =tokenizer(snake_case    ,				padding='max_length'    ,				max_length=3_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')
          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(*snake_case    ,				padding='max_length'    ,				max_length=6_0    ,				return_tensors='np')
          _UpperCAmelCase  :  List[Any]				       =tokenizer(snake_case    ,				padding=snake_case    ,				truncate=snake_case    ,				return_tensors='np')

          # s
          # test single string max_length padding
          self.assertEqual(out_s['input_ids'].shape[-1]    ,				3_0)
          self.assertTrue(pad_token_id in out_s['input_ids'])
          self.assertTrue(0 in out_s['attention_mask'])

          # s2
          # test automatic padding
          self.assertEqual(out_sa['input_ids'].shape[-1]    ,				3_3)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa['input_ids'][0])
          self.assertFalse(0 in out_sa['attention_mask'][0])
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa['input_ids'][1])
          self.assertTrue(0 in out_sa['attention_mask'][1])

          # p
          # test single pair max_length padding
          self.assertEqual(out_p['input_ids'].shape[-1]    ,				6_0)
          self.assertTrue(pad_token_id in out_p['input_ids'])
          self.assertTrue(0 in out_p['attention_mask'])

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa['input_ids'].shape[-1]    ,				5_2)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa['input_ids'][0])
          self.assertFalse(0 in out_pa['attention_mask'][0])
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa['input_ids'][1])
          self.assertTrue(0 in out_pa['attention_mask'][1])






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ='$$$'
          _UpperCAmelCase  :  Any				       =CodeGenTokenizer.from_pretrained(self.tmpdirname    ,				bos_token=snake_case    ,				add_bos_token=snake_case)

          _UpperCAmelCase  :  Optional[Any]				       ='This is a simple input'
          _UpperCAmelCase  :  Any				       =['This is a simple input 1', 'This is a simple input 2']

          _UpperCAmelCase  :  int				       =tokenizer.bos_token_id

          _UpperCAmelCase  :  Optional[Any]				       =tokenizer(snake_case)
          _UpperCAmelCase  :  Tuple				       =tokenizer(snake_case)

          self.assertEqual(out_s.input_ids[0]    ,				snake_case)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.decode(out_s.input_ids)
          _UpperCAmelCase  :  Any				       =tokenizer.batch_decode(out_sa.input_ids)

          self.assertEqual(decode_s.split()[0]    ,				snake_case)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))






       @slow
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono')

          _UpperCAmelCase  :  Optional[Any]				       ='\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
          _UpperCAmelCase  :  Tuple				       ='\nif len_a > len_b:      result = a\nelse:      result = b'

          _UpperCAmelCase  :  Optional[int]				       =tokenizer.encode(snake_case)
          _UpperCAmelCase  :  List[str]				       =['^#', re.escape('<|endoftext|>'), '^\'\'\'', '^"""', '\n\n\n']
          _UpperCAmelCase  :  Dict				       =tokenizer.decode(snake_case    ,				truncate_before_pattern=snake_case)
          self.assertEqual(snake_case    ,				snake_case)






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




          '''simple docstring'''
          pass

