
"""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


UpperCamelCase			:       Union[str, Any]									=				logging.get_logger(__name__)

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

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"instructblip_vision_model"





     def __init__(			self			,  __UpperCAmelCase=1408			,  __UpperCAmelCase=6144			,  __UpperCAmelCase=39			,  __UpperCAmelCase=16			,  __UpperCAmelCase=224			,  __UpperCAmelCase=14			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=1E-6			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-10			,  __UpperCAmelCase=True			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

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







     @classmethod
     def 		UpperCAmelCase      (			cls			,  __UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      cls._set_token_in_kwargs(__UpperCAmelCase   )

      __UpperCamelCase						,			__UpperCamelCase       						= cls.get_config_dict(__UpperCAmelCase			,  **__UpperCAmelCase   )

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


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"instructblip_qformer"





     def __init__(			self			,  __UpperCAmelCase=3_0522			,  __UpperCAmelCase=768			,  __UpperCAmelCase=12			,  __UpperCAmelCase=12			,  __UpperCAmelCase=3072			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=512			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1E-12			,  __UpperCAmelCase=0			,  __UpperCAmelCase="absolute"			,  __UpperCAmelCase=2			,  __UpperCAmelCase=1408			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      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       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= position_embedding_type
      __UpperCamelCase       						= cross_attention_frequency
      __UpperCamelCase       						= encoder_hidden_size







     @classmethod
     def 		UpperCAmelCase      (			cls			,  __UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      cls._set_token_in_kwargs(__UpperCAmelCase   )

      __UpperCamelCase						,			__UpperCamelCase       						= cls.get_config_dict(__UpperCAmelCase			,  **__UpperCAmelCase   )

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


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"instructblip"
     lowercase		     =		True





     def __init__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=32			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

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

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

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

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

      __UpperCamelCase       						= self.text_config.tie_word_embeddings
      __UpperCamelCase       						= self.text_config.is_encoder_decoder

      __UpperCamelCase       						= num_query_tokens
      __UpperCamelCase       						= self.vision_config.hidden_size
      __UpperCamelCase       						= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      __UpperCamelCase       						= 1.0
      __UpperCamelCase       						= 0.0_2





     @classmethod
     def 		UpperCAmelCase      (			cls			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= copy.deepcopy(self.__dict__   )
      __UpperCamelCase       						= self.vision_config.to_dict()
      __UpperCamelCase       						= self.qformer_config.to_dict()
      __UpperCamelCase       						= self.text_config.to_dict()
      __UpperCamelCase       						= self.__class__.model_type
      return output





"""simple docstring"""

import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase			:       Union[str, Any]									=				"__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase			:       List[Any]									=				"Dummy User"
UpperCamelCase			:       List[Any]									=				"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"

UpperCamelCase			:       Any									=				"https://hub-ci.huggingface.co"
UpperCamelCase			:       str									=				CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase			:       Optional[Any]									=				CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase			:       Optional[int]									=				Path("~/.huggingface/hub_ci_token").expanduser()






@pytest.fixture
def 							A		(       snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 monkeypatch.setattr(
     'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			List[Any]:
 monkeypatch.setattr('datasets.config.HF_ENDPOINT'       ,      snake_case	)
 monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :Optional[int]	)   ->			Dict:
 monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :List[Any]       ,      snake_case   :Tuple	)   ->			Tuple:
 HfFolder.save_token(snake_case	)
 yield
 HfFolder.delete_token()






@pytest.fixture(scope='session'	)
def 							A		(       )   ->			List[Any]:
 return HfApi(endpoint=snake_case	)






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi	)   ->			List[Any]:
 __UpperCamelCase       						= HfFolder.get_token()
 HfFolder.save_token(snake_case	)
 yield CI_HUB_USER_TOKEN
 if previous_token is not None:
  HfFolder.save_token(snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			str:
 def _cleanup_repo(snake_case   :Union[str, Any]	):
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)

 return _cleanup_repo






@pytest.fixture
def 							A		(       snake_case   :List[str]	)   ->			Any:
 @contextmanager
 def _temporary_repo(snake_case   :Tuple	):
  try:
   yield repo_id
  finally:
   cleanup_repo(snake_case	)

 return _temporary_repo






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :Dict       ,      snake_case   :Dict	)   ->			List[str]:
 __UpperCamelCase       						= f'repo_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data/text_data.txt'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :Optional[int]       ,      snake_case   :str       ,      snake_case   :Tuple	)   ->			Any:
 return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[Any]       ,      snake_case   :str	)   ->			Optional[int]:
 __UpperCamelCase       						= f'repo_zipped_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[int]	)   ->			str:
 return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[str]       ,      snake_case   :Any	)   ->			List[Any]:
 __UpperCamelCase       						= f'repo_zipped_img_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[int]	)   ->			Optional[int]:
 return hf_private_dataset_repo_zipped_img_data_



