import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )
@require_torch
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	3
   __a							=	2_50

   __a							=	ids_tensor((batch_size, length)  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	torch.ones((batch_size, length)  ,				device=__SCREAMING_SNAKE_CASE  ,				dtype=torch.float							) / length
   return input_ids, scores

  def 		_UpperCAmelCase (		self			:   int							):
   __a							,  __a							=	self._get_tensors(5							)

   __a							=	StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10							),
           MaxTimeCriteria(max_time=0.1							),
       ]							)

   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							,  __a							=	self._get_tensors(9							)
   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							,  __a							=	self._get_tensors(10							)
   self.assertTrue(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	MaxLengthCriteria(max_length=10							)

   __a							,  __a							=	self._get_tensors(5							)
   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							,  __a							=	self._get_tensors(9							)
   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							,  __a							=	self._get_tensors(10							)
   self.assertTrue(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	MaxNewTokensCriteria(start_length=5  ,				max_new_tokens=5							)

   __a							,  __a							=	self._get_tensors(5							)
   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							,  __a							=	self._get_tensors(9							)
   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							,  __a							=	self._get_tensors(10							)
   self.assertTrue(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							=	StoppingCriteriaList([criteria]							)
   self.assertEqual(criteria_list.max_length  ,				10							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							,  __a							=	self._get_tensors(5							)

   __a							=	MaxTimeCriteria(max_time=0.1							)
   self.assertFalse(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

   __a							=	MaxTimeCriteria(max_time=0.1  ,				initial_timestamp=time.time() - 0.2							)
   self.assertTrue(criteria(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10							)]							)  ,				10							)

   with self.assertWarns(__SCREAMING_SNAKE_CASE							):
    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10							)]							)  ,				11							)

   __a							=	validate_stopping_criteria(StoppingCriteriaList()  ,				11							)

   self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				1							)



import copy
import os
from typing import Union

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


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

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip_text_model"""

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str=3_05_24  ,				__SCREAMING_SNAKE_CASE			:   Any=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Tuple=30_72  ,				__SCREAMING_SNAKE_CASE			:   Any=7_68  ,				__SCREAMING_SNAKE_CASE			:   Tuple=12  ,				__SCREAMING_SNAKE_CASE			:   Dict=8  ,				__SCREAMING_SNAKE_CASE			:   int=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   int=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.0  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.0  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=3_05_22  ,				__SCREAMING_SNAKE_CASE			:   Any=2  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1_02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   super().__init__(
       pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				sep_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	encoder_hidden_size
   __a							=	intermediate_size
   __a							=	projection_dim
   __a							=	hidden_dropout_prob
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	max_position_embeddings
   __a							=	layer_norm_eps
   __a							=	hidden_act
   __a							=	initializer_range
   __a							=	attention_probs_dropout_prob
   __a							=	is_decoder
   __a							=	use_cache

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the text config dict if we are loading from BlipConfig
   if config_dict.get("model_type"							) == "blip":
    __a							=	config_dict["text_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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip_vision_model"""

  def __init__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str=7_68  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=30_72  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=3_84  ,				__SCREAMING_SNAKE_CASE			:   str=16  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-10  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	projection_dim
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	patch_size
   __a							=	image_size
   __a							=	initializer_range
   __a							=	attention_dropout
   __a							=	layer_norm_eps
   __a							=	hidden_act

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the vision config dict if we are loading from BlipConfig
   if config_dict.get("model_type"							) == "blip":
    __a							=	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(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   int=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Any=2_56  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   if text_config is None:
    __a							=	{}
    logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."							)

   if vision_config is None:
    __a							=	{}
    logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."							)

   __a							=	BlipTextConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	BlipVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	self.vision_config.hidden_size

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	1.0
   __a							=	0.02
   __a							=	image_text_hidden_size

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Any  ,				__SCREAMING_SNAKE_CASE			:   BlipTextConfig  ,				__SCREAMING_SNAKE_CASE			:   BlipVisionConfig  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   return cls(text_config=text_config.to_dict()  ,				vision_config=vision_config.to_dict()  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.text_config.to_dict()
   __a							=	self.vision_config.to_dict()
   __a							=	self.__class__.model_type
   return output


