import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
      pass

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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[str]      =			'align_text_model'


   def __init__(self     :						Dict       ,  __UpperCAmelCase     :						List[str]=3_0_5_2_2       ,  __UpperCAmelCase     :						str=7_6_8       ,  __UpperCAmelCase     :						int=1_2       ,  __UpperCAmelCase     :						List[str]=1_2       ,  __UpperCAmelCase     :						Any=3_0_7_2       ,  __UpperCAmelCase     :						Any="gelu"       ,  __UpperCAmelCase     :						Optional[int]=0.1       ,  __UpperCAmelCase     :						int=0.1       ,  __UpperCAmelCase     :						Dict=5_1_2       ,  __UpperCAmelCase     :						Dict=2       ,  __UpperCAmelCase     :						Tuple=0.02       ,  __UpperCAmelCase     :						int=1E-12       ,  __UpperCAmelCase     :						Dict=0       ,  __UpperCAmelCase     :						Optional[int]="absolute"       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				Dict:
         """simple docstring"""



         super().__init__(**__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__								=  type_vocab_size
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  layer_norm_eps
         UpperCAmelCase__								=  position_embedding_type
         UpperCAmelCase__								=  use_cache
         UpperCAmelCase__								=  pad_token_id





   @classmethod
   def 			lowercase_      (cls     :						Tuple       ,  __UpperCAmelCase     :						Union[str, os.PathLike]       ,  **__UpperCAmelCase     :						int		)						->				"PretrainedConfig":
         """simple docstring"""



         cls._set_token_in_kwargs(__UpperCAmelCase		)

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

         # get the text config dict if we are loading from AlignConfig
         if config_dict.get("model_type"		) == "align":
               UpperCAmelCase__								=  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(__UpperCAmelCase       ,  **__UpperCAmelCase		)





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[Any]      =			'align_vision_model'


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						int = 3       ,  __UpperCAmelCase     :						int = 6_0_0       ,  __UpperCAmelCase     :						float = 2.0       ,  __UpperCAmelCase     :						float = 3.1       ,  __UpperCAmelCase     :						int = 8       ,  __UpperCAmelCase     :						List[int] = [3, 3, 5, 3, 5, 5, 3]       ,  __UpperCAmelCase     :						List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]       ,  __UpperCAmelCase     :						List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]       ,  __UpperCAmelCase     :						List[int] = []       ,  __UpperCAmelCase     :						List[int] = [1, 2, 2, 2, 1, 2, 1]       ,  __UpperCAmelCase     :						List[int] = [1, 2, 2, 3, 3, 4, 1]       ,  __UpperCAmelCase     :						List[int] = [1, 6, 6, 6, 6, 6, 6]       ,  __UpperCAmelCase     :						float = 0.25       ,  __UpperCAmelCase     :						str = "swish"       ,  __UpperCAmelCase     :						int = 2_5_6_0       ,  __UpperCAmelCase     :						str = "mean"       ,  __UpperCAmelCase     :						float = 0.02       ,  __UpperCAmelCase     :						float = 0.001       ,  __UpperCAmelCase     :						float = 0.99       ,  __UpperCAmelCase     :						float = 0.2       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				Union[str, Any]:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)

         UpperCAmelCase__								=  num_channels
         UpperCAmelCase__								=  image_size
         UpperCAmelCase__								=  width_coefficient
         UpperCAmelCase__								=  depth_coefficient
         UpperCAmelCase__								=  depth_divisor
         UpperCAmelCase__								=  kernel_sizes
         UpperCAmelCase__								=  in_channels
         UpperCAmelCase__								=  out_channels
         UpperCAmelCase__								=  depthwise_padding
         UpperCAmelCase__								=  strides
         UpperCAmelCase__								=  num_block_repeats
         UpperCAmelCase__								=  expand_ratios
         UpperCAmelCase__								=  squeeze_expansion_ratio
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dim
         UpperCAmelCase__								=  pooling_type
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  batch_norm_eps
         UpperCAmelCase__								=  batch_norm_momentum
         UpperCAmelCase__								=  drop_connect_rate
         UpperCAmelCase__								=  sum(__UpperCAmelCase		) * 4





   @classmethod
   def 			lowercase_      (cls     :						List[str]       ,  __UpperCAmelCase     :						Union[str, os.PathLike]       ,  **__UpperCAmelCase     :						Any		)						->				"PretrainedConfig":
         """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 AlignConfig
         if config_dict.get("model_type"		) == "align":
               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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Any      =			'align'
   __UpperCAmelCase : str      =			True


   def __init__(self     :						str       ,  __UpperCAmelCase     :						Any=None       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						Tuple=6_4_0       ,  __UpperCAmelCase     :						int=1.0       ,  __UpperCAmelCase     :						List[str]=0.02       ,  **__UpperCAmelCase     :						Optional[int]       ,  )						->				Any:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)

         if text_config is None:
               UpperCAmelCase__								=  {}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."		)

         if vision_config is None:
               UpperCAmelCase__								=  {}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."		)

         UpperCAmelCase__								=  AlignTextConfig(**__UpperCAmelCase		)
         UpperCAmelCase__								=  AlignVisionConfig(**__UpperCAmelCase		)

         UpperCAmelCase__								=  projection_dim
         UpperCAmelCase__								=  temperature_init_value
         UpperCAmelCase__								=  initializer_range


   @classmethod
   def 			lowercase_      (cls     :						int       ,  __UpperCAmelCase     :						AlignTextConfig       ,  __UpperCAmelCase     :						AlignVisionConfig       ,  **__UpperCAmelCase     :						int		)						->				Dict:
         """simple docstring"""



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





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



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










import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase_    =				datasets.logging.get_logger(__name__)


lowerCAmelCase_    =				'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

lowerCAmelCase_    =				'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

lowerCAmelCase_    =				'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

lowerCAmelCase_    =				{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION     )
class 					__lowerCAmelCase       (			datasets.Metric     ):






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


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION					,							citation=_CITATION					,							homepage='''https://github.com/google-research/bleurt'''					,							inputs_description=_KWARGS_DESCRIPTION					,							features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''					,							id='''sequence'''    ),
							            '''references''': datasets.Value('''string'''					,							id='''sequence'''    ),
							        }    )					,							codebase_urls=['''https://github.com/google-research/bleurt''']					,							reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']					,							)






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							if self.config_name == "default":
											logger.warning(
											    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
											    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''    )
											snake_case_						:    Dict				=  '''bleurt-base-128'''

							if self.config_name.lower() in CHECKPOINT_URLS:
											snake_case_						:    Optional[int]				=  self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
											snake_case_						:    Union[str, Any]				=  self.config_name.upper()

							else:
											raise KeyError(
											    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''    )

							# download the model checkpoint specified by self.config_name and set up the scorer
							snake_case_						:    Any				=  dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
							snake_case_						:    Dict				=  score.BleurtScorer(os.path.join(__magic_name__					,							__magic_name__    )    )






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


							'''simple docstring'''



							snake_case_						:    Dict				=  self.scorer.score(references=__magic_name__					,							candidates=__magic_name__    )
							return {"scores": scores}
