





'''simple docstring'''




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


if TYPE_CHECKING:
  pass

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


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

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







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""align_text_model"""




 def __init__(					self       :    str	,			snake_case_       :    Any=3_0_5_2_2	,			snake_case_       :    Dict=7_6_8	,			snake_case_       :    Union[str, Any]=1_2	,			snake_case_       :    Dict=1_2	,			snake_case_       :    Optional[int]=3_0_7_2	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[str]=0.1	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Dict=5_1_2	,			snake_case_       :    Optional[Any]=2	,			snake_case_       :    int=0.0_2	,			snake_case_       :    Dict=1e-12	,			snake_case_       :    List[Any]=0	,			snake_case_       :    Optional[Any]="absolute"	,			snake_case_       :    Dict=True	,			**snake_case_       :    Dict	,			):
      super().__init__(**snake_case_    )

      _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       :    Optional[int]	,			snake_case_       :    Union[str, os.PathLike]	,			**snake_case_       :    Optional[int]    ):
      cls._set_token_in_kwargs(snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   cls.get_config_dict(snake_case_	,			**snake_case_    )

      # 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(snake_case_	,			**snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""align_vision_model"""




 def __init__(					self       :    int	,			snake_case_       :    int = 3	,			snake_case_       :    int = 6_0_0	,			snake_case_       :    float = 2.0	,			snake_case_       :    float = 3.1	,			snake_case_       :    int = 8	,			snake_case_       :    List[int] = [3, 3, 5, 3, 5, 5, 3]	,			snake_case_       :    List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]	,			snake_case_       :    List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]	,			snake_case_       :    List[int] = []	,			snake_case_       :    List[int] = [1, 2, 2, 2, 1, 2, 1]	,			snake_case_       :    List[int] = [1, 2, 2, 3, 3, 4, 1]	,			snake_case_       :    List[int] = [1, 6, 6, 6, 6, 6, 6]	,			snake_case_       :    float = 0.2_5	,			snake_case_       :    str = "swish"	,			snake_case_       :    int = 2_5_6_0	,			snake_case_       :    str = "mean"	,			snake_case_       :    float = 0.0_2	,			snake_case_       :    float = 0.0_0_1	,			snake_case_       :    float = 0.9_9	,			snake_case_       :    float = 0.2	,			**snake_case_       :    Dict	,			):
      super().__init__(**snake_case_    )

      _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(snake_case_    ) * 4




 @classmethod
 def      lowercase	(					cls       :    int	,			snake_case_       :    Union[str, os.PathLike]	,			**snake_case_       :    Tuple    ):
      cls._set_token_in_kwargs(snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   cls.get_config_dict(snake_case_	,			**snake_case_    )

      # 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(snake_case_	,			**snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""align"""
 _lowerCamelCase  : Any           =							True




 def __init__(					self       :    Tuple	,			snake_case_       :    Dict=None	,			snake_case_       :    Dict=None	,			snake_case_       :    Tuple=6_4_0	,			snake_case_       :    List[str]=1.0	,			snake_case_       :    Dict=0.0_2	,			**snake_case_       :    Optional[int]	,			):
      super().__init__(**snake_case_    )

      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(**snake_case_    )
      _UpperCAmelCase     							=   AlignVisionConfig(**snake_case_    )

      _UpperCAmelCase     							=   projection_dim
      _UpperCAmelCase     							=   temperature_init_value
      _UpperCAmelCase     							=   initializer_range




 @classmethod
 def      lowercase	(					cls       :    Optional[Any]	,			snake_case_       :    AlignTextConfig	,			snake_case_       :    AlignVisionConfig	,			**snake_case_       :    Union[str, Any]    ):
      return cls(text_config=text_config.to_dict()	,			vision_config=vision_config.to_dict()	,			**snake_case_    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   copy.deepcopy(self.__dict__    )
      _UpperCAmelCase     							=   self.text_config.to_dict()
      _UpperCAmelCase     							=   self.vision_config.to_dict()
      _UpperCAmelCase     							=   self.__class__.model_type
      return output









from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


