




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


SCREAMING_SNAKE_CASE__ :   List[str]						=     logging.get_logger(__name__)




class 	UpperCamelCase__			(lowerCAmelCase__  ):

				'''simple docstring'''



				lowerCamelCase_   :			List[str]			  =   """speech-encoder-decoder"""
				lowerCamelCase_   :			int			  =   True







				def __init__(					self  ,						**UpperCamelCase__  )  ->   Union[str, Any]:
											super().__init__(**UpperCamelCase__  )
											if "encoder" not in kwargs or "decoder" not in kwargs:
																		raise ValueError(
																		    F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
																		    F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )

											lowerCamelCase     :   List[Any]    				=      kwargs.pop("encoder"  )
											lowerCamelCase     :   Union[str, Any]    				=      encoder_config.pop("model_type"  )
											lowerCamelCase     :   Tuple    				=      kwargs.pop("decoder"  )
											lowerCamelCase     :   str    				=      decoder_config.pop("model_type"  )

											lowerCamelCase     :   Union[str, Any]    				=      AutoConfig.for_model(UpperCamelCase__  ,						**UpperCamelCase__  )
											lowerCamelCase     :   Union[str, Any]    				=      AutoConfig.for_model(UpperCamelCase__  ,						**UpperCamelCase__  )
											lowerCamelCase     :   List[Any]    				=      True







				@classmethod
				def 					_lowercase  (					cls  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						**UpperCamelCase__  )  ->   PretrainedConfig:
											logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"  )
											lowerCamelCase     :   Optional[int]    				=      True
											lowerCamelCase     :   Optional[int]    				=      True

											return cls(encoder=encoder_config.to_dict()  ,						decoder=decoder_config.to_dict()  ,						**UpperCamelCase__  )







				def 					_lowercase  (					self  )  ->   int:
											lowerCamelCase     :   Dict    				=      copy.deepcopy(self.__dict__  )
											lowerCamelCase     :   Optional[Any]    				=      self.encoder.to_dict()
											lowerCamelCase     :   Tuple    				=      self.decoder.to_dict()
											lowerCamelCase     :   Tuple    				=      self.__class__.model_type
											return output












import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__			(unittest.TestCase  ):

    '''simple docstring'''



    @slow
    @unittest.skip("Model is not available."  )
    def 					_lowercase  (					self  )  ->   List[Any]:
           lowerCamelCase     :   List[Any]    				=      "nvidia/megatron-gpt2-345m/"
           if "MYDIR" in os.environ:
                  lowerCamelCase     :   Union[str, Any]    				=      os.path.join(os.environ["MYDIR"]  ,						UpperCamelCase__  )
           lowerCamelCase     :   Any    				=      GPTaLMHeadModel.from_pretrained(UpperCamelCase__  )
           model.to(UpperCamelCase__  )
           model.half()

           lowerCamelCase     :   Dict    				=      torch.tensor(
               [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,						device=UpperCamelCase__  ,						dtype=torch.long  ,						)

           with torch.no_grad():
                  lowerCamelCase     :   Any    				=      model(UpperCamelCase__  ).logits

           lowerCamelCase     :   Union[str, Any]    				=      torch.Size((1, 9, 5_0257)  )
           self.assertEqual(output.shape  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[str]    				=      torch.tensor(
               [
                   4.9414,
                   -0.2920,
                   -1.2148,
                   -4.0273,
                   -0.5161,
                   -5.2109,
                   -1.2412,
                   -1.8301,
                   -1.7734,
                   -4.7148,
                   -0.2317,
                   -1.0811,
                   -2.1777,
                   0.4141,
                   -3.7969,
                   -4.0586,
                   -2.5332,
                   -3.3809,
                   4.3867,
               ]  ,						device=UpperCamelCase__  ,						dtype=torch.half  ,						)

           for i in range(19  ):
                  lowerCamelCase				,   lowerCamelCase     :   Optional[int]    				=      8 * i // 17, 2792 * i  # along the diagonal
                  lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      output[0, r, c], expected_diag[i]
                  lowerCamelCase     :   List[str]    				=      F'''row={r} col={c} computed={computed} expected={expected}'''
                  self.assertAlmostEqual(UpperCamelCase__  ,						UpperCamelCase__  ,						delta=1e-4  ,						msg=UpperCamelCase__  )






