
'''simple docstring'''







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():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)










"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  '''llama'''
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  ['''past_key_values''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_20_00		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=1_10_08		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="silu"		,SCREAMING_SNAKE_CASE__=20_48		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-6		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :int						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :List[str]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[Any]						=  num_attention_heads

             # for backward compatibility
             if num_key_value_heads is None:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  num_attention_heads

             __SCREAMING_SNAKE_CASE   :str						=  num_key_value_heads
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :List[str]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  rms_norm_eps
             __SCREAMING_SNAKE_CASE   :Dict						=  pretraining_tp
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  use_cache
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  rope_scaling
             self._rope_scaling_validation()

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,tie_word_embeddings=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             if self.rope_scaling is None:
                   return

             if not isinstance(self.rope_scaling		,SCREAMING_SNAKE_CASE__							) or len(self.rope_scaling							) != 2:
                   raise ValueError(
                       '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                       f'''got {self.rope_scaling}'''							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.rope_scaling.get('''type'''		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.rope_scaling.get('''factor'''		,SCREAMING_SNAKE_CASE__							)
             if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                   raise ValueError(
                       f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''							)
             if rope_scaling_factor is None or not isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) or rope_scaling_factor <= 1.0:
                   raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''							)