



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


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

__lowerCamelCase     :							Union[str, Any]  							=				{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}





class  __magic_name__   (							a__						):
		lowercase      :       Tuple          ='''ctrl'''
		lowercase      :       Any          =['''past_key_values''']
		lowercase      :       int          ={
		    '''max_position_embeddings''': '''n_positions''',
		    '''hidden_size''': '''n_embd''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}






		def __init__(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Dict=24_65_34				,		UpperCamelCase__      :     str=2_56				,		UpperCamelCase__      :     Optional[int]=12_80				,		UpperCamelCase__      :     Union[str, Any]=81_92				,		UpperCamelCase__      :     str=48				,		UpperCamelCase__      :     Tuple=16				,		UpperCamelCase__      :     Optional[int]=0.1				,		UpperCamelCase__      :     Dict=0.1				,		UpperCamelCase__      :     int=1e-6				,		UpperCamelCase__      :     Optional[int]=0.02				,		UpperCamelCase__      :     Optional[Any]=True				,		**UpperCamelCase__      :     str				,		)				->					Union[str, Any]:
								'''simple docstring'''







								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     n_positions
								UpperCAmelCase         =     n_embd
								UpperCAmelCase         =     n_layer
								UpperCAmelCase         =     n_head
								UpperCAmelCase         =     dff
								UpperCAmelCase         =     resid_pdrop
								UpperCAmelCase         =     embd_pdrop
								UpperCAmelCase         =     layer_norm_epsilon
								UpperCAmelCase         =     initializer_range

								UpperCAmelCase         =     use_cache

								super().__init__(**_A      )








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch





@require_torch
@require_optimum
@slow
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoTokenizer.from_pretrained(UpperCamelCase__      )
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer("This is me"				,		return_tensors="pt"      )

								UpperCAmelCase         =     model.to_bettertransformer()

								self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								UpperCAmelCase         =     model.generate(**UpperCamelCase__      )

								UpperCAmelCase         =     model.reverse_bettertransformer()

								self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

														self.assertFalse(
														    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

														UpperCAmelCase         =     model_reloaded.generate(**UpperCamelCase__      )
														self.assertTrue(torch.allclose(UpperCamelCase__				,		UpperCamelCase__      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
														with self.assertRaises(UpperCamelCase__      ):
																				model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     model.reverse_bettertransformer()
														model.save_pretrained(UpperCamelCase__      )



