




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     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			"hf-internal-testing/tiny-random-t5"
							a    =			AutoTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)

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

							a    =			model.to_bettertransformer()

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

							a    =			model.generate(**__lowerCamelCase							)

							a    =			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(__lowerCamelCase							)

										a    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)

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

										a    =			model_reloaded.generate(**__lowerCamelCase							)
										self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase							)							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Tuple:
							a    =			"hf-internal-testing/tiny-random-t5"
							a    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)

							a    =			model.to_bettertransformer()

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

										a    =			model.reverse_bettertransformer()
										model.save_pretrained(__lowerCamelCase							)












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


__lowerCAmelCase		:				Optional[Any]       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				str       			=		{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Dict							 =					"""openai-gpt"""
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(      self				:     str					,      __lowerCamelCase				:     List[str]=4_04_78					,      __lowerCamelCase				:     List[Any]=5_12					,      __lowerCamelCase				:     List[str]=7_68					,      __lowerCamelCase				:     List[str]=12					,      __lowerCamelCase				:     Optional[Any]=12					,      __lowerCamelCase				:     str="gelu"					,      __lowerCamelCase				:     Optional[int]=0.1					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Any=1e-5					,      __lowerCamelCase				:     Optional[int]=0.02					,      __lowerCamelCase				:     Optional[int]="cls_index"					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     Tuple=0.1					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     List[str]:
							a    =			vocab_size
							a    =			n_positions
							a    =			n_embd
							a    =			n_layer
							a    =			n_head
							a    =			afn
							a    =			resid_pdrop
							a    =			embd_pdrop
							a    =			attn_pdrop
							a    =			layer_norm_epsilon
							a    =			initializer_range
							a    =			summary_type
							a    =			summary_use_proj
							a    =			summary_activation
							a    =			summary_first_dropout
							a    =			summary_proj_to_labels
							super().__init__(**__lowerCamelCase							)






