





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     __snake_case       (							__lowerCamelCase   ,			__lowerCamelCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       @register_to_config
       def __init__(  self :    Dict							,				A :    int							,				A :    int							,				A :    int							,				A :    float							,				A :    int							,				A :    int							,				A :    int							,				A :    int							,				A :    str							,				A :    bool = False							,				):
         super().__init__()

         __snake_case:						Tuple   =						nn.Embedding(A							,				A		)

         __snake_case:						str   =						nn.Embedding(A							,				A		)
         __snake_case:						List[str]   =						False

         __snake_case:						Tuple   =						nn.Dropout(p=A		)

         __snake_case:						Optional[int]   =						TaConfig(
             vocab_size=A							,				d_model=A							,				num_heads=A							,				d_kv=A							,				d_ff=A							,				dropout_rate=A							,				feed_forward_proj=A							,				is_decoder=A							,				is_encoder_decoder=A							,				)

         __snake_case:						str   =						nn.ModuleList()
         for lyr_num in range(A		):
           __snake_case:						List[str]   =						TaBlock(A		)
           self.encoders.append(A		)

         __snake_case:						Tuple   =						TaLayerNorm(A		)
         __snake_case:						Tuple   =						nn.Dropout(p=A		)






       def 							UpperCAmelCase__ (  self :    Dict							,				A :    Any							,				A :    str		):
         __snake_case:						Union[str, Any]   =						self.token_embedder(A		)

         __snake_case:						Any   =						encoder_input_tokens.shape[1]
         __snake_case:						Dict   =						torch.arange(A							,				device=encoder_input_tokens.device		)
         x += self.position_encoding(A		)

         __snake_case:						Optional[int]   =						self.dropout_pre(A		)

         # inverted the attention mask
         __snake_case:						Optional[Any]   =						encoder_input_tokens.size()
         __snake_case:						str   =						self.get_extended_attention_mask(A							,				A		)

         for lyr in self.encoders:
           __snake_case:						Union[str, Any]   =						lyr(A							,				A		)[0]
         __snake_case:						Union[str, Any]   =						self.layer_norm(A		)

         return self.dropout_post(A		), encoder_inputs_mask








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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			tokenizer('This is me'			,  return_tensors='pt'	)

				_snake_case		=			model.to_bettertransformer()

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

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			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(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

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

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

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

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)