

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 			__UpperCAmelCase    (	unittest.TestCase      ):




				def    a							( self	):
											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	):
											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_	)







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


_UpperCAmelCase		:       str						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       int						    =	{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			int	      =      '''canine'''




				def __init__( self				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1_6384				,       snake_case=16				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=0xE000				,       snake_case=0xE001				,       snake_case=4				,       snake_case=4				,       snake_case=8				,       snake_case=1_6384				,       snake_case=128				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case				,       **snake_case	)

											snake_case_          =   max_position_embeddings
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_act
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   initializer_range
											snake_case_          =   type_vocab_size
											snake_case_          =   layer_norm_eps

											# Character config:
											snake_case_          =   downsampling_rate
											snake_case_          =   upsampling_kernel_size
											snake_case_          =   num_hash_functions
											snake_case_          =   num_hash_buckets
											snake_case_          =   local_transformer_stride




