

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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "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 	lowerCamelCase      (						__lowerCAmelCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Dict									=  "canine"



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

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

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






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  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

