

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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Tuple									=  'mgp-str'



					def __init__(			self    ,	lowerCAmelCase=[32, 128]    ,	lowerCAmelCase=4    ,	lowerCAmelCase=3    ,	lowerCAmelCase=27    ,	lowerCAmelCase=38    ,	lowerCAmelCase=5_0257    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=4.0    ,	lowerCAmelCase=True    ,	lowerCAmelCase=False    ,	lowerCAmelCase=1e-5    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=False    ,	lowerCAmelCase=0.02    ,	**lowerCAmelCase    ,	):
							super().__init__(**lowerCAmelCase     )

							UpperCAmelCase_						=				image_size
							UpperCAmelCase_						=				patch_size
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				max_token_length
							UpperCAmelCase_						=				num_character_labels
							UpperCAmelCase_						=				num_bpe_labels
							UpperCAmelCase_						=				num_wordpiece_labels
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				mlp_ratio
							UpperCAmelCase_						=				distilled
							UpperCAmelCase_						=				layer_norm_eps
							UpperCAmelCase_						=				drop_rate
							UpperCAmelCase_						=				qkv_bias
							UpperCAmelCase_						=				attn_drop_rate
							UpperCAmelCase_						=				drop_path_rate
							UpperCAmelCase_						=				output_aa_attentions
							UpperCAmelCase_						=				initializer_range






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     )

