


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


_lowercase       :      str	=	logging.get_logger(__name__)

_lowercase       :      Dict	=	{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'trocr'
				lowerCAmelCase 		= ['past_key_values']
				lowerCAmelCase 		= {
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}

				def __init__(						self				,   a__=5_0265				,   a__=1024				,   a__=12				,   a__=16				,   a__=4096				,   a__="gelu"				,   a__=512				,   a__=0.1				,   a__=0.0				,   a__=0.0				,   a__=2				,   a__=0.02				,   a__=0.0				,   a__=True				,   a__=False				,   a__=True				,   a__=True				,   a__=1				,   a__=0				,   a__=2				,   **a__				,   )	->				int:
					A								=				vocab_size
					A								=				d_model
					A								=				decoder_layers
					A								=				decoder_attention_heads
					A								=				decoder_ffn_dim
					A								=				activation_function
					A								=				max_position_embeddings
					A								=				dropout
					A								=				attention_dropout
					A								=				activation_dropout
					A								=				init_std
					A								=				decoder_layerdrop
					A								=				use_cache
					A								=				scale_embedding
					A								=				use_learned_position_embeddings
					A								=				layernorm_embedding

					super().__init__(
					    pad_token_id=a__				,   bos_token_id=a__				,   eos_token_id=a__				,   decoder_start_token_id=a__				,   **a__				,   )







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



