





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


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''gpt_bigcode'''
					lowerCAmelCase_							=	['''past_key_values''']
					lowerCAmelCase_							=	{
					    '''hidden_size''': '''n_embd''',
					    '''max_position_embeddings''': '''n_positions''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}




					def __init__(		self				,						lowerCAmelCase_=5_0257				,						lowerCAmelCase_=1024				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=None				,						lowerCAmelCase_="gelu_pytorch_tanh"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=1E-5				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=5_0256				,						lowerCAmelCase_=5_0256				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						**lowerCAmelCase_				,						)		->							Optional[Any]:
										_snake_case									=				vocab_size
										_snake_case									=				n_positions
										_snake_case									=				n_embd
										_snake_case									=				n_layer
										_snake_case									=				n_head
										_snake_case									=				n_inner
										_snake_case									=				activation_function
										_snake_case									=				resid_pdrop
										_snake_case									=				embd_pdrop
										_snake_case									=				attn_pdrop
										_snake_case									=				layer_norm_epsilon
										_snake_case									=				initializer_range
										_snake_case									=				scale_attn_weights
										_snake_case									=				use_cache
										_snake_case									=				attention_softmax_in_fpaa
										_snake_case									=				scale_attention_softmax_in_fpaa
										_snake_case									=				multi_query

										_snake_case									=				bos_token_id
										_snake_case									=				eos_token_id

										super().__init__(bos_token_id=lowerCAmelCase_				,						eos_token_id=lowerCAmelCase_				,						**lowerCAmelCase_  )










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_      							=     {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_byta import ByTaTokenizer
else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



