
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__SCREAMING_SNAKE_CASE      : Optional[Any]       =  get_logger(__name__)


class 		__magic_name__ (    enum.Enum     ):
		_lowerCAmelCase		=						"all_checks"
		_lowerCAmelCase		=						"basic_checks"
		_lowerCAmelCase		=						"no_checks"



class 		__magic_name__ (    lowercase_     ):
		pass



class 		__magic_name__ (    lowercase_     ):
		pass



class 		__magic_name__ (    lowercase_     ):
		pass



class 		__magic_name__ (    lowercase_     ):
		pass



def      UpperCAmelCase__				(							__magic_name__    :  Optional[dict]				,	__magic_name__    :  dict				,	__magic_name__    :  List[Any]=None	):


					'''simple docstring'''

					if expected_checksums is None:
										logger.info('''Unable to verify checksums.'''	)
										return
					if len(set(__lowercase	) - set(__lowercase	)	) > 0:
										raise ExpectedMoreDownloadedFiles(str(set(__lowercase	) - set(__lowercase	)	)	)
					if len(set(__lowercase	) - set(__lowercase	)	) > 0:
										raise UnexpectedDownloadedFile(str(set(__lowercase	) - set(__lowercase	)	)	)
					lowerCAmelCase		:       str							=  [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
					lowerCAmelCase		:       Dict							=  ''' for ''' + verification_name if verification_name is not None else ''''''
					if len(__lowercase	) > 0:
										raise NonMatchingChecksumError(
										    f'''Checksums didn\'t match{for_verification_name}:\n'''
										    f'''{bad_urls}\n'''
										    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''	)
					logger.info('''All the checksums matched successfully''' + for_verification_name	)


class 		__magic_name__ (    lowercase_     ):
		pass



class 		__magic_name__ (    lowercase_     ):
		pass



class 		__magic_name__ (    lowercase_     ):
		pass



class 		__magic_name__ (    lowercase_     ):
		pass



def      UpperCAmelCase__				(							__magic_name__    :  Optional[dict]				,	__magic_name__    :  dict	):


					'''simple docstring'''

					if expected_splits is None:
										logger.info('''Unable to verify splits sizes.'''	)
										return
					if len(set(__lowercase	) - set(__lowercase	)	) > 0:
										raise ExpectedMoreSplits(str(set(__lowercase	) - set(__lowercase	)	)	)
					if len(set(__lowercase	) - set(__lowercase	)	) > 0:
										raise UnexpectedSplits(str(set(__lowercase	) - set(__lowercase	)	)	)
					lowerCAmelCase		:       Tuple							=  [
					    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
					    for name in expected_splits
					    if expected_splits[name].num_examples != recorded_splits[name].num_examples
					]
					if len(__lowercase	) > 0:
										raise NonMatchingSplitsSizesError(str(__lowercase	)	)
					logger.info('''All the splits matched successfully.'''	)
def      UpperCAmelCase__				(							__magic_name__    :  str				,	__magic_name__    :  bool = True	):


					'''simple docstring'''

					if record_checksum:
										lowerCAmelCase		:       Dict							=  shaaaa()
										with open(__lowercase				,	'''rb'''	) as f:
															for chunk in iter(lambda: f.read(1 << 20	)				,	b''''''	):
																				m.update(__lowercase	)
															lowerCAmelCase		:       Tuple							=  m.hexdigest()
					else:
										lowerCAmelCase		:       Optional[Any]							=  None
					return {"num_bytes": os.path.getsize(__lowercase	), "checksum": checksum}
def      UpperCAmelCase__				(							__magic_name__    :  Union[str, Any]	):


					'''simple docstring'''

					if dataset_size and config.IN_MEMORY_MAX_SIZE:
										return dataset_size < config.IN_MEMORY_MAX_SIZE
					else:
										return False








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


__a   :							Dict						= logging.get_logger(__name__)

__a   :							Dict						= {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"gpt_bigcode"
     SCREAMING_SNAKE_CASE     =					["past_key_values"]
     SCREAMING_SNAKE_CASE     =					{
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      Dict=50_257			,						UpperCamelCase_							:      int=1_024			,						UpperCamelCase_							:      List[Any]=768			,						UpperCamelCase_							:      str=12			,						UpperCamelCase_							:      Union[str, Any]=12			,						UpperCamelCase_							:      Optional[Any]=None			,						UpperCamelCase_							:      Tuple="gelu_pytorch_tanh"			,						UpperCamelCase_							:      Optional[Any]=0.1			,						UpperCamelCase_							:      List[str]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      int=1e-5			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Dict=50_256			,						UpperCamelCase_							:      Optional[Any]=50_256			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      List[Any]=True			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						vocab_size
         __A       =						n_positions
         __A       =						n_embd
         __A       =						n_layer
         __A       =						n_head
         __A       =						n_inner
         __A       =						activation_function
         __A       =						resid_pdrop
         __A       =						embd_pdrop
         __A       =						attn_pdrop
         __A       =						layer_norm_epsilon
         __A       =						initializer_range
         __A       =						scale_attn_weights
         __A       =						use_cache
         __A       =						attention_softmax_in_fpaa
         __A       =						scale_attention_softmax_in_fpaa
         __A       =						multi_query

         __A       =						bos_token_id
         __A       =						eos_token_id

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




