

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


UpperCAmelCase_							:					Optional[Any]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					Optional[Any]		 =   {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[Any]     					=				'''open-llama'''




	def __init__(     self						:     Union[str, Any]					,      __A						:     Dict=10_0000					,      __A						:     Optional[int]=4096					,      __A						:     str=1_1008					,      __A						:     str=32					,      __A						:     Union[str, Any]=32					,      __A						:     str="silu"					,      __A						:     Tuple=2048					,      __A						:     Optional[Any]=0.0_2					,      __A						:     List[Any]=1e-6					,      __A						:     Dict=True					,      __A						:     Optional[Any]=0					,      __A						:     str=1					,      __A						:     Any=2					,      __A						:     Optional[int]=False					,      __A						:     List[Any]=True					,      __A						:     Any=0.1					,      __A						:     int=0.1					,      __A						:     Optional[int]=True					,      __A						:     Optional[int]=True					,      __A						:     Optional[Any]=None					,      **__A						:     str					,      ):
		__A :			List[str]		    =			vocab_size
		__A :			Dict		    =			max_position_embeddings
		__A :			Dict		    =			hidden_size
		__A :			str		    =			intermediate_size
		__A :			Tuple		    =			num_hidden_layers
		__A :			Optional[Any]		    =			num_attention_heads
		__A :			Optional[int]		    =			hidden_act
		__A :			List[Any]		    =			initializer_range
		__A :			Tuple		    =			rms_norm_eps
		__A :			str		    =			use_cache
		__A :			Optional[int]		    =			kwargs.pop(
		    """use_memorry_efficient_attention"""					,      __A							)
		__A :			Union[str, Any]		    =			hidden_dropout_prob
		__A :			Optional[Any]		    =			attention_dropout_prob
		__A :			str		    =			use_stable_embedding
		__A :			Dict		    =			shared_input_output_embedding
		__A :			List[str]		    =			rope_scaling
		self._rope_scaling_validation()

		super().__init__(
		    pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A					,      tie_word_embeddings=__A					,      **__A					,      )




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		if self.rope_scaling is None:
			return

		if not isinstance(self.rope_scaling					,      __A							) or len(self.rope_scaling							) != 2:
			raise ValueError(
			    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
			    F"""got {self.rope_scaling}"""							)
		__A :			Dict		    =			self.rope_scaling.get("""type"""					,      __A							)
		__A :			Optional[Any]		    =			self.rope_scaling.get("""factor"""					,      __A							)
		if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
			raise ValueError(
			    F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""							)
		if rope_scaling_factor is None or not isinstance(__A					,      __A							) or rope_scaling_factor <= 1.0:
			raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""							)








import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()


