

"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase			=					logging.get_logger(__name__)

__UpperCAmelCase			=					{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class   __lowercase      (     lowercase_       ):
  snake_case_												=							"gptj"
  snake_case_												=							{
      "max_position_embeddings": "n_positions",
      "hidden_size": "n_embd",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }

  def __init__(	self		:      int       ,A		:      Optional[int]=50_400       ,A		:      str=2_048       ,A		:      Optional[Any]=4_096       ,A		:      List[str]=28       ,A		:      Optional[Any]=16       ,A		:      Optional[Any]=64       ,A		:      List[str]=None       ,A		:      Dict="gelu_new"       ,A		:      Dict=0.0       ,A		:      Any=0.0       ,A		:      str=0.0       ,A		:      str=1e-5       ,A		:      Dict=0.0_2       ,A		:      Any=True       ,A		:      Any=50_256       ,A		:      List[str]=50_256       ,A		:      List[Any]=False       ,**A		:      Optional[int]       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       vocab_size
   UpperCAmelCase__       :	int        =       n_positions
   UpperCAmelCase__       :	Optional[int]        =       n_embd
   UpperCAmelCase__       :	Dict        =       n_layer
   UpperCAmelCase__       :	Any        =       n_head
   UpperCAmelCase__       :	Optional[int]        =       n_inner
   UpperCAmelCase__       :	Optional[int]        =       rotary_dim
   UpperCAmelCase__       :	Any        =       activation_function
   UpperCAmelCase__       :	int        =       resid_pdrop
   UpperCAmelCase__       :	str        =       embd_pdrop
   UpperCAmelCase__       :	int        =       attn_pdrop
   UpperCAmelCase__       :	List[str]        =       layer_norm_epsilon
   UpperCAmelCase__       :	Any        =       initializer_range
   UpperCAmelCase__       :	List[str]        =       use_cache

   UpperCAmelCase__       :	str        =       bos_token_id
   UpperCAmelCase__       :	str        =       eos_token_id

   super().__init__(
       bos_token_id=a__       ,eos_token_id=a__       ,tie_word_embeddings=a__       ,**a__     )







class   __lowercase      (     lowercase_       ):

  def __init__(	self		:      Any       ,A		:      List[str]       ,A		:      List[str] = "default"       ,A		:      Optional[int] = None       ,A		:      Dict = False       ,):






   '''simple docstring'''
   super().__init__(a__       ,task=a__       ,patching_specs=a__       ,use_past=a__     )
   if not getattr(self._config       ,"""pad_token_id"""       ,a__     ):
    # TODO: how to do that better?
    UpperCAmelCase__       :	int        =       0

  @property
  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
   if self.use_past:
    self.fill_with_past_key_values_(a__       ,direction="""inputs"""     )
    UpperCAmelCase__       :	Any        =       {0: """batch""", 1: """past_sequence + sequence"""}
   else:
    UpperCAmelCase__       :	Dict        =       {0: """batch""", 1: """sequence"""}

   return common_inputs

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   return self._config.n_layer

  @property
  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   return self._config.n_head

  def  __lowercase							(	self		:      str       ,A		:      Optional[Any]       ,A		:      List[str] = -1       ,A		:      str = -1       ,A		:      Dict = False       ,A		:      Optional[Any] = None       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       super(a__       ,self     ).generate_dummy_inputs(
       a__       ,batch_size=a__       ,seq_length=a__       ,is_pair=a__       ,framework=a__     )

   # We need to order the input in the way they appears in the forward()
   UpperCAmelCase__       :	List[Any]        =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}     )

   # Need to add the past_keys
   if self.use_past:
    if not is_torch_available():
     raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
    else:
     import torch

     UpperCAmelCase__       :	Any        =       common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     UpperCAmelCase__       :	Any        =       seqlen + 2
     UpperCAmelCase__       :	Dict        =       (
         batch,
         self.num_attention_heads,
         past_key_values_length,
         self._config.hidden_size // self.num_attention_heads,
     )
     UpperCAmelCase__       :	str        =       [
         (torch.zeros(a__     ), torch.zeros(a__     )) for _ in range(self.num_layers     )
     ]

   UpperCAmelCase__       :	List[Any]        =       common_inputs["""attention_mask"""]
   if self.use_past:
    UpperCAmelCase__       :	Tuple        =       ordered_inputs["""attention_mask"""].dtype
    UpperCAmelCase__       :	Tuple        =       torch.cat(
        [ordered_inputs["""attention_mask"""], torch.ones(a__       ,a__       ,dtype=a__     )]       ,dim=1     )

   return ordered_inputs




  @property
  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   return 13













import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # 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(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


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


