




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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}


class 	A_ (	__snake_case   ):




      '''simple docstring'''


      __snake_case 	=       """longformer"""




      def __init__(    self:	Union[str, Any]						,				a:	Union[List[int], int] = 512						,				a:	int = 2						,				a:	int = 1						,				a:	int = 0						,				a:	int = 2						,				a:	int = 3_0522						,				a:	int = 768						,				a:	int = 12						,				a:	int = 12						,				a:	int = 3072						,				a:	str = "gelu"						,				a:	float = 0.1						,				a:	float = 0.1						,				a:	int = 512						,				a:	int = 2						,				a:	float = 0.0_2						,				a:	float = 1e-12						,				a:	bool = False						,				**a:	List[Any]						,				):
       super().__init__(pad_token_id=UpperCamelCase__						,				**UpperCamelCase__    )

       __lowerCamelCase		:							List[Any]		 =						attention_window
       __lowerCamelCase		:							Optional[int]		 =						sep_token_id
       __lowerCamelCase		:							Dict		 =						bos_token_id
       __lowerCamelCase		:							str		 =						eos_token_id
       __lowerCamelCase		:							Tuple		 =						vocab_size
       __lowerCamelCase		:							Optional[Any]		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							Any		 =						intermediate_size
       __lowerCamelCase		:							List[str]		 =						hidden_dropout_prob
       __lowerCamelCase		:							str		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							Optional[int]		 =						max_position_embeddings
       __lowerCamelCase		:							Optional[Any]		 =						type_vocab_size
       __lowerCamelCase		:							List[Any]		 =						initializer_range
       __lowerCamelCase		:							List[str]		 =						layer_norm_eps
       __lowerCamelCase		:							Optional[Any]		 =						onnx_export



class 	A_ (	__snake_case   ):




      '''simple docstring'''


      def __init__(    self:	int						,				a:	"PretrainedConfig"						,				a:	str = "default"						,				a:	"List[PatchingSpec]" = None    ):
       super().__init__(UpperCamelCase__						,				UpperCamelCase__						,				UpperCamelCase__    )
       __lowerCamelCase		:							Any		 =						True




      @property
      def 					_snake_case     (    self:	Optional[int]    ):


       if self.task == "multiple-choice":
        __lowerCamelCase		:							Union[str, Any]		 =						{0: "batch", 1: "choice", 2: "sequence"}
       else:
        __lowerCamelCase		:							int		 =						{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('global_attention_mask', dynamic_axis),
           ]    )




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						super().outputs
       if self.task == "default":
        __lowerCamelCase		:							int		 =						{0: "batch"}
       return outputs




      @property
      def 					_snake_case     (    self:	Any    ):
       return 1e-4




      @property
      def 					_snake_case     (    self:	str    ):
       return max(super().default_onnx_opset						,				14    )




      def 					_snake_case     (    self:	Dict						,				a:	"PreTrainedTokenizerBase"						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional[TensorType] = None						,				):
       __lowerCamelCase		:							Optional[Any]		 =						super().generate_dummy_inputs(
           preprocessor=UpperCamelCase__						,				batch_size=UpperCamelCase__						,				seq_length=UpperCamelCase__						,				is_pair=UpperCamelCase__						,				framework=UpperCamelCase__    )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       __lowerCamelCase		:							Tuple		 =						torch.zeros_like(inputs['input_ids']    )
       # make every second token global
       __lowerCamelCase		:							Optional[int]		 =						1

       return inputs





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0
lowercase_	   =     5_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Tuple		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(0      ,	len(SCREAMING_SNAKE_CASE__						)      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Optional[Any]		 =						dataset[i : i + batch_size]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							str		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(0      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							int		 =						dataset[i : i + batch_size]
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Union[str, Any]		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCamelCase		:							Optional[Any]		 =						[
     (read, {'length': SMALL_TEST}),
     (read, {'length': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
 ]

 __lowerCamelCase		:							Any		 =						[
     (read, {'length': SMALL_TEST}),
     (read, {'length': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('generating dataset'						)
  __lowerCamelCase		:							Optional[int]		 =						datasets.Features(
      {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							str		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__      ,	seq_shapes={'list': (100,)}      ,	)
  print('first set of iterations'						)
  for func, kwargs in functions:
   print(func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							Optional[int]		 =						func(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

  print('shuffling dataset'						)
  __lowerCamelCase		:							str		 =						dataset.shuffle()
  print('Second set of iterations (after shuffling'						)
  for func, kwargs in functions_shuffled:
   print('shuffled '      ,	func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							int		 =						func(
       SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

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


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

