






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a      	=  {
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CTRLForSequenceClassification''',
		    '''CTRLLMHeadModel''',
		    '''CTRLModel''',
		    '''CTRLPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCTRLForSequenceClassification''',
		    '''TFCTRLLMHeadModel''',
		    '''TFCTRLModel''',
		    '''TFCTRLPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
		from .tokenization_ctrl import CTRLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ctrl import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				    CTRLPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_ctrl import (
				    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCTRLForSequenceClassification,
				    TFCTRLLMHeadModel,
				    TFCTRLModel,
				    TFCTRLPreTrainedModel,
				)

else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:       List[str]      	=	5_0000
_lowerCamelCase	:       Optional[int]      	=	5000

_lowerCamelCase	,_lowerCamelCase	:       int      	=	os.path.split(__file__)
_lowerCamelCase	:       str      	=	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   for i in range(A__    ):
      UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   for i in range(0    ,						len(A__    )    ,						A__    ):
      UpperCamelCase					=  dataset[i : i + batch_size]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(A__    ):
         UpperCamelCase					=  dataset[i]







@get_duration
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   with dataset.formatted_as(type=A__    ):
      for i in range(0    ,						A__    ,						A__    ):
         UpperCamelCase					=  dataset[i : i + batch_size]







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase					=  [
       (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}),
   ]

   UpperCamelCase					=  [
       (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'    )
      UpperCamelCase					=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
      UpperCamelCase					=  generate_example_dataset(
          os.path.join(A__    ,						'dataset.arrow'    )    ,						A__    ,						num_examples=A__    ,						seq_shapes={'list': (100,)}    ,						)
      print('first set of iterations'    )
      for func, kwargs in functions:
         print(func.__name__    ,						str(A__    )    )
         UpperCamelCase					=  func(A__    ,						**A__    )

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

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


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

