




'''simple docstring'''



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_    	=				5_0_0_0_0
UpperCamelCase_    	=				5_0_0_0

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




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      for i in range(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      str    					=						dataset[i]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      for i in range(0      ,len(__UpperCamelCase    )      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						dataset[i : i + batch_size]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      with dataset.formatted_as(type=__UpperCamelCase    ):
            for i in range(__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						dataset[i]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      with dataset.formatted_as(type=__UpperCamelCase    ):
            for i in range(0      ,__UpperCamelCase      ,__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      str    					=						dataset[i : i + batch_size]




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						{'num examples': SPEED_TEST_N_EXAMPLES}
      SCREAMING_SNAKE_CASE   :      Union[str, 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': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (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': 10_00}),
      ]

      SCREAMING_SNAKE_CASE   :      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': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (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': 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print('generating dataset'    )
            SCREAMING_SNAKE_CASE   :      Any    					=						datasets.Features(
                {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
            SCREAMING_SNAKE_CASE   :      int    					=						generate_example_dataset(
                os.path.join(__UpperCamelCase      ,'dataset.arrow'    )      ,__UpperCamelCase      ,num_examples=__UpperCamelCase      ,seq_shapes={'list': (1_00,)}      ,)
            print('first set of iterations'    )
            for func, kwargs in functions:
                  print(func.__name__      ,str(__UpperCamelCase    )    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						func(__UpperCamelCase      ,**__UpperCamelCase    )

            print('shuffling dataset'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						dataset.shuffle()
            print('Second set of iterations (after shuffling'    )
            for func, kwargs in functions_shuffled:
                  print('shuffled '      ,func.__name__      ,str(__UpperCamelCase    )    )
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						func(
                      __UpperCamelCase      ,**__UpperCamelCase    )

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


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








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    	=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				["RoFormerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase_    	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


