





'''simple docstring'''






from typing import TYPE_CHECKING

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


A__    :       Dict              ={
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Optional[Any]              =['''RoFormerTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Any              =[
          '''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:
      A__    :       Union[str, Any]              =[
          '''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:
      A__    :       List[str]              =[
          '''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

      A__    :       int              =_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


A__    :       str              =5_00_00
A__    :       Optional[int]              =50_00

A__       ,						A__    :       Optional[int]              =os.path.split(__file__)
A__    :       Tuple              =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for i in range(0							,     len(lowerCAmelCase  )							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							dataset[i : i + batch_size]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i]
@get_duration
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      with dataset.formatted_as(type=lowerCAmelCase  ):
            for i in range(0							,     lowerCAmelCase							,     lowerCAmelCase  ):
                  _lowerCAmelCase												=							dataset[i : i + batch_size]
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							{"""num examples""": SPEED_TEST_N_EXAMPLES}
      _lowerCAmelCase												=							[
          (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}),
      ]

      _lowerCAmelCase												=							[
          (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"""  )
            _lowerCAmelCase												=							datasets.Features(
                {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
            _lowerCAmelCase												=							generate_example_dataset(
                os.path.join(lowerCAmelCase							,     """dataset.arrow"""  )							,     lowerCAmelCase							,     num_examples=lowerCAmelCase							,     seq_shapes={"""list""": (1_00,)}							,     )
            print("""first set of iterations"""  )
            for func, kwargs in functions:
                  print(func.__name__							,     str(lowerCAmelCase  )  )
                  _lowerCAmelCase												=							func(lowerCAmelCase							,     **lowerCAmelCase  )

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

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


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






