
'''simple docstring'''
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		def wrapper(*SCREAMING_SNAKE_CASE__ ,       **SCREAMING_SNAKE_CASE__			):
				snake_case  :       str     			=						timeit.default_timer()
				snake_case  :       Union[str, Any]     			=						func(*SCREAMING_SNAKE_CASE__ ,       **SCREAMING_SNAKE_CASE__			)
				snake_case  :       Tuple     			=						timeit.default_timer() - starttime
				return delta

		snake_case  :       str     			=						func.__name__

		return wrapper




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=100 ,       SCREAMING_SNAKE_CASE__=None			)  ->							Tuple:


		'''simple docstring'''


		snake_case  :       Dict     			=						[]
		snake_case  :       List[Any]     			=						seq_shapes or {}
		for i in range(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Dict     			=						{}
				for col_id, (k, v) in enumerate(features.items()			):
						if isinstance(SCREAMING_SNAKE_CASE__ ,       _ArrayXD			):
								snake_case  :       List[Any]     			=						np.random.rand(*v.shape			).astype(v.dtype			)
						elif isinstance(SCREAMING_SNAKE_CASE__ ,       datasets.Value			):
								if v.dtype == "string":
										snake_case  :       int     			=						'''The small grey turtle was surprisingly fast when challenged.'''
								else:
										snake_case  :       str     			=						np.random.randint(10 ,       size=1			).astype(v.dtype			).item()
						elif isinstance(SCREAMING_SNAKE_CASE__ ,       datasets.Sequence			):
								while isinstance(SCREAMING_SNAKE_CASE__ ,       datasets.Sequence			):
										snake_case  :       int     			=						v.feature
								snake_case  :       Tuple     			=						seq_shapes[k]
								snake_case  :       List[Any]     			=						np.random.rand(*SCREAMING_SNAKE_CASE__			).astype(v.dtype			)
						snake_case  :       List[str]     			=						data

				dummy_data.append((i, example)			)

		return dummy_data




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=100 ,       SCREAMING_SNAKE_CASE__=None			)  ->							Optional[int]:


		'''simple docstring'''


		snake_case  :       Any     			=						generate_examples(SCREAMING_SNAKE_CASE__ ,       num_examples=SCREAMING_SNAKE_CASE__ ,       seq_shapes=SCREAMING_SNAKE_CASE__			)

		with ArrowWriter(features=SCREAMING_SNAKE_CASE__ ,       path=SCREAMING_SNAKE_CASE__			) as writer:
				for key, record in dummy_data:
						snake_case  :       int     			=						features.encode_example(SCREAMING_SNAKE_CASE__			)
						writer.write(SCREAMING_SNAKE_CASE__			)

				snake_case			,snake_case  :       List[Any]     			=						writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'			)

		snake_case  :       int     			=						datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE__ ,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__			)			)

		return dataset




'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["EncoderDecoderModel"]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["TFEncoderDecoderModel"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
						from .configuration_encoder_decoder import EncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_encoder_decoder import EncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
						import sys

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


