


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase						=		{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

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








import timeit

import numpy as np

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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		def wrapper(*_a     ,       **_a	):
				lowerCAmelCase__       :      List[str]   					= timeit.default_timer()
				lowerCAmelCase__       :      List[Any]   					= func(*_a     ,       **_a	)
				lowerCAmelCase__       :      Any   					= timeit.default_timer() - starttime
				return delta

		lowerCAmelCase__       :      Any   					= func.__name__

		return wrapper






def 					lowerCamelCase_   (	_a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






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

				dummy_data.append((i, example)	)

		return dummy_data






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=100     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= generate_examples(_a     ,       num_examples=_a     ,       seq_shapes=_a	)

		with ArrowWriter(features=_a     ,       path=_a	) as writer:
				for key, record in dummy_data:
						lowerCAmelCase__       :      Optional[int]   					= features.encode_example(_a	)
						writer.write(_a	)

				lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= 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}.'	)

		lowerCAmelCase__       :      List[Any]   					= datasets.Dataset.from_file(filename=_a     ,       info=datasets.DatasetInfo(features=_a	)	)

		return dataset




