



'''simple docstring'''



import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def 					UpperCAmelCase   (						A  :				Optional[Any]				):
							def wrapper(*A  :				int					,		**A  :				Optional[Any]				):
														SCREAMING_SNAKE_CASE					:      Tuple     					=			timeit.default_timer()
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			func(*A					,		**A				)
														SCREAMING_SNAKE_CASE					:      int     					=			timeit.default_timer() - starttime
														return delta

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			func.__name__

							return wrapper
def 					UpperCAmelCase   (						A  :				dict					,		A  :				int=100					,		A  :				Union[str, Any]=None				):
							SCREAMING_SNAKE_CASE					:      Dict     					=			[]
							SCREAMING_SNAKE_CASE					:      Dict     					=			seq_shapes or {}
							for i in range(A				):
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{}
														for col_id, (k, v) in enumerate(features.items()				):
																					if isinstance(A					,		_ArrayXD				):
																												SCREAMING_SNAKE_CASE					:      Optional[int]     					=			np.random.rand(*v.shape				).astype(v.dtype				)
																					elif isinstance(A					,		datasets.Value				):
																												if v.dtype == "string":
																																			SCREAMING_SNAKE_CASE					:      Optional[int]     					=			'''The small grey turtle was surprisingly fast when challenged.'''
																												else:
																																			SCREAMING_SNAKE_CASE					:      Any     					=			np.random.randint(10					,		size=1				).astype(v.dtype				).item()
																					elif isinstance(A					,		datasets.Sequence				):
																												while isinstance(A					,		datasets.Sequence				):
																																			SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			v.feature
																												SCREAMING_SNAKE_CASE					:      Dict     					=			seq_shapes[k]
																												SCREAMING_SNAKE_CASE					:      Dict     					=			np.random.rand(*A				).astype(v.dtype				)
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			data

														dummy_data.append((i, example)				)

							return dummy_data






def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				Tuple					,		A  :				Optional[int]=100					,		A  :				str=None				):
							SCREAMING_SNAKE_CASE					:      Tuple     					=			generate_examples(A					,		num_examples=A					,		seq_shapes=A				)

							with ArrowWriter(features=A					,		path=A				) as writer:
														for key, record in dummy_data:
																					SCREAMING_SNAKE_CASE					:      Dict     					=			features.encode_example(A				)
																					writer.write(A				)

														SCREAMING_SNAKE_CASE					,SCREAMING_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}."""				)

							SCREAMING_SNAKE_CASE					:      Dict     					=			datasets.Dataset.from_file(filename=A					,		info=datasets.DatasetInfo(features=A				)				)

							return dataset









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_     :       str								=						{
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Optional[int]								=						[
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							lowerCAmelCase_     :       Optional[Any]								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




