
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a									=				{
    'agents': ['Agent', 'AzureOpenAiAgent', 'HfAgent', 'LocalAgent', 'OpenAiAgent'],
    'base': ['PipelineTool', 'RemoteTool', 'Tool', 'launch_gradio_demo', 'load_tool'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				['DocumentQuestionAnsweringTool']
    _a									=				['ImageCaptioningTool']
    _a									=				['ImageQuestionAnsweringTool']
    _a									=				['ImageSegmentationTool']
    _a									=				['SpeechToTextTool']
    _a									=				['TextClassificationTool']
    _a									=				['TextQuestionAnsweringTool']
    _a									=				['TextSummarizationTool']
    _a									=				['TextToSpeechTool']
    _a									=				['TranslationTool']

if TYPE_CHECKING:
    from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
    from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .document_question_answering import DocumentQuestionAnsweringTool
        from .image_captioning import ImageCaptioningTool
        from .image_question_answering import ImageQuestionAnsweringTool
        from .image_segmentation import ImageSegmentationTool
        from .speech_to_text import SpeechToTextTool
        from .text_classification import TextClassificationTool
        from .text_question_answering import TextQuestionAnsweringTool
        from .text_summarization import TextSummarizationTool
        from .text_to_speech import TextToSpeechTool
        from .translation import TranslationTool
else:
    import sys

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




"""simple docstring"""

import sys

from transformers import AutoTokenizer


_A				=					sys.argv[1]
_A				=					sys.argv[2]
_A				=					int(sys.argv[3])

_A				=					0

_A				=					AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
					for line in f_p:
										_A				=					line.rstrip()

										if not line:
															print(line)
															_A				=					0
															continue

										_A				=					line.split()[0]

										_A				=					len(tokenizer.tokenize(token))

										# Token contains strange control characters like \x96 or \x95
										# Just filter out the complete line
										if current_subwords_len == 0:
															continue

										if (subword_len_counter + current_subwords_len) > max_len:
															print('')
															print(line)
															_A				=					current_subwords_len
															continue

										subword_len_counter += current_subwords_len

										print(line)






