import sys

from transformers import AutoTokenizer


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

UpperCAmelCase      		=					0

UpperCAmelCase      		=					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:
  UpperCAmelCase      		=					line.rstrip()

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

  UpperCAmelCase      		=					line.split()[0]

  UpperCAmelCase      		=					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)
   UpperCAmelCase      		=					current_subwords_len
   continue

  subword_len_counter += current_subwords_len

  print(line)








"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					str 		=		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					List[Any] 		=		["OpenAIGPTTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Union[str, Any] 		=		[
		    "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OpenAIGPTDoubleHeadsModel",
		    "OpenAIGPTForSequenceClassification",
		    "OpenAIGPTLMHeadModel",
		    "OpenAIGPTModel",
		    "OpenAIGPTPreTrainedModel",
		    "load_tf_weights_in_openai_gpt",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Optional[Any] 		=		[
		    "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFOpenAIGPTDoubleHeadsModel",
		    "TFOpenAIGPTForSequenceClassification",
		    "TFOpenAIGPTLMHeadModel",
		    "TFOpenAIGPTMainLayer",
		    "TFOpenAIGPTModel",
		    "TFOpenAIGPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					Dict 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




