



import sys

from transformers import AutoTokenizer


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

UpperCAmelCase				:							Optional[int]								=0

UpperCAmelCase				:							str								=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				:							Optional[Any]								=line.rstrip()

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

		UpperCAmelCase				:							Any								=line.split()[0]

		UpperCAmelCase				:							str								=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				:							int								=current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
	import torch

UpperCAmelCase				:							Any								=logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
	from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



	def 				_lowerCAmelCase      (cls):
						return cls



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase = None				,		_lowerCAmelCase = None				,		_lowerCAmelCase = None				,		_lowerCAmelCase = None				,		):
					UpperCamelCase_											=				device
					UpperCamelCase_											=				shape[0]

					UpperCamelCase_											=				layout or torch.strided
					UpperCamelCase_											=				device or torch.device("cpu")

					if generator is not None:
										UpperCamelCase_											=				generator.device.type if not isinstance(_lowerCAmelCase				,		_lowerCAmelCase) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															UpperCamelCase_											=				"cpu"
															if device != "mps":
																				logger.info(
																				    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																				    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																				    f""" slighly speed up this function by passing a generator that was created on the {device} device.""")
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}.""")

					if isinstance(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				(1,) + shape[1:]
										UpperCamelCase_											=				[
										    torch.randn(_lowerCAmelCase				,		generator=generator[i]				,		device=_lowerCAmelCase				,		dtype=_lowerCAmelCase				,		layout=_lowerCAmelCase)
										    for i in range(_lowerCAmelCase)
										]
										UpperCamelCase_											=				torch.cat(_lowerCAmelCase				,		dim=0).to(_lowerCAmelCase)
					else:
										UpperCamelCase_											=				torch.randn(_lowerCAmelCase				,		generator=_lowerCAmelCase				,		device=_lowerCAmelCase				,		dtype=_lowerCAmelCase				,		layout=_lowerCAmelCase).to(_lowerCAmelCase)

					return latents






def 				_lowerCAmelCase      (_lowerCAmelCase):
					if is_torch_version("<"				,		"2.0.0") or not hasattr(_lowerCAmelCase				,		"_dynamo"):
										return False
					return isinstance(_lowerCAmelCase				,		torch._dynamo.eval_frame.OptimizedModule)
