



'''simple docstring'''



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowercase       :						Tuple						=logging.getLogger(__name__)


def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Union[str, Any]:
				np.random.seed(args.seed			)
				torch.manual_seed(args.seed			)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed			)


def 							__UpperCAmelCase				(				)		->						Union[str, Any]:
				snake_case__      :   Any      =    argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_name_or_path'''   ,				default=UpperCamelCase__   ,				type=UpperCamelCase__   ,				required=UpperCamelCase__   ,				)
				parser.add_argument('''--prompt'''   ,				type=UpperCamelCase__   ,				default=''''''			)
				parser.add_argument('''--length'''   ,				type=UpperCamelCase__   ,				default=20			)
				parser.add_argument('''--stop_token'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				help='''Token at which text generation is stopped'''			)
				parser.add_argument(
				    '''--temperature'''   ,				type=UpperCamelCase__   ,				default=1.0   ,				help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''   ,				)
				parser.add_argument(
				    '''--repetition_penalty'''   ,				type=UpperCamelCase__   ,				default=1.0   ,				help='''primarily useful for CTRL model; in that case, use 1.2'''			)
				parser.add_argument('''--k'''   ,				type=UpperCamelCase__   ,				default=0			)
				parser.add_argument('''--penalty_alpha'''   ,				type=UpperCamelCase__   ,				default=0.0			)
				parser.add_argument('''--p'''   ,				type=UpperCamelCase__   ,				default=0.9			)

				parser.add_argument('''--prefix'''   ,				type=UpperCamelCase__   ,				default=''''''   ,				help='''Text added prior to input.'''			)
				parser.add_argument('''--padding_text'''   ,				type=UpperCamelCase__   ,				default=''''''   ,				help='''Deprecated, the use of `--prefix` is preferred.'''			)
				parser.add_argument('''--xlm_language'''   ,				type=UpperCamelCase__   ,				default=''''''   ,				help='''Optional language when used with the XLM model.'''			)

				parser.add_argument('''--seed'''   ,				type=UpperCamelCase__   ,				default=42   ,				help='''random seed for initialization'''			)
				parser.add_argument('''--no_cuda'''   ,				action='''store_true'''   ,				help='''Avoid using CUDA when available'''			)
				parser.add_argument(
				    '''--fp16'''   ,				action='''store_true'''   ,				help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''   ,				)
				snake_case__      :   Any      =    parser.parse_args()

				snake_case__      :   List[str]      =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
				snake_case__      :   Any      =    0 if args.no_cuda else torch.cuda.device_count()

				logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''			)

				set_seed(UpperCamelCase__			)

				# Initialize the model and tokenizer
				snake_case__      :   Dict      =    AutoTokenizer.from_pretrained(args.model_name_or_path			)
				snake_case__      :   int      =    AutoModelForCausalLM.from_pretrained(args.model_name_or_path			)

				# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
				# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
				model.to(args.device			)

				if args.fpaa:
								model.half()

				logger.info(UpperCamelCase__			)
				snake_case__      :   str      =    args.prompt if args.prompt else input('''Model prompt >>> '''			)

				snake_case__      :   Union[str, Any]      =    tokenizer(UpperCamelCase__   ,				return_tensors='''pt'''   ,				add_special_tokens=UpperCamelCase__			)
				snake_case__      :   List[Any]      =    {key: value.to(args.device			) for key, value in inputs.items()}

				snake_case__      :   List[str]      =    model.generate(
				    **UpperCamelCase__   ,				max_length=args.length + len(inputs['''input_ids'''][0]			)   ,				penalty_alpha=args.penalty_alpha   ,				top_k=args.k   ,				)

				snake_case__      :   str      =    []
				for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__			):
								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''			)
								snake_case__      :   Optional[Any]      =    generated_sequence.tolist()

								# Decode text
								snake_case__      :   Optional[int]      =    tokenizer.decode(UpperCamelCase__   ,				clean_up_tokenization_spaces=UpperCamelCase__   ,				add_special_tokens=UpperCamelCase__			)

								# Remove all text after the stop token
								snake_case__      :   Tuple      =    text[: text.find(args.stop_token			) if args.stop_token else None]

								# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
								snake_case__      :   int      =    (
								    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]   ,				clean_up_tokenization_spaces=UpperCamelCase__			)			) :]
								)

								generated_sequences.append(UpperCamelCase__			)
								print(UpperCamelCase__			)

				return generated_sequences


if __name__ == "__main__":
				main()






"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		Optional[int]     					=     GPTNeoXJapaneseTokenizer
   a_					:		Dict     					=     False
   a_					:		Union[str, Any]     					=     {"""do_clean_text""": False, """add_prefix_space""": False}







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      super().setUp()

      a_						   =     [
          "こん",
          "こんに",
          "にちは",
          "ばんは",
          "世界,㔺界",
          "、",
          "。",
          "<BR>",
          "<SP>",
          "<TAB>",
          "<URL>",
          "<EMAIL>",
          "<TEL>",
          "<DATE>",
          "<PRICE>",
          "<BLOCK>",
          "<KIGOU>",
          "<U2000U2BFF>",
          "<|emoji1|>",
          "<unk>",
          "<|startoftext|>",
          "<|endoftext|>",
      ]
      a_						   =     {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
      a_						   =     {"unk_token": "<unk>"}

      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"])
      a_						   =     os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["emoji_file"])
      with open(self.vocab_file       ,							"w"       ,							encoding="utf-8") as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
      with open(self.emoji_file       ,							"w") as emoji_writer:
         emoji_writer.write(json.dumps(__UpperCAmelCase))







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->int:
      kwargs.update(self.special_tokens_map)
      return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Dict:
      a_						   =     "こんにちは、世界。 \nこんばんは、㔺界。😀"
      a_						   =     "こんにちは、世界。 \nこんばんは、世界。😀"
      return input_text, output_text







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_   ,			a_						   =     self.get_input_output_texts(__UpperCAmelCase)
      a_						   =     tokenizer.encode(__UpperCAmelCase       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.decode(__UpperCAmelCase       ,							clean_up_tokenization_spaces=__UpperCAmelCase)
      return text, ids







   def    UpperCAmelCase__	( self)   ->Any:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->int:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      pass  # TODO add if relevant







   def    UpperCAmelCase__	( self)   ->List[str]:
      a_						   =     self.get_tokenizer()

      # Testing tokenization
      a_						   =     "こんにちは、世界。　こんばんは、㔺界。"
      a_						   =     ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
      a_						   =     tokenizer.tokenize(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids without special tokens
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)

      # Testing conversion to ids with special tokens
      a_						   =     tokens + [tokenizer.unk_token]
      a_						   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
      a_						   =     tokenizer.convert_tokens_to_ids(__UpperCAmelCase)
      self.assertListEqual(__UpperCAmelCase       ,							__UpperCAmelCase)







   @slow
   def    UpperCAmelCase__	( self)   ->Tuple:
      a_						   =     self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b")

      a_						   =     tokenizer.encode("ありがとう。"       ,							add_special_tokens=__UpperCAmelCase)
      a_						   =     tokenizer.encode("どういたしまして。"       ,							add_special_tokens=__UpperCAmelCase)

      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase)
      a_						   =     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,							__UpperCAmelCase)

      assert encoded_sentence == ids_a
      assert encoded_pair == ids_a + ids_a







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Intentionally convert some words to accommodate character fluctuations unique to Japanese
      pass







   def    UpperCAmelCase__	( self)   ->Union[str, Any]:
      # tokenizer has no padding token
      pass