




'''simple docstring'''


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]			):
  if model.config.model_type == "gpt2":
    return model.transformer.h[0].mlp.c_fc
  return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
  import torch
  import torch.nn as nn





  class    __lowercase    (  nn.Module ):







     def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    int:
       super().__init__()
       __a			=					module
       __a			=					nn.Sequential(
           nn.Linear(module.in_features      ,							__A      ,							bias=__A      )      ,							nn.Linear(__A      ,							module.out_features      ,							bias=__A      )      ,							)
       __a			=					(2.0 / (5 * min(module.in_features      ,							module.out_features      ))) ** 0.5
       nn.init.normal_(self.adapter[0].weight      ,							std=__A      )
       nn.init.zeros_(self.adapter[1].weight      )
       self.adapter.to(module.weight.device      )







     def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    List[Any]:
       return self.module(__A      ,							*__A      ,							**__A      ) + self.adapter(__A      )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    __lowercase    (  unittest.TestCase ):
   _a							=      """bigscience/bloom-1b7"""

   # Constant values
   _a							=      2.109_6595_5269_2574

   _a							=      """Hello my name is"""
   _a							=      set()
   EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I""" )
   EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n""" )
   EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University""" )
   _a							=      10







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     # Models and tokenizer
     __a			=					AutoTokenizer.from_pretrained(self.model_name      )





class    __lowercase    (  UpperCamelCase_ ):







   def 		UpperCamelCase__			(	self      )				->    Dict:
     super().setUp()

     # Models and tokenizer
     __a			=					AutoModelForCausalLM.from_pretrained(
         self.model_name      ,							torch_dtype=torch.floataa      ,							device_map='auto'      )
     __a			=					AutoModelForCausalLM.from_pretrained(self.model_name      ,							load_in_abit=__A      ,							device_map='auto'      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     del self.model_fpaa
     del self.model_abit

     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.model_abit.config

     self.assertTrue(hasattr(__A      ,							'quantization_config'      )      )

     __a			=					config.to_dict()
     __a			=					config.to_diff_dict()

     __a			=					config.to_json_string()







   def 		UpperCamelCase__			(	self      )				->    Any:
     from bitsandbytes.nn import Paramsabit

     __a			=					self.model_fpaa.get_memory_footprint()
     __a			=					self.model_abit.get_memory_footprint()

     self.assertAlmostEqual(mem_fpaa / mem_abit      ,							self.EXPECTED_RELATIVE_DIFFERENCE      )
     __a			=					get_some_linear_layer(self.model_abit      )
     self.assertTrue(linear.weight.__class__ == Paramsabit      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     from transformers import TaPreTrainedModel

     self.model_fpaa.get_memory_footprint()
     self.model_abit.get_memory_footprint()

     for name, module in self.model_abit.named_modules():
       if isinstance(__A      ,							torch.nn.Linear      ):
         if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
           # 4-bit parameters are packed in uint8 variables
           self.assertTrue(module.weight.dtype == torch.uinta      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      )
     __a			=					self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0      )      ,							max_new_tokens=10      )

     self.assertIn(self.tokenizer.decode(output_sequences[0]      ,							skip_special_tokens=__A      )      ,							self.EXPECTED_OUTPUTS      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					BitsAndBytesConfig()
     __a			=					True

     __a			=					AutoModelForCausalLM.from_pretrained(
         self.model_name      ,							quantization_config=__A      ,							device_map='auto'      )

     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      )
     __a			=					model_abit_from_config.generate(
         input_ids=encoded_input['input_ids'].to(0      )      ,							max_new_tokens=10      )

     self.assertIn(self.tokenizer.decode(output_sequences[0]      ,							skip_special_tokens=__A      )      ,							self.EXPECTED_OUTPUTS      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     with self.assertRaises(__A      ), tempfile.TemporaryDirectory() as tmpdirname:
       self.model_abit.save_pretrained(__A      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					BitsAndBytesConfig()

     with self.assertRaises(__A      ):
       __a			=					AutoModelForCausalLM.from_pretrained(
           self.model_name      ,							quantization_config=__A      ,							load_in_abit=__A      ,							device_map='auto'      ,							bnb_abit_quant_type='nf4'      ,							)







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     with self.assertRaises(__A      ):
       # Tries with `str`
       self.model_abit.to('cpu'      )

     with self.assertRaises(__A      ):
       # Tries with a `dtype``
       self.model_abit.to(torch.floataa      )

     with self.assertRaises(__A      ):
       # Tries with a `device`
       self.model_abit.to(torch.device('cuda:0'      )      )

     with self.assertRaises(__A      ):
       # Tries with a `device`
       self.model_abit.float()

     with self.assertRaises(__A      ):
       # Tries with a `device`
       self.model_abit.half()

     # Test if we did not break anything
     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      )

     __a			=					self.model_fpaa.to(torch.floataa      )
     __a			=					self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0      )      ,							max_new_tokens=10      )

     # Check this does not throw an error
     __a			=					self.model_fpaa.to('cpu'      )

     # Check this does not throw an error
     __a			=					self.model_fpaa.half()

     # Check this does not throw an error
     __a			=					self.model_fpaa.float()







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					AutoModelForSeqaSeqLM.from_pretrained('t5-small'      ,							load_in_abit=__A      ,							device_map='auto'      )
     self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa      )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    __lowercase    (  unittest.TestCase ):







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    List[Any]:
     __a			=					"t5-small"
     __a			=					"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
     __a			=					AutoTokenizer.from_pretrained(cls.model_name      )
     __a			=					"Translate in German: Hello, my dog is cute"







   def 		UpperCamelCase__			(	self      )				->    Any:
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     from transformers import TaForConditionalGeneration

     __a			=					TaForConditionalGeneration._keep_in_fpaa_modules
     __a			=					None

     # test with `t5-small`
     __a			=					TaForConditionalGeneration.from_pretrained(self.model_name      ,							load_in_abit=__A      ,							device_map='auto'      )
     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      ).to(0      )
     __a			=					model.generate(**__A      )

     # test with `flan-t5-small`
     __a			=					TaForConditionalGeneration.from_pretrained(
         self.dense_act_model_name      ,							load_in_abit=__A      ,							device_map='auto'      )
     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      ).to(0      )
     __a			=					model.generate(**__A      )
     __a			=					modules







   def 		UpperCamelCase__			(	self      )				->    Dict:
     import bitsandbytes as bnb

     from transformers import TaForConditionalGeneration

     # test with `t5-small`
     __a			=					TaForConditionalGeneration.from_pretrained(self.model_name      ,							load_in_abit=__A      ,							device_map='auto'      )

     # there was a bug with decoders - this test checks that it is fixed
     self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q      ,							bnb.nn.Linearabit      )      )

     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      ).to(0      )
     __a			=					model.generate(**__A      )

     # test with `flan-t5-small`
     __a			=					TaForConditionalGeneration.from_pretrained(
         self.dense_act_model_name      ,							load_in_abit=__A      ,							device_map='auto'      )
     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      ).to(0      )
     __a			=					model.generate(**__A      )





class    __lowercase    (  UpperCamelCase_ ):







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     super().setUp()
     # model_name
     __a			=					"bigscience/bloom-560m"
     __a			=					"t5-small"

     # Different types of model

     __a			=					AutoModel.from_pretrained(self.model_name      ,							load_in_abit=__A      ,							device_map='auto'      )
     # Sequence classification model
     __a			=					AutoModelForSequenceClassification.from_pretrained(
         self.model_name      ,							load_in_abit=__A      ,							device_map='auto'      )
     # CausalLM model
     __a			=					AutoModelForCausalLM.from_pretrained(self.model_name      ,							load_in_abit=__A      ,							device_map='auto'      )
     # Seq2seq model
     __a			=					AutoModelForSeqaSeqLM.from_pretrained(
         self.seq_to_seq_name      ,							load_in_abit=__A      ,							device_map='auto'      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     del self.base_model
     del self.sequence_model
     del self.model_abit
     del self.seq_to_seq_model

     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     from bitsandbytes.nn import Paramsabit

     self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit      )

     # Other heads should be nn.Parameter
     self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter      )
     self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter      )
     self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter      )





class    __lowercase    (  UpperCamelCase_ ):







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     super().setUp()







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     del self.pipe

     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					pipeline(
         'text-generation'      ,							model=self.model_name      ,							model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa}      ,							max_new_tokens=self.MAX_NEW_TOKENS      ,							)

     # Real second forward pass
     __a			=					self.pipe(self.input_text      )
     self.assertIn(pipeline_output[0]['generated_text']      ,							self.EXPECTED_OUTPUTS      )





@require_torch_multi_gpu
class    __lowercase    (  UpperCamelCase_ ):







   def 		UpperCamelCase__			(	self      )				->    str:
     super().setUp()







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					AutoModelForCausalLM.from_pretrained(
         self.model_name      ,							load_in_abit=__A      ,							device_map='balanced'      )

     # Check correct device map
     self.assertEqual(set(model_parallel.hf_device_map.values()      )      ,							{0, 1}      )

     # Check that inference pass works on the model
     __a			=					self.tokenizer(self.input_text      ,							return_tensors='pt'      )

     # Second real batch
     __a			=					model_parallel.generate(input_ids=encoded_input['input_ids'].to(0      )      ,							max_new_tokens=10      )
     self.assertIn(self.tokenizer.decode(output_parallel[0]      ,							skip_special_tokens=__A      )      ,							self.EXPECTED_OUTPUTS      )





class    __lowercase    (  UpperCamelCase_ ):







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					"facebook/opt-350m"
     super().setUp()







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     if version.parse(importlib.metadata.version('bitsandbytes'      )      ) < version.parse('0.37.0'      ):
       return

     # Step 1: freeze all parameters
     __a			=					AutoModelForCausalLM.from_pretrained(self.model_name      ,							load_in_abit=__A      )

     self.assertEqual(set(model.hf_device_map.values()      )      ,							{torch.cuda.current_device()}      )

     for param in model.parameters():
       __a			=					False  # freeze the model - train adapters later
       if param.ndim == 1:
         # cast the small parameters (e.g. layernorm) to fp32 for stability
         __a			=					param.data.to(torch.floataa      )

        # Step 2: add adapters
     for _, module in model.named_modules():
       if "OPTAttention" in repr(type(__A      )      ):
         __a			=					LoRALayer(module.q_proj      ,							rank=16      )
         __a			=					LoRALayer(module.k_proj      ,							rank=16      )
         __a			=					LoRALayer(module.v_proj      ,							rank=16      )

        # Step 3: dummy batch
     __a			=					self.tokenizer('Test batch '      ,							return_tensors='pt'      ).to(0      )

     # Step 4: Check if the gradient is not None
     with torch.cuda.amp.autocast():
       __a			=					model.forward(**__A      )
       out.logits.norm().backward()

     for module in model.modules():
       if isinstance(__A      ,							__A      ):
         self.assertTrue(module.adapter[1].weight.grad is not None      )
         self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0      )
       elif isinstance(__A      ,							nn.Embedding      ):
         self.assertTrue(module.weight.grad is None      )





class    __lowercase    (  UpperCamelCase_ ):
   _a							=      """gpt2-xl"""
   _a							=      3.3191_8548_5415_2187









'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  if (
      (cp >= 0X4e00 and cp <= 0X9fff)
      or (cp >= 0X3400 and cp <= 0X4dbf)  #
      or (cp >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # word like '180' or '身高' or '神'
  for char in word:
    __a			=					ord(a_			)
    if not _is_chinese_char(a_			):
      return 0
  return 1


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __a			=					set()

  for token in tokens:
    __a			=					len(a_			) > 1 and is_chinese(a_			)
    if chinese_word:
      word_set.add(a_			)
  __a			=					list(a_			)
  return word_list


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  if not chinese_word_set:
    return bert_tokens
  __a			=					max([len(a_			) for w in chinese_word_set]			)

  __a			=					bert_tokens
  __a	,       __a			=					0, len(a_			)
  while start < end:
    __a			=					True
    if is_chinese(bert_word[start]			):
      __a			=					min(end - start				,					a_			)
      for i in range(a_				,					1				,					-1			):
        __a			=					''.join(bert_word[start : start + i]			)
        if whole_word in chinese_word_set:
          for j in range(start + 1				,					start + i			):
            __a			=					'##' + bert_word[j]
          __a			=					start + i
          __a			=					False
          break
    if single_word:
      start += 1
  return bert_word


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __a			=					[get_chinese_word(a_			) for r in res]
    ltp_res.extend(a_			)
  assert len(a_			) == len(a_			)

  __a			=					[]
  for i in range(0				,					len(a_			)				,					100			):
    __a			=					bert_tokenizer(lines[i : i + 100]				,					add_special_tokens=a_				,					truncation=a_				,					max_length=512			)
    bert_res.extend(res['input_ids']			)
  assert len(a_			) == len(a_			)

  __a			=					[]
  for input_ids, chinese_word in zip(a_				,					a_			):
    __a			=					[]
    for id in input_ids:
      __a			=					bert_tokenizer._convert_id_to_token(a_			)
      input_tokens.append(a_			)
    __a			=					add_sub_symbol(a_				,					a_			)
    __a			=					[]
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(a_			):
      if token[:2] == "##":
        __a			=					token[2:]
        # save chinese tokens' pos
        if len(a_			) == 1 and _is_chinese_char(ord(a_			)			):
          ref_id.append(a_			)
    ref_ids.append(a_			)

  assert len(a_			) == len(a_			)

  return ref_ids


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name				,					'r'				,					encoding='utf-8'			) as f:
    __a			=					f.readlines()
  __a			=					[line.strip() for line in data if len(a_			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  __a			=					LTP(args.ltp			)  # faster in GPU device
  __a			=					BertTokenizer.from_pretrained(args.bert			)

  __a			=					prepare_ref(a_				,					a_				,					a_			)

  with open(args.save_path				,					'w'				,					encoding='utf-8'			) as f:
    __a			=					[json.dumps(a_			) + '\n' for ref in ref_ids]
    f.writelines(a_			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser(description="prepare_chinese_ref")
  parser.add_argument(
      "--file_name",
      type=str,
      default="./resources/chinese-demo.txt",
      help="file need process, same as training data in lm",
  )
  parser.add_argument(
      "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
  )
  parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
  parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)



