



'''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        a_     (							_lowerCAmelCase       )      ->      Tuple:
  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 					lowerCamelCase_    (				nn.Module     ):
     """simple docstring"""




     def __init__( self      :	Any			,	_a      :	nn.Module			,	_a      :	int   )					->					Tuple:
       super().__init__()
       __lowerCamelCase       :       Optional[Any]           = module
       __lowerCamelCase       :       Union[str, Any]           = nn.Sequential(
           nn.Linear(module.in_features			,	_a			,	bias=_a   )			,	nn.Linear(_a			,	module.out_features			,	bias=_a   )			,	)
       __lowerCamelCase       :       Union[str, Any]           = (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 						_lowercase     ( self      :	Any			,	_a      :	Tuple			,	*_a      :	Dict			,	**_a      :	Tuple   )					->					int:
       return self.module(_a			,	*_a			,	**_a   ) + self.adapter(_a   )






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  a_												="""bigscience/bloom-1b7"""

  # Constant values
  a_												=2.109659552692574

  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 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    # Models and tokenizer
    __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(self.model_name   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Any   )					->					Optional[int]:
    super().setUp()

    # Models and tokenizer
    __lowerCamelCase       :       str           = AutoModelForCausalLM.from_pretrained(
        self.model_name			,	torch_dtype=torch.floataa			,	device_map='auto'   )
    __lowerCamelCase       :       Dict           = AutoModelForCausalLM.from_pretrained(self.model_name			,	load_in_abit=_a			,	device_map='auto'   )




  def 						_lowercase     ( self      :	List[str]   )					->					List[Any]:
    del self.model_fpaa
    del self.model_abit

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




  def 						_lowercase     ( self      :	List[str]   )					->					Optional[Any]:
    __lowerCamelCase       :       Optional[int]           = self.model_abit.config

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

    __lowerCamelCase       :       Any           = config.to_dict()
    __lowerCamelCase       :       Any           = config.to_diff_dict()

    __lowerCamelCase       :       Optional[Any]           = config.to_json_string()




  def 						_lowercase     ( self      :	str   )					->					Optional[int]:
    from bitsandbytes.nn import Paramsabit

    __lowerCamelCase       :       Optional[int]           = self.model_fpaa.get_memory_footprint()
    __lowerCamelCase       :       str           = self.model_abit.get_memory_footprint()

    self.assertAlmostEqual(mem_fpaa / mem_abit			,	self.EXPECTED_RELATIVE_DIFFERENCE   )
    __lowerCamelCase       :       Union[str, Any]           = get_some_linear_layer(self.model_abit   )
    self.assertTrue(linear.weight.__class__ == Paramsabit   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[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 						_lowercase     ( self      :	List[str]   )					->					Dict:
    __lowerCamelCase       :       Optional[Any]           = self.tokenizer(self.input_text			,	return_tensors='pt'   )
    __lowerCamelCase       :       Dict           = 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 						_lowercase     ( self      :	str   )					->					Any:
    __lowerCamelCase       :       Dict           = BitsAndBytesConfig()
    __lowerCamelCase       :       Optional[int]           = True

    __lowerCamelCase       :       Any           = AutoModelForCausalLM.from_pretrained(
        self.model_name			,	quantization_config=_a			,	device_map='auto'   )

    __lowerCamelCase       :       Tuple           = self.tokenizer(self.input_text			,	return_tensors='pt'   )
    __lowerCamelCase       :       List[str]           = 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 						_lowercase     ( self      :	List[Any]   )					->					List[Any]:
    with self.assertRaises(_a   ), tempfile.TemporaryDirectory() as tmpdirname:
      self.model_abit.save_pretrained(_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					List[str]:
    __lowerCamelCase       :       Union[str, Any]           = BitsAndBytesConfig()

    with self.assertRaises(_a   ):
      __lowerCamelCase       :       Tuple           = AutoModelForCausalLM.from_pretrained(
          self.model_name			,	quantization_config=_a			,	load_in_abit=_a			,	device_map='auto'			,	bnb_abit_quant_type='nf4'			,	)




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Dict:
    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
    __lowerCamelCase       :       Optional[int]           = self.tokenizer(self.input_text			,	return_tensors='pt'   )

    __lowerCamelCase       :       Tuple           = self.model_fpaa.to(torch.floataa   )
    __lowerCamelCase       :       List[Any]           = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0   )			,	max_new_tokens=10   )

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

    # Check this does not throw an error
    __lowerCamelCase       :       int           = self.model_fpaa.half()

    # Check this does not throw an error
    __lowerCamelCase       :       Any           = self.model_fpaa.float()




  def 						_lowercase     ( self      :	List[str]   )					->					Optional[Any]:
    __lowerCamelCase       :       Tuple           = 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 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  @classmethod
  def 						_lowercase     ( cls      :	Dict   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[int]           = 't5-small'
    __lowerCamelCase       :       Tuple           = 'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
    __lowerCamelCase       :       int           = AutoTokenizer.from_pretrained(cls.model_name   )
    __lowerCamelCase       :       List[Any]           = 'Translate in German: Hello, my dog is cute'




  def 						_lowercase     ( self      :	str   )					->					Union[str, Any]:
    gc.collect()
    torch.cuda.empty_cache()




  def 						_lowercase     ( self      :	List[str]   )					->					Any:
    from transformers import TaForConditionalGeneration

    __lowerCamelCase       :       Optional[int]           = TaForConditionalGeneration._keep_in_fpaa_modules
    __lowerCamelCase       :       Dict           = None

    # test with `t5-small`
    __lowerCamelCase       :       str           = TaForConditionalGeneration.from_pretrained(self.model_name			,	load_in_abit=_a			,	device_map='auto'   )
    __lowerCamelCase       :       Optional[int]           = self.tokenizer(self.input_text			,	return_tensors='pt'   ).to(0   )
    __lowerCamelCase       :       Any           = model.generate(**_a   )

    # test with `flan-t5-small`
    __lowerCamelCase       :       Union[str, Any]           = TaForConditionalGeneration.from_pretrained(
        self.dense_act_model_name			,	load_in_abit=_a			,	device_map='auto'   )
    __lowerCamelCase       :       List[Any]           = self.tokenizer(self.input_text			,	return_tensors='pt'   ).to(0   )
    __lowerCamelCase       :       Union[str, Any]           = model.generate(**_a   )
    __lowerCamelCase       :       List[str]           = modules




  def 						_lowercase     ( self      :	Dict   )					->					Union[str, Any]:
    import bitsandbytes as bnb

    from transformers import TaForConditionalGeneration

    # test with `t5-small`
    __lowerCamelCase       :       List[Any]           = 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   )   )

    __lowerCamelCase       :       List[str]           = self.tokenizer(self.input_text			,	return_tensors='pt'   ).to(0   )
    __lowerCamelCase       :       Union[str, Any]           = model.generate(**_a   )

    # test with `flan-t5-small`
    __lowerCamelCase       :       Optional[int]           = TaForConditionalGeneration.from_pretrained(
        self.dense_act_model_name			,	load_in_abit=_a			,	device_map='auto'   )
    __lowerCamelCase       :       int           = self.tokenizer(self.input_text			,	return_tensors='pt'   ).to(0   )
    __lowerCamelCase       :       Any           = model.generate(**_a   )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Tuple:
    super().setUp()
    # model_name
    __lowerCamelCase       :       Optional[Any]           = 'bigscience/bloom-560m'
    __lowerCamelCase       :       int           = 't5-small'

    # Different types of model

    __lowerCamelCase       :       Dict           = AutoModel.from_pretrained(self.model_name			,	load_in_abit=_a			,	device_map='auto'   )
    # Sequence classification model
    __lowerCamelCase       :       Optional[Any]           = AutoModelForSequenceClassification.from_pretrained(
        self.model_name			,	load_in_abit=_a			,	device_map='auto'   )
    # CausalLM model
    __lowerCamelCase       :       str           = AutoModelForCausalLM.from_pretrained(self.model_name			,	load_in_abit=_a			,	device_map='auto'   )
    # Seq2seq model
    __lowerCamelCase       :       Any           = AutoModelForSeqaSeqLM.from_pretrained(
        self.seq_to_seq_name			,	load_in_abit=_a			,	device_map='auto'   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					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 						_lowercase     ( self      :	Tuple   )					->					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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    super().setUp()




  def 						_lowercase     ( self      :	Optional[int]   )					->					List[Any]:
    del self.pipe

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




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[int]:
    __lowerCamelCase       :       Union[str, Any]           = 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
    __lowerCamelCase       :       Dict           = self.pipe(self.input_text   )
    self.assertIn(pipeline_output[0]['generated_text']			,	self.EXPECTED_OUTPUTS   )






@require_torch_multi_gpu
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[Any]:
    super().setUp()




  def 						_lowercase     ( self      :	Optional[int]   )					->					Optional[Any]:
    __lowerCamelCase       :       Tuple           = 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
    __lowerCamelCase       :       List[Any]           = self.tokenizer(self.input_text			,	return_tensors='pt'   )

    # Second real batch
    __lowerCamelCase       :       Any           = 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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Any   )					->					int:
    __lowerCamelCase       :       Tuple           = 'facebook/opt-350m'
    super().setUp()




  def 						_lowercase     ( self      :	Any   )					->					Tuple:
    if version.parse(importlib.metadata.version('bitsandbytes'   )   ) < version.parse('0.37.0'   ):
      return

    # Step 1: freeze all parameters
    __lowerCamelCase       :       Dict           = 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():
      __lowerCamelCase       :       str           = False  # freeze the model - train adapters later
      if param.ndim == 1:
        # cast the small parameters (e.g. layernorm) to fp32 for stability
        __lowerCamelCase       :       Optional[int]           = param.data.to(torch.floataa   )

        # Step 2: add adapters
    for _, module in model.named_modules():
      if "OPTAttention" in repr(type(_a   )   ):
        __lowerCamelCase       :       Optional[int]           = LoRALayer(module.q_proj			,	rank=16   )
        __lowerCamelCase       :       Optional[Any]           = LoRALayer(module.k_proj			,	rank=16   )
        __lowerCamelCase       :       Tuple           = LoRALayer(module.v_proj			,	rank=16   )

        # Step 3: dummy batch
    __lowerCamelCase       :       Optional[int]           = self.tokenizer('Test batch '			,	return_tensors='pt'   ).to(0   )

    # Step 4: Check if the gradient is not None
    with torch.cuda.amp.autocast():
      __lowerCamelCase       :       Optional[Any]           = 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 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""gpt2-xl"""
  a_												=3.3191854854152187











'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






