


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 UpperCamelCase_				(				__a      )	->   Union[str, Any]:
      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 A__     ( nn.Module  ):



      """simple docstring"""



      def __init__( self			:  List[str]				,				lowerCamelCase__			:  nn.Module				,				lowerCamelCase__			:  int       ):
            super().__init__()
            a__	:  int     =   module
            a__	:  Any     =   nn.Sequential(
                nn.Linear(module.in_features				,				lowerCamelCase__				,				bias=lowerCamelCase__       )				,				nn.Linear(lowerCamelCase__				,				module.out_features				,				bias=lowerCamelCase__       )				,				)
            a__	:  Tuple     =   (2.0 / (5 * min(module.in_features				,				module.out_features       ))) ** 0.5
            nn.init.normal_(self.adapter[0].weight				,				std=lowerCamelCase__       )
            nn.init.zeros_(self.adapter[1].weight       )
            self.adapter.to(module.weight.device       )

      def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Optional[int]				,				*lowerCamelCase__			:  int				,				**lowerCamelCase__			:  Dict       ):
            return self.module(lowerCamelCase__				,				*lowerCamelCase__				,				**lowerCamelCase__       ) + self.adapter(lowerCamelCase__       )



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



  """simple docstring"""



  _lowercase									=		'bigscience/bloom-1b7'

  # Constant values
  _lowercase									=		2.1_09_65_95_52_69_25_74

  _lowercase									=		'Hello my name is'
  _lowercase									=		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'  )
  _lowercase									=		1_0

  def    _UpperCamelCase( self			:  Dict       ):
        # Models and tokenizer
        a__	:  List[str]     =   AutoTokenizer.from_pretrained(self.model_name       )




class A__     ( A__  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        super().setUp()

        # Models and tokenizer
        a__	:  List[Any]     =   AutoModelForCausalLM.from_pretrained(
            self.model_name				,				torch_dtype=torch.floataa				,				device_map="auto"       )
        a__	:  Union[str, Any]     =   AutoModelForCausalLM.from_pretrained(self.model_name				,				load_in_abit=lowerCamelCase__				,				device_map="auto"       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        del self.model_fpaa
        del self.model_abit

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

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  str     =   self.model_abit.config

        self.assertTrue(hasattr(lowerCamelCase__				,				"quantization_config"       )       )

        a__	:  Optional[Any]     =   config.to_dict()
        a__	:  int     =   config.to_diff_dict()

        a__	:  List[str]     =   config.to_json_string()

  def    _UpperCamelCase( self			:  int       ):
        from bitsandbytes.nn import Paramsabit

        a__	:  List[Any]     =   self.model_fpaa.get_memory_footprint()
        a__	:  str     =   self.model_abit.get_memory_footprint()

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

  def    _UpperCamelCase( self			:  Tuple       ):
        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(lowerCamelCase__				,				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__	:  Dict     =   self.tokenizer(self.input_text				,				return_tensors="pt"       )
        a__	:  Tuple     =   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=lowerCamelCase__       )				,				self.EXPECTED_OUTPUTS       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Optional[Any]     =   BitsAndBytesConfig()
        a__	:  Optional[int]     =   True

        a__	:  int     =   AutoModelForCausalLM.from_pretrained(
            self.model_name				,				quantization_config=lowerCamelCase__				,				device_map="auto"       )

        a__	:  str     =   self.tokenizer(self.input_text				,				return_tensors="pt"       )
        a__	:  int     =   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=lowerCamelCase__       )				,				self.EXPECTED_OUTPUTS       )

  def    _UpperCamelCase( self			:  Dict       ):
        with self.assertRaises(lowerCamelCase__       ), tempfile.TemporaryDirectory() as tmpdirname:
              self.model_abit.save_pretrained(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  int     =   BitsAndBytesConfig()

        with self.assertRaises(lowerCamelCase__       ):
              a__	:  Dict     =   AutoModelForCausalLM.from_pretrained(
                  self.model_name				,				quantization_config=lowerCamelCase__				,				load_in_abit=lowerCamelCase__				,				device_map="auto"				,				bnb_abit_quant_type="nf4"				,				)

  def    _UpperCamelCase( self			:  int       ):
        with self.assertRaises(lowerCamelCase__       ):
              # Tries with `str`
              self.model_abit.to("cpu"       )

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

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

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

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

        # Test if we did not break anything
        a__	:  int     =   self.tokenizer(self.input_text				,				return_tensors="pt"       )

        a__	:  Any     =   self.model_fpaa.to(torch.floataa       )
        a__	:  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
        a__	:  Tuple     =   self.model_fpaa.to("cpu"       )

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

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

  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  List[str]     =   AutoModelForSeqaSeqLM.from_pretrained("t5-small"				,				load_in_abit=lowerCamelCase__				,				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 A__     ( unittest.TestCase  ):



  """simple docstring"""



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

  def    _UpperCamelCase( self			:  Optional[int]       ):
        gc.collect()
        torch.cuda.empty_cache()

  def    _UpperCamelCase( self			:  Optional[int]       ):
        from transformers import TaForConditionalGeneration

        a__	:  List[Any]     =   TaForConditionalGeneration._keep_in_fpaa_modules
        a__	:  Optional[Any]     =   None

        # test with `t5-small`
        a__	:  Any     =   TaForConditionalGeneration.from_pretrained(self.model_name				,				load_in_abit=lowerCamelCase__				,				device_map="auto"       )
        a__	:  Dict     =   self.tokenizer(self.input_text				,				return_tensors="pt"       ).to(0       )
        a__	:  int     =   model.generate(**lowerCamelCase__       )

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

  def    _UpperCamelCase( self			:  List[Any]       ):
        import bitsandbytes as bnb

        from transformers import TaForConditionalGeneration

        # test with `t5-small`
        a__	:  List[str]     =   TaForConditionalGeneration.from_pretrained(self.model_name				,				load_in_abit=lowerCamelCase__				,				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__	:  Union[str, Any]     =   self.tokenizer(self.input_text				,				return_tensors="pt"       ).to(0       )
        a__	:  int     =   model.generate(**lowerCamelCase__       )

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




class A__     ( A__  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  List[str]       ):
        super().setUp()
        # model_name
        a__	:  Union[str, Any]     =   "bigscience/bloom-560m"
        a__	:  Union[str, Any]     =   "t5-small"

        # Different types of model

        a__	:  int     =   AutoModel.from_pretrained(self.model_name				,				load_in_abit=lowerCamelCase__				,				device_map="auto"       )
        # Sequence classification model
        a__	:  Dict     =   AutoModelForSequenceClassification.from_pretrained(
            self.model_name				,				load_in_abit=lowerCamelCase__				,				device_map="auto"       )
        # CausalLM model
        a__	:  str     =   AutoModelForCausalLM.from_pretrained(self.model_name				,				load_in_abit=lowerCamelCase__				,				device_map="auto"       )
        # Seq2seq model
        a__	:  Dict     =   AutoModelForSeqaSeqLM.from_pretrained(
            self.seq_to_seq_name				,				load_in_abit=lowerCamelCase__				,				device_map="auto"       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        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			:  Union[str, 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 A__     ( A__  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Dict       ):
        super().setUp()

  def    _UpperCamelCase( self			:  int       ):
        del self.pipe

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

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  int     =   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__	:  Tuple     =   self.pipe(self.input_text       )
        self.assertIn(pipeline_output[0]["generated_text"]				,				self.EXPECTED_OUTPUTS       )




@require_torch_multi_gpu
class A__     ( A__  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Tuple       ):
        super().setUp()

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  str     =   AutoModelForCausalLM.from_pretrained(
            self.model_name				,				load_in_abit=lowerCamelCase__				,				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__	:  List[Any]     =   self.tokenizer(self.input_text				,				return_tensors="pt"       )

        # Second real batch
        a__	:  List[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=lowerCamelCase__       )				,				self.EXPECTED_OUTPUTS       )




class A__     ( A__  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Dict       ):
        a__	:  Any     =   "facebook/opt-350m"
        super().setUp()

  def    _UpperCamelCase( self			:  int       ):
        if version.parse(importlib.metadata.version("bitsandbytes"       )       ) < version.parse("0.37.0"       ):
              return

        # Step 1: freeze all parameters
        a__	:  Tuple     =   AutoModelForCausalLM.from_pretrained(self.model_name				,				load_in_abit=lowerCamelCase__       )

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

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

        # Step 2: add adapters
        for _, module in model.named_modules():
              if "OPTAttention" in repr(type(lowerCamelCase__       )       ):
                    a__	:  Dict     =   LoRALayer(module.q_proj				,				rank=16       )
                    a__	:  List[Any]     =   LoRALayer(module.k_proj				,				rank=16       )
                    a__	:  List[Any]     =   LoRALayer(module.v_proj				,				rank=16       )

        # Step 3: dummy batch
        a__	:  Dict     =   self.tokenizer("Test batch "				,				return_tensors="pt"       ).to(0       )

        # Step 4: Check if the gradient is not None
        with torch.cuda.amp.autocast():
              a__	:  Optional[Any]     =   model.forward(**lowerCamelCase__       )
              out.logits.norm().backward()

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




class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		'gpt2-xl'
  _lowercase									=		3.31_91_85_48_54_15_21_87



def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = " "		):
      UpperCamelCase__     : Optional[int]											=					[]

      UpperCamelCase__     : int											=					0
      for index, char in enumerate(UpperCamelCase__		):
            if char == separator:
                  split_words.append(string[last_index:index]		)
                  UpperCamelCase__     : int											=					index + 1
            elif index + 1 == len(UpperCamelCase__		):
                  split_words.append(string[last_index : index + 1]		)
      return split_words


if __name__ == "__main__":
    from doctest import testmod

    testmod()


