






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   snake_case (UpperCAmelCase__  )	->    List[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     _lowerCAmelCase(	nn.Module ):





          """simple docstring"""







          def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
            super().__init__()
            UpperCamelCase_: Dict   				=    module
            UpperCamelCase_: str   				=    nn.Sequential(
                nn.Linear(module.in_features  ,      _lowerCamelCase  ,      bias=_lowerCamelCase							)  ,      nn.Linear(_lowerCamelCase  ,      module.out_features  ,      bias=_lowerCamelCase							)  ,      )
            UpperCamelCase_: List[Any]   				=    (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       _a   ( self  ,      _lowerCamelCase  ,      *_lowerCamelCase  ,      **_lowerCamelCase							):
            return self.module(_lowerCamelCase  ,      *_lowerCamelCase  ,      **_lowerCamelCase							) + self.adapter(_lowerCamelCase							)




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





       """simple docstring"""







       a    :		Optional[Any]									='''bigscience/bloom-1b7'''

       # Constant values
       a    :		Any									=2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

       a    :		Any									='''Hello my name is'''
       a    :		List[str]									=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    :		Dict									=10





       def       _a   ( self							):
         # Models and tokenizer
         UpperCamelCase_: str   				=    AutoTokenizer.from_pretrained(self.model_name							)







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def       _a   ( self							):
         super().setUp()

         # Models and tokenizer
         UpperCamelCase_: Any   				=    AutoModelForCausalLM.from_pretrained(
             self.model_name  ,      torch_dtype=torch.floataa  ,      device_map='auto'							)
         UpperCamelCase_: str   				=    AutoModelForCausalLM.from_pretrained(self.model_name  ,      load_in_abit=_lowerCamelCase  ,      device_map='auto'							)





       def       _a   ( self							):
         del self.model_fpaa
         del self.model_abit

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





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.model_abit.config

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

         UpperCamelCase_: Dict   				=    config.to_dict()
         UpperCamelCase_: Union[str, Any]   				=    config.to_diff_dict()

         UpperCamelCase_: Any   				=    config.to_json_string()





       def       _a   ( self							):
         from bitsandbytes.nn import Paramsabit

         UpperCamelCase_: List[str]   				=    self.model_fpaa.get_memory_footprint()
         UpperCamelCase_: int   				=    self.model_abit.get_memory_footprint()

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





       def       _a   ( self							):
         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       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.tokenizer(self.input_text  ,      return_tensors='pt'							)
         UpperCamelCase_: Optional[int]   				=    self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0							)  ,      max_new_tokens=1_0							)

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





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    BitsAndBytesConfig()
         UpperCamelCase_: Optional[Any]   				=    True

         UpperCamelCase_: str   				=    AutoModelForCausalLM.from_pretrained(
             self.model_name  ,      quantization_config=_lowerCamelCase  ,      device_map='auto'							)

         UpperCamelCase_: Optional[int]   				=    self.tokenizer(self.input_text  ,      return_tensors='pt'							)
         UpperCamelCase_: List[str]   				=    model_abit_from_config.generate(
             input_ids=encoded_input['input_ids'].to(0							)  ,      max_new_tokens=1_0							)

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





       def       _a   ( self							):
         with self.assertRaises(_lowerCamelCase							), tempfile.TemporaryDirectory() as tmpdirname:
           self.model_abit.save_pretrained(_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    BitsAndBytesConfig()

         with self.assertRaises(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    AutoModelForCausalLM.from_pretrained(
               self.model_name  ,      quantization_config=_lowerCamelCase  ,      load_in_abit=_lowerCamelCase  ,      device_map='auto'  ,      bnb_abit_quant_type='nf4'  ,      )





       def       _a   ( self							):
         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
         UpperCamelCase_: Optional[Any]   				=    self.tokenizer(self.input_text  ,      return_tensors='pt'							)

         UpperCamelCase_: Dict   				=    self.model_fpaa.to(torch.floataa							)
         UpperCamelCase_: Optional[Any]   				=    self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0							)  ,      max_new_tokens=1_0							)

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

         # Check this does not throw an error
         UpperCamelCase_: List[str]   				=    self.model_fpaa.half()

         # Check this does not throw an error
         UpperCamelCase_: Optional[Any]   				=    self.model_fpaa.float()





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    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     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







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





       def       _a   ( self							):
         gc.collect()
         torch.cuda.empty_cache()





       def       _a   ( self							):
         from transformers import TaForConditionalGeneration

         UpperCamelCase_: List[str]   				=    TaForConditionalGeneration._keep_in_fpaa_modules
         UpperCamelCase_: Optional[Any]   				=    None

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

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





       def       _a   ( self							):
         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         UpperCamelCase_: 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							)							)

         UpperCamelCase_: Union[str, Any]   				=    self.tokenizer(self.input_text  ,      return_tensors='pt'							).to(0							)
         UpperCamelCase_: Optional[int]   				=    model.generate(**_lowerCamelCase							)

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







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def       _a   ( self							):
         super().setUp()
         # model_name
         UpperCamelCase_: Tuple   				=    'bigscience/bloom-560m'
         UpperCamelCase_: Tuple   				=    't5-small'

         # Different types of model

         UpperCamelCase_: List[str]   				=    AutoModel.from_pretrained(self.model_name  ,      load_in_abit=_lowerCamelCase  ,      device_map='auto'							)
         # Sequence classification model
         UpperCamelCase_: str   				=    AutoModelForSequenceClassification.from_pretrained(
             self.model_name  ,      load_in_abit=_lowerCamelCase  ,      device_map='auto'							)
         # CausalLM model
         UpperCamelCase_: List[str]   				=    AutoModelForCausalLM.from_pretrained(self.model_name  ,      load_in_abit=_lowerCamelCase  ,      device_map='auto'							)
         # Seq2seq model
         UpperCamelCase_: str   				=    AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name  ,      load_in_abit=_lowerCamelCase  ,      device_map='auto'							)





       def       _a   ( self							):
         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       _a   ( self							):
         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(	UpperCAmelCase_ ):





       """simple docstring"""







       def       _a   ( self							):
         super().setUp()





       def       _a   ( self							):
         del self.pipe

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





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    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
         UpperCamelCase_: int   				=    self.pipe(self.input_text							)
         self.assertIn(pipeline_output[0]['generated_text']  ,      self.EXPECTED_OUTPUTS							)







@require_torch_multi_gpu
class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def       _a   ( self							):
         super().setUp()





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    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
         UpperCamelCase_: int   				=    self.tokenizer(self.input_text  ,      return_tensors='pt'							)

         # Second real batch
         UpperCamelCase_: List[Any]   				=    model_parallel.generate(input_ids=encoded_input['input_ids'].to(0							)  ,      max_new_tokens=1_0							)
         self.assertIn(self.tokenizer.decode(output_parallel[0]  ,      skip_special_tokens=_lowerCamelCase							)  ,      self.EXPECTED_OUTPUTS							)







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    'facebook/opt-350m'
         super().setUp()





       def       _a   ( self							):
         if version.parse(importlib.metadata.version('bitsandbytes'							)							) < version.parse('0.37.0'							):
           return

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

        # Step 2: add adapters
         for _, module in model.named_modules():
           if "OPTAttention" in repr(type(_lowerCamelCase							)							):
             UpperCamelCase_: List[Any]   				=    LoRALayer(module.q_proj  ,      rank=1_6							)
             UpperCamelCase_: str   				=    LoRALayer(module.k_proj  ,      rank=1_6							)
             UpperCamelCase_: Dict   				=    LoRALayer(module.v_proj  ,      rank=1_6							)

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

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
           UpperCamelCase_: Union[str, 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     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Dict									='''gpt2-xl'''
       a    :		List[str]									=3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @staticmethod
       @abstractmethod
       def       _a   ( _lowerCamelCase							):
         raise NotImplementedError()





       @abstractmethod
       def       _a   ( self							):
         raise NotImplementedError()