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						(      __UpperCamelCase					: Optional[int]			)							->      int:







  """simple docstring"""





  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 __snake_case       (		nn.Module	):




      def __init__(					self    :       Optional[int] ,			_lowercase    :       nn.Module ,			_lowercase    :       int   ):



        """simple docstring"""






        super().__init__()
        SCREAMING_SNAKE_CASE__    							=   module
        SCREAMING_SNAKE_CASE__    							=   nn.Sequential(
            nn.Linear(module.in_features ,			_lowercase ,			bias=_lowercase   ) ,			nn.Linear(_lowercase ,			module.out_features ,			bias=_lowercase   ) ,			)
        SCREAMING_SNAKE_CASE__    							=   (2.0 / (5 * min(module.in_features ,			module.out_features   ))) ** 0.5
        nn.init.normal_(self.adapter[0].weight ,			std=_lowercase   )
        nn.init.zeros_(self.adapter[1].weight   )
        self.adapter.to(module.weight.device   )




      def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[str] ,			*_lowercase    :       Tuple ,			**_lowercase    :       Tuple   ):



        """simple docstring"""






        return self.module(_lowercase ,			*_lowercase ,			**_lowercase   ) + self.adapter(_lowercase   )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __snake_case       (		unittest.TestCase	):
   # We keep the constants inside the init function and model loading inside setUp function

   # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
   # Therefore here we use only bloom-1b3 to test our module
   lowerCAmelCase_														=							"bigscience/bloom-1b7"

   # Constant values
   lowerCAmelCase_														=							2.109659552692574

   lowerCAmelCase_														=							"Hello my name is"
   lowerCAmelCase_														=							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"	)
   lowerCAmelCase_														=							10




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   AutoTokenizer.from_pretrained(self.model_name   )







class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     super().setUp()

     # Models and tokenizer
     SCREAMING_SNAKE_CASE__    							=   AutoModelForCausalLM.from_pretrained(
         self.model_name ,			torch_dtype=torch.floataa ,			device_map="""auto"""   )
     SCREAMING_SNAKE_CASE__    							=   AutoModelForCausalLM.from_pretrained(self.model_name ,			load_in_abit=_lowercase ,			device_map="""auto"""   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     del self.model_fpaa
     del self.model_abit

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




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.model_abit.config

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

     SCREAMING_SNAKE_CASE__    							=   config.to_dict()
     SCREAMING_SNAKE_CASE__    							=   config.to_diff_dict()

     SCREAMING_SNAKE_CASE__    							=   config.to_json_string()




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     from bitsandbytes.nn import Paramsabit

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

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




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     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(_lowercase ,			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    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(self.input_text ,			return_tensors="""pt"""   )
     SCREAMING_SNAKE_CASE__    							=   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=_lowercase   ) ,			self.EXPECTED_OUTPUTS   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BitsAndBytesConfig()
     SCREAMING_SNAKE_CASE__    							=   True

     SCREAMING_SNAKE_CASE__    							=   AutoModelForCausalLM.from_pretrained(
         self.model_name ,			quantization_config=_lowercase ,			device_map="""auto"""   )

     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(self.input_text ,			return_tensors="""pt"""   )
     SCREAMING_SNAKE_CASE__    							=   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=_lowercase   ) ,			self.EXPECTED_OUTPUTS   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ), tempfile.TemporaryDirectory() as tmpdirname:
       self.model_abit.save_pretrained(_lowercase   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BitsAndBytesConfig()

     with self.assertRaises(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   AutoModelForCausalLM.from_pretrained(
           self.model_name ,			quantization_config=_lowercase ,			load_in_abit=_lowercase ,			device_map="""auto""" ,			bnb_abit_quant_type="""nf4""" ,			)




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     with self.assertRaises(_lowercase   ):
       # Tries with `str`
       self.model_abit.to("""cpu"""   )

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

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

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

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

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

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

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

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

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




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" ,			load_in_abit=_lowercase ,			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 __snake_case       (		unittest.TestCase	):




   @classmethod
   def 			__a		(					cls    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """t5-small"""
     SCREAMING_SNAKE_CASE__    							=   """google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
     SCREAMING_SNAKE_CASE__    							=   AutoTokenizer.from_pretrained(cls.model_name   )
     SCREAMING_SNAKE_CASE__    							=   """Translate in German: Hello, my dog is cute"""




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     from transformers import TaForConditionalGeneration

     SCREAMING_SNAKE_CASE__    							=   TaForConditionalGeneration._keep_in_fpaa_modules
     SCREAMING_SNAKE_CASE__    							=   None

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

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




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     import bitsandbytes as bnb

     from transformers import TaForConditionalGeneration

     # test with `t5-small`
     SCREAMING_SNAKE_CASE__    							=   TaForConditionalGeneration.from_pretrained(self.model_name ,			load_in_abit=_lowercase ,			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   )   )

     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(self.input_text ,			return_tensors="""pt"""   ).to(0   )
     SCREAMING_SNAKE_CASE__    							=   model.generate(**_lowercase   )

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







class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     super().setUp()
     # model_name
     SCREAMING_SNAKE_CASE__    							=   """bigscience/bloom-560m"""
     SCREAMING_SNAKE_CASE__    							=   """t5-small"""

     # Different types of model

     SCREAMING_SNAKE_CASE__    							=   AutoModel.from_pretrained(self.model_name ,			load_in_abit=_lowercase ,			device_map="""auto"""   )
     # Sequence classification model
     SCREAMING_SNAKE_CASE__    							=   AutoModelForSequenceClassification.from_pretrained(
         self.model_name ,			load_in_abit=_lowercase ,			device_map="""auto"""   )
     # CausalLM model
     SCREAMING_SNAKE_CASE__    							=   AutoModelForCausalLM.from_pretrained(self.model_name ,			load_in_abit=_lowercase ,			device_map="""auto"""   )
     # Seq2seq model
     SCREAMING_SNAKE_CASE__    							=   AutoModelForSeqaSeqLM.from_pretrained(
         self.seq_to_seq_name ,			load_in_abit=_lowercase ,			device_map="""auto"""   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     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    :       Any   ):



     """simple docstring"""






     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 __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     super().setUp()




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     del self.pipe

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




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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
     SCREAMING_SNAKE_CASE__    							=   self.pipe(self.input_text   )
     self.assertIn(pipeline_output[0]["""generated_text"""] ,			self.EXPECTED_OUTPUTS   )







@require_torch_multi_gpu
class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     super().setUp()




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   AutoModelForCausalLM.from_pretrained(
         self.model_name ,			load_in_abit=_lowercase ,			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
     SCREAMING_SNAKE_CASE__    							=   self.tokenizer(self.input_text ,			return_tensors="""pt"""   )

     # Second real batch
     SCREAMING_SNAKE_CASE__    							=   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=_lowercase   ) ,			self.EXPECTED_OUTPUTS   )







class __snake_case       (		lowerCamelCase_	):




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """facebook/opt-350m"""
     super().setUp()




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     if version.parse(importlib.metadata.version("""bitsandbytes"""   )   ) < version.parse("""0.37.0"""   ):
       return

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

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

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

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

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

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

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







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"gpt2-xl"
   lowerCAmelCase_														=							3.3191854854152187

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  if index == number_of_items:
    return 0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   knapsack(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  index + 1			)
  if weights[index] <= max_weight:
    SCREAMING_SNAKE_CASE__    							=   values[index] + knapsack(
        __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  max_weight - weights[index]				,  index + 1			)
  return max(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
