






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   lowerCamelCase__			(     lowercase				):

  """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     SCREAMING_SNAKE_CASE			(			nn.Module					):
        '''simple docstring'''


        def __init__(				self       :	Optional[Any]					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	str      ):
          super().__init__()
          SCREAMING_SNAKE_CASE       :      Optional[int]							=    module
          SCREAMING_SNAKE_CASE       :      List[Any]							=    nn.Sequential(
              nn.Linear(module.in_features					, A__					, bias=A__      )					, nn.Linear(A__					, module.out_features					, bias=A__      )					, )
          SCREAMING_SNAKE_CASE       :      List[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 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	Union[str, Any]					, *UpperCAmelCase_       :	int					, **UpperCAmelCase_       :	Union[str, Any]      ):
          return self.module(A__					, *A__					, **A__      ) + self.adapter(A__      )


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		int			=	'''bigscience/bloom-1b7'''

     # Constant values
     UpperCamelCase_							:		List[Any]			=	2.109659552692574

     UpperCamelCase_							:		str			=	'''Hello my name is'''
     UpperCamelCase_							:		List[Any]			=	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'''					)
     UpperCamelCase_							:		List[str]			=	1_0


     def 				_A       (				self       :	Any      ):
       # Models and tokenizer
       SCREAMING_SNAKE_CASE       :      Any							=    AutoTokenizer.from_pretrained(self.model_name      )





class     SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	int      ):
       super().setUp()

       # Models and tokenizer
       SCREAMING_SNAKE_CASE       :      Any							=    AutoModelForCausalLM.from_pretrained(
           self.model_name					, torch_dtype=torch.floataa					, device_map="auto"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    AutoModelForCausalLM.from_pretrained(self.model_name					, load_in_abit=A__					, device_map="auto"      )


     def 				_A       (				self       :	int      ):
       del self.model_fpaa
       del self.model_abit

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


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      int							=    self.model_abit.config

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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    config.to_dict()
       SCREAMING_SNAKE_CASE       :      str							=    config.to_diff_dict()

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


     def 				_A       (				self       :	Any      ):
       from bitsandbytes.nn import Paramsabit

       SCREAMING_SNAKE_CASE       :      Any							=    self.model_fpaa.get_memory_footprint()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.model_abit.get_memory_footprint()

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


     def 				_A       (				self       :	List[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 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      str							=    self.tokenizer(self.input_text					, return_tensors="pt"      )
       SCREAMING_SNAKE_CASE       :      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=A__      )					, self.EXPECTED_OUTPUTS      )


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    BitsAndBytesConfig()
       SCREAMING_SNAKE_CASE       :      Dict							=    True

       SCREAMING_SNAKE_CASE       :      List[str]							=    AutoModelForCausalLM.from_pretrained(
           self.model_name					, quantization_config=A__					, device_map="auto"      )

       SCREAMING_SNAKE_CASE       :      str							=    self.tokenizer(self.input_text					, return_tensors="pt"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    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 				_A       (				self       :	str      ):
       with self.assertRaises(A__      ), tempfile.TemporaryDirectory() as tmpdirname:
         self.model_abit.save_pretrained(A__      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    BitsAndBytesConfig()

       with self.assertRaises(A__      ):
         SCREAMING_SNAKE_CASE       :      str							=    AutoModelForCausalLM.from_pretrained(
             self.model_name					, quantization_config=A__					, load_in_abit=A__					, device_map="auto"					, bnb_abit_quant_type="nf4"					, )


     def 				_A       (				self       :	List[Any]      ):
       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
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.tokenizer(self.input_text					, return_tensors="pt"      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    self.model_fpaa.to(torch.floataa      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    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       :      int							=    self.model_fpaa.to("cpu"      )

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

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






     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    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     SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


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


     def 				_A       (				self       :	List[str]      ):
       gc.collect()
       torch.cuda.empty_cache()


     def 				_A       (				self       :	int      ):
       from transformers import TaForConditionalGeneration

       SCREAMING_SNAKE_CASE       :      Tuple							=    TaForConditionalGeneration._keep_in_fpaa_modules
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    None

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

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






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

       from transformers import TaForConditionalGeneration

       # test with `t5-small`
       SCREAMING_SNAKE_CASE       :      Dict							=    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      )      )

       SCREAMING_SNAKE_CASE       :      Tuple							=    self.tokenizer(self.input_text					, return_tensors="pt"      ).to(0      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    model.generate(**A__      )

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





class     SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict      ):
       super().setUp()
       # model_name
       SCREAMING_SNAKE_CASE       :      Any							=    """bigscience/bloom-560m"""
       SCREAMING_SNAKE_CASE       :      int							=    """t5-small"""

       # Different types of model

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    AutoModel.from_pretrained(self.model_name					, load_in_abit=A__					, device_map="auto"      )
       # Sequence classification model
       SCREAMING_SNAKE_CASE       :      Dict							=    AutoModelForSequenceClassification.from_pretrained(
           self.model_name					, load_in_abit=A__					, device_map="auto"      )
       # CausalLM model
       SCREAMING_SNAKE_CASE       :      str							=    AutoModelForCausalLM.from_pretrained(self.model_name					, load_in_abit=A__					, device_map="auto"      )
       # Seq2seq model
       SCREAMING_SNAKE_CASE       :      List[Any]							=    AutoModelForSeqaSeqLM.from_pretrained(
           self.seq_to_seq_name					, load_in_abit=A__					, device_map="auto"      )


     def 				_A       (				self       :	int      ):
       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       :	str      ):
       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     SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Optional[int]      ):
       super().setUp()


     def 				_A       (				self       :	List[str]      ):
       del self.pipe

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






     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      str							=    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       :      str							=    self.pipe(self.input_text      )
       self.assertIn(pipeline_output[0]["generated_text"]					, self.EXPECTED_OUTPUTS      )





@require_torch_multi_gpu
class     SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	str      ):
       super().setUp()






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    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
       SCREAMING_SNAKE_CASE       :      Dict							=    self.tokenizer(self.input_text					, return_tensors="pt"      )

       # Second real batch
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    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     SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      str							=    """facebook/opt-350m"""
       super().setUp()






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

       # Step 1: freeze all parameters
       SCREAMING_SNAKE_CASE       :      str							=    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():
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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       :      List[str]							=    param.data.to(torch.floataa      )

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

        # Step 3: dummy batch
       SCREAMING_SNAKE_CASE       :      List[Any]							=    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       :      List[str]							=    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     SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	'''gpt2-xl'''
     UpperCamelCase_							:		str			=	3.3191854854152187














from ..utils import DummyObject, requires_backends


class  SCREAMING_SNAKE_CASE			(			metaclass=lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	['''keras_nlp''']


     def __init__(				self       :	Dict					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Dict      ):
       requires_backends(self					, ["keras_nlp"]      )






