
'''simple docstring'''




from torch import nn







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    if act_fn in ["swish", "silu"]:
        return nn.SiLU()
    elif act_fn == "mish":
        return nn.Mish()
    elif act_fn == "gelu":
        return nn.GELU()
    else:
        raise ValueError(F'Unsupported activation function: {act_fn}'		)






'''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 						lowerCAmelCase_							(  snake_case__		):




    '''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 					A							(				nn.Module		):





           def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






               """simple docstring"""






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





           def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     *SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )					->							str:






               """simple docstring"""






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





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					A							(				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
     __magic_name__          =						'''bigscience/bloom-1b7'''

     # Constant values
     __magic_name__          =						2.1_09_65_95_52_69_25_74

     __magic_name__          =						'''Hello my name is'''
     __magic_name__          =						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'''		)
     __magic_name__          =						10





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Any   =		AutoTokenizer.from_pretrained(self.model_name      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         super().setUp()

         # Models and tokenizer
         A			:  Optional[Any]   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     torch_dtype=torch.floataa		,     device_map='''auto'''      )
         A			:  List[Any]   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.model_fpaa
         del self.model_abit

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





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Any   =		self.model_abit.config

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

         A			:  List[str]   =		config.to_dict()
         A			:  List[Any]   =		config.to_diff_dict()

         A			:  Union[str, Any]   =		config.to_json_string()





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         from bitsandbytes.nn import Paramsabit

         A			:  Optional[Any]   =		self.model_fpaa.get_memory_footprint()
         A			:  Any   =		self.model_abit.get_memory_footprint()

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





     def 							__lowerCAmelCase				(				self      )					->							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(SCREAMING_SNAKE_CASE		,     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 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  List[str]   =		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=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  str   =		BitsAndBytesConfig()
         A			:  Optional[Any]   =		True

         A			:  int   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )

         A			:  Union[str, Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  Tuple   =		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=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		BitsAndBytesConfig()

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             A			:  int   =		AutoModelForCausalLM.from_pretrained(
                 self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''		,     bnb_abit_quant_type='''nf4'''		,     )





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # Tries with `str`
             self.model_abit.to('''cpu'''      )

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

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

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

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             # 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			:  Dict   =		self.model_fpaa.to(torch.floataa      )
         A			:  Union[str, 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			:  str   =		self.model_fpaa.to('''cpu'''      )

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

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





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Tuple   =		AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''		,     load_in_abit=SCREAMING_SNAKE_CASE		,     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		):





     @classmethod
     def 							__lowerCAmelCase				(				cls      )					->							Union[str, Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         from transformers import TaForConditionalGeneration

         A			:  int   =		TaForConditionalGeneration._keep_in_fpaa_modules
         A			:  Optional[Any]   =		None

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

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





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

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

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





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()
         # model_name
         A			:  Tuple   =		'''bigscience/bloom-560m'''
         A			:  int   =		'''t5-small'''

         # Different types of model

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





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """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 							__lowerCAmelCase				(				self      )					->							List[str]:






         """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 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.pipe

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





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Optional[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
         A			:  str   =		self.pipe(self.input_text      )
         self.assertIn(pipeline_output[0]['''generated_text''']		,     self.EXPECTED_OUTPUTS      )





@require_torch_multi_gpu
class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     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			:  Optional[int]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         # Second real batch
         A			:  Optional[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=SCREAMING_SNAKE_CASE      )		,     self.EXPECTED_OUTPUTS      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  int   =		'''facebook/opt-350m'''
         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         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=SCREAMING_SNAKE_CASE      )

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

         for param in model.parameters():
             A			:  Optional[int]   =		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(SCREAMING_SNAKE_CASE      )      ):
                 A			:  Tuple   =		LoRALayer(module.q_proj		,     rank=16      )
                 A			:  Tuple   =		LoRALayer(module.k_proj		,     rank=16      )
                 A			:  str   =		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			:  str   =		model.forward(**SCREAMING_SNAKE_CASE      )
             out.logits.norm().backward()

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





class 					A							(				__snake_case		):
     __magic_name__          =						'''gpt2-xl'''
     __magic_name__          =						3.31_91_85_48_54_15_21_87




