

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   (			SCREAMING_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    _lowercase				(					nn.Module			):

         def __init__(				self	:    List[str]			,						lowerCamelCase__	:    nn.Module			,						lowerCamelCase__	:    int       ) ->			int:
               """simple docstring"""





               super().__init__()
               A_							       =		module
               A_							       =		nn.Sequential(
                   nn.Linear(module.in_features			,						lowerCamelCase__			,						bias=lowerCamelCase__       )			,						nn.Linear(lowerCamelCase__			,						module.out_features			,						bias=lowerCamelCase__       )			,						)
               A_							       =		(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	:    int			,						lowerCamelCase__	:    int			,						*lowerCamelCase__	:    Tuple			,						**lowerCamelCase__	:    List[Any]       ) ->			Tuple:
               """simple docstring"""





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

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class    _lowercase				(					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
   _lowercase     :       Union[str, Any]  					=    'bigscience/bloom-1b7'

   # Constant values
   _lowercase     :       Optional[Any]  					=    2.109_6595_5269_2574

   _lowercase     :       Optional[int]  					=    'Hello my name is'
   _lowercase     :       Union[str, 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'			)
   _lowercase     :       Dict  					=    10

   def     UpperCamelCase					(				self	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		AutoTokenizer.from_pretrained(self.model_name       )



class    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			List[Any]:
         """simple docstring"""





         super().setUp()

         # Models and tokenizer
         A_							       =		AutoModelForCausalLM.from_pretrained(
             self.model_name			,						torch_dtype=torch.floataa			,						device_map='''auto'''       )
         A_							       =		AutoModelForCausalLM.from_pretrained(self.model_name			,						load_in_abit=lowerCamelCase__			,						device_map='''auto'''       )

   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





         del self.model_fpaa
         del self.model_abit

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

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.model_abit.config

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

         A_							       =		config.to_dict()
         A_							       =		config.to_diff_dict()

         A_							       =		config.to_json_string()

   def     UpperCamelCase					(				self	:    Dict       ) ->			int:
         """simple docstring"""





         from bitsandbytes.nn import Paramsabit

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

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

   def     UpperCamelCase					(				self	:    Dict       ) ->			Tuple:
         """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(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	:    Union[str, Any]       ) ->			Tuple:
         """simple docstring"""





         A_							       =		self.tokenizer(self.input_text			,						return_tensors='''pt'''       )
         A_							       =		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     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





         A_							       =		BitsAndBytesConfig()
         A_							       =		True

         A_							       =		AutoModelForCausalLM.from_pretrained(
             self.model_name			,						quantization_config=lowerCamelCase__			,						device_map='''auto'''       )

         A_							       =		self.tokenizer(self.input_text			,						return_tensors='''pt'''       )
         A_							       =		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     UpperCamelCase					(				self	:    Optional[int]       ) ->			Tuple:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





         A_							       =		BitsAndBytesConfig()

         with self.assertRaises(lowerCamelCase__       ):
               A_							       =		AutoModelForCausalLM.from_pretrained(
                   self.model_name			,						quantization_config=lowerCamelCase__			,						load_in_abit=lowerCamelCase__			,						device_map='''auto'''			,						bnb_abit_quant_type='''nf4'''			,						)

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Tuple:
         """simple docstring"""





         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_							       =		self.tokenizer(self.input_text			,						return_tensors='''pt'''       )

         A_							       =		self.model_fpaa.to(torch.floataa       )
         A_							       =		self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0       )			,						max_new_tokens=1_0       )

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

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

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





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		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    _lowercase				(					unittest.TestCase			):

   @classmethod
   def     UpperCamelCase					(				cls	:    List[str]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Tuple:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Tuple:
         """simple docstring"""





         from transformers import TaForConditionalGeneration

         A_							       =		TaForConditionalGeneration._keep_in_fpaa_modules
         A_							       =		None

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

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





   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Optional[Any]:
         """simple docstring"""





         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         A_							       =		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_							       =		self.tokenizer(self.input_text			,						return_tensors='''pt'''       ).to(0       )
         A_							       =		model.generate(**lowerCamelCase__       )

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



class    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





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

         # Different types of model

         A_							       =		AutoModel.from_pretrained(self.model_name			,						load_in_abit=lowerCamelCase__			,						device_map='''auto'''       )
         # Sequence classification model
         A_							       =		AutoModelForSequenceClassification.from_pretrained(
             self.model_name			,						load_in_abit=lowerCamelCase__			,						device_map='''auto'''       )
         # CausalLM model
         A_							       =		AutoModelForCausalLM.from_pretrained(self.model_name			,						load_in_abit=lowerCamelCase__			,						device_map='''auto'''       )
         # Seq2seq model
         A_							       =		AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name			,						load_in_abit=lowerCamelCase__			,						device_map='''auto'''       )

   def     UpperCamelCase					(				self	:    Any       ) ->			str:
         """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     UpperCamelCase					(				self	:    str       ) ->			List[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    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Any:
         """simple docstring"""





         super().setUp()

   def     UpperCamelCase					(				self	:    Any       ) ->			Union[str, Any]:
         """simple docstring"""





         del self.pipe

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





   def     UpperCamelCase					(				self	:    Tuple       ) ->			Dict:
         """simple docstring"""





         A_							       =		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_							       =		self.pipe(self.input_text       )
         self.assertIn(pipeline_output[0]['''generated_text''']			,						self.EXPECTED_OUTPUTS       )



@require_torch_multi_gpu
class    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Union[str, Any]:
         """simple docstring"""





         super().setUp()





   def     UpperCamelCase					(				self	:    Any       ) ->			str:
         """simple docstring"""





         A_							       =		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_							       =		self.tokenizer(self.input_text			,						return_tensors='''pt'''       )

         # Second real batch
         A_							       =		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    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		'''facebook/opt-350m'''
         super().setUp()





   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





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

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

        # Step 2: add adapters
         for _, module in model.named_modules():
               if "OPTAttention" in repr(type(lowerCamelCase__       )       ):
                     A_							       =		LoRALayer(module.q_proj			,						rank=1_6       )
                     A_							       =		LoRALayer(module.k_proj			,						rank=1_6       )
                     A_							       =		LoRALayer(module.v_proj			,						rank=1_6       )

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

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
               A_							       =		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    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Dict  					=    'gpt2-xl'
   _lowercase     :       Optional[int]  					=    3.3191_8548_5415_2187








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      A_							       =		[]
      for char_count in range(SCREAMING_SNAKE_CASE   ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]   )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]   )
      return "".join(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)





