





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_      (		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       __SCREAMING_SNAKE_CASE	(						nn.Module			):



      '''simple docstring'''






      def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):
        super().__init__()
        _snake_case     		=				module
        _snake_case     		=				nn.Sequential(
            nn.Linear(module.in_features						,   lowerCamelCase						,   bias=lowerCamelCase	)						,   nn.Linear(lowerCamelCase						,   module.out_features						,   bias=lowerCamelCase	)						,   )
        _snake_case     		=				(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						,   lowerCamelCase						,   *lowerCamelCase						,   **lowerCamelCase	):
        return self.module(lowerCamelCase						,   *lowerCamelCase						,   **lowerCamelCase	) + self.adapter(lowerCamelCase	)


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



     '''simple docstring'''






     UpperCAmelCase__       :						Tuple      =   '''bigscience/bloom-1b7'''

     # Constant values
     UpperCAmelCase__       :						List[Any]      =   2.1_09_65_95_52_69_25_74

     UpperCAmelCase__       :						Dict      =   '''Hello my name is'''
     UpperCAmelCase__       :						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__       :						Union[str, Any]      =   10

     def 	UpperCamelCase(					self	):
       # Models and tokenizer
       _snake_case     		=				AutoTokenizer.from_pretrained(self.model_name	)







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






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

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

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

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_abit.config

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

       _snake_case     		=				config.to_dict()
       _snake_case     		=				config.to_diff_dict()

       _snake_case     		=				config.to_json_string()

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

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

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

     def 	UpperCamelCase(					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 	UpperCamelCase(					self	):
       _snake_case     		=				self.tokenizer(self.input_text						,   return_tensors="pt"	)
       _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=lowerCamelCase	)						,   self.EXPECTED_OUTPUTS	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				BitsAndBytesConfig()
       _snake_case     		=				True

       _snake_case     		=				AutoModelForCausalLM.from_pretrained(
           self.model_name						,   quantization_config=lowerCamelCase						,   device_map="auto"	)

       _snake_case     		=				self.tokenizer(self.input_text						,   return_tensors="pt"	)
       _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=lowerCamelCase	)						,   self.EXPECTED_OUTPUTS	)

     def 	UpperCamelCase(					self	):
       with self.assertRaises(lowerCamelCase	), tempfile.TemporaryDirectory() as tmpdirname:
         self.model_abit.save_pretrained(lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				BitsAndBytesConfig()

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

     def 	UpperCamelCase(					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
       _snake_case     		=				self.tokenizer(self.input_text						,   return_tensors="pt"	)

       _snake_case     		=				self.model_fpaa.to(torch.floataa	)
       _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
       _snake_case     		=				self.model_fpaa.to("cpu"	)

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

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






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

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

     def 	UpperCamelCase(					self	):
       from transformers import TaForConditionalGeneration

       _snake_case     		=				TaForConditionalGeneration._keep_in_fpaa_modules
       _snake_case     		=				None

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

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






     def 	UpperCamelCase(					self	):
       import bitsandbytes as bnb

       from transformers import TaForConditionalGeneration

       # test with `t5-small`
       _snake_case     		=				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	)	)

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

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







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       super().setUp()
       # model_name
       _snake_case     		=				"bigscience/bloom-560m"
       _snake_case     		=				"t5-small"

       # Different types of model

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

     def 	UpperCamelCase(					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 	UpperCamelCase(					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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






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

     def 	UpperCamelCase(					self	):
       del self.pipe

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






     def 	UpperCamelCase(					self	):
       _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
       _snake_case     		=				self.pipe(self.input_text	)
       self.assertIn(pipeline_output[0]["generated_text"]						,   self.EXPECTED_OUTPUTS	)







@require_torch_multi_gpu
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				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
       _snake_case     		=				self.tokenizer(self.input_text						,   return_tensors="pt"	)

       # Second real batch
       _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=lowerCamelCase	)						,   self.EXPECTED_OUTPUTS	)







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"facebook/opt-350m"
       super().setUp()






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

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

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

        # Step 3: dummy batch
       _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():
         _snake_case     		=				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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   '''gpt2-xl'''
     UpperCAmelCase__       :						Dict      =   3.31_91_85_48_54_15_21_87









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


