'''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      UpperCamelCase_						(					snake_case_       :       Any      )					->     Dict:






 '''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			):


  '''simple docstring'''






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





  def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       *SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    int:
   return self.module(UpperCamelCase__      ,       *UpperCamelCase__      ,       **UpperCamelCase__							) + self.adapter(UpperCamelCase__							)







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


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""bigscience/bloom-1b7"""

 # Constant values
 _SCREAMING_SNAKE_CASE     :       int      	=					2.109659552692574

 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""Hello my name is"""
 _SCREAMING_SNAKE_CASE     :       Dict      	=					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"""			)
 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					10
 def a					(      self   :	Optional[int]							)  ->    List[str]:
  # Models and tokenizer
  __lowerCAmelCase											=						AutoTokenizer.from_pretrained(self.model_name							)







class 				_lowercase     (				lowerCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	Tuple							)  ->    Tuple:
  super().setUp()

  # Models and tokenizer
  __lowerCAmelCase											=						AutoModelForCausalLM.from_pretrained(
      self.model_name      ,       torch_dtype=torch.floataa      ,       device_map="""auto"""							)
  __lowerCAmelCase											=						AutoModelForCausalLM.from_pretrained(self.model_name      ,       load_in_abit=UpperCamelCase__      ,       device_map="""auto"""							)
 def a					(      self   :	str							)  ->    Dict:
  del self.model_fpaa
  del self.model_abit

  gc.collect()
  torch.cuda.empty_cache()
 def a					(      self   :	Optional[int]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.model_abit.config

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

  __lowerCAmelCase											=						config.to_dict()
  __lowerCAmelCase											=						config.to_diff_dict()

  __lowerCAmelCase											=						config.to_json_string()
 def a					(      self   :	Tuple							)  ->    Any:
  from bitsandbytes.nn import Paramsabit

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

  self.assertAlmostEqual(mem_fpaa / mem_abit      ,       self.EXPECTED_RELATIVE_DIFFERENCE							)
  __lowerCAmelCase											=						get_some_linear_layer(self.model_abit							)
  self.assertTrue(linear.weight.__class__ == Paramsabit							)
 def a					(      self   :	str							)  ->    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(UpperCamelCase__      ,       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   :	Optional[Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						self.tokenizer(self.input_text      ,       return_tensors="""pt"""							)
  __lowerCAmelCase											=						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=UpperCamelCase__							)      ,       self.EXPECTED_OUTPUTS							)
 def a					(      self   :	Dict							)  ->    Any:
  __lowerCAmelCase											=						BitsAndBytesConfig()
  __lowerCAmelCase											=						True

  __lowerCAmelCase											=						AutoModelForCausalLM.from_pretrained(
      self.model_name      ,       quantization_config=UpperCamelCase__      ,       device_map="""auto"""							)

  __lowerCAmelCase											=						self.tokenizer(self.input_text      ,       return_tensors="""pt"""							)
  __lowerCAmelCase											=						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=UpperCamelCase__							)      ,       self.EXPECTED_OUTPUTS							)
 def a					(      self   :	str							)  ->    str:
  with self.assertRaises(UpperCamelCase__							), tempfile.TemporaryDirectory() as tmpdirname:
   self.model_abit.save_pretrained(UpperCamelCase__							)
 def a					(      self   :	Tuple							)  ->    Any:
  __lowerCAmelCase											=						BitsAndBytesConfig()

  with self.assertRaises(UpperCamelCase__							):
   __lowerCAmelCase											=						AutoModelForCausalLM.from_pretrained(
       self.model_name      ,       quantization_config=UpperCamelCase__      ,       load_in_abit=UpperCamelCase__      ,       device_map="""auto"""      ,       bnb_abit_quant_type="""nf4"""      ,       )
 def a					(      self   :	Union[str, Any]							)  ->    Optional[int]:
  with self.assertRaises(UpperCamelCase__							):
   # Tries with `str`
   self.model_abit.to("""cpu"""							)

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

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

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

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

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

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

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

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

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





 def a					(      self   :	Optional[int]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""      ,       load_in_abit=UpperCamelCase__      ,       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			):


 '''simple docstring'''






 @classmethod
 def a					(      cls   :	str							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						"t5-small"
  __lowerCAmelCase											=						"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
  __lowerCAmelCase											=						AutoTokenizer.from_pretrained(cls.model_name							)
  __lowerCAmelCase											=						"Translate in German: Hello, my dog is cute"
 def a					(      self   :	List[str]							)  ->    List[Any]:
  gc.collect()
  torch.cuda.empty_cache()
 def a					(      self   :	Dict							)  ->    Optional[int]:
  from transformers import TaForConditionalGeneration

  __lowerCAmelCase											=						TaForConditionalGeneration._keep_in_fpaa_modules
  __lowerCAmelCase											=						None

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

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





 def a					(      self   :	str							)  ->    int:
  import bitsandbytes as bnb

  from transformers import TaForConditionalGeneration

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

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

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







class 				_lowercase     (				lowerCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	List[Any]							)  ->    Optional[Any]:
  super().setUp()
  # model_name
  __lowerCAmelCase											=						"bigscience/bloom-560m"
  __lowerCAmelCase											=						"t5-small"

  # Different types of model

  __lowerCAmelCase											=						AutoModel.from_pretrained(self.model_name      ,       load_in_abit=UpperCamelCase__      ,       device_map="""auto"""							)
  # Sequence classification model
  __lowerCAmelCase											=						AutoModelForSequenceClassification.from_pretrained(
      self.model_name      ,       load_in_abit=UpperCamelCase__      ,       device_map="""auto"""							)
  # CausalLM model
  __lowerCAmelCase											=						AutoModelForCausalLM.from_pretrained(self.model_name      ,       load_in_abit=UpperCamelCase__      ,       device_map="""auto"""							)
  # Seq2seq model
  __lowerCAmelCase											=						AutoModelForSeqaSeqLM.from_pretrained(
      self.seq_to_seq_name      ,       load_in_abit=UpperCamelCase__      ,       device_map="""auto"""							)
 def a					(      self   :	Optional[int]							)  ->    List[str]:
  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   :	Optional[int]							)  ->    int:
  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__			):


 '''simple docstring'''






 def a					(      self   :	Optional[int]							)  ->    List[str]:
  super().setUp()
 def a					(      self   :	List[Any]							)  ->    str:
  del self.pipe

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





 def a					(      self   :	List[str]							)  ->    List[str]:
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						self.pipe(self.input_text							)
  self.assertIn(pipeline_output[0]["""generated_text"""]      ,       self.EXPECTED_OUTPUTS							)







@require_torch_multi_gpu
class 				_lowercase     (				lowerCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	Tuple							)  ->    Optional[Any]:
  super().setUp()





 def a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						AutoModelForCausalLM.from_pretrained(
      self.model_name      ,       load_in_abit=UpperCamelCase__      ,       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
  __lowerCAmelCase											=						self.tokenizer(self.input_text      ,       return_tensors="""pt"""							)

  # Second real batch
  __lowerCAmelCase											=						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=UpperCamelCase__							)      ,       self.EXPECTED_OUTPUTS							)







class 				_lowercase     (				lowerCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	List[Any]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						"facebook/opt-350m"
  super().setUp()





 def a					(      self   :	Optional[int]							)  ->    Dict:
  if version.parse(importlib.metadata.version("""bitsandbytes"""							)							) < version.parse("""0.37.0"""							):
   return

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

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

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

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

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

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

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







class 				_lowercase     (				lowerCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       str      	=					"""gpt2-xl"""
 _SCREAMING_SNAKE_CASE     :       int      	=					3.3191854854152187












import argparse
import os
import re


SCREAMING_SNAKE_CASE__       :      List[Any]  =   'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__       :      Tuple  =   re.compile(r'\s*\(\s*"(\S[^"]+)"')

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False    )	-> int:
       with open(_SCREAMING_SNAKE_CASE ,"r" ,encoding="utf-8"    ) as f:
              lowerCamelCase   :      List[Any]			 =					f.read()

       lowerCamelCase   :      str			 =					content.split("\n"    )
       lowerCamelCase   :      int			 =					[]
       lowerCamelCase   :      List[Any]			 =					0
       while line_idx < len(_SCREAMING_SNAKE_CASE    ):
              if _re_intro_mapping.search(lines[line_idx]    ) is not None:
                     lowerCamelCase   :      Optional[int]			 =					len(re.search(r"^(\s*)\S" ,lines[line_idx]    ).groups()[0]    ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("    ):
                            new_lines.append(lines[line_idx]    )
                            line_idx += 1

                     lowerCamelCase   :      Optional[int]			 =					[]
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   lowerCamelCase   :      List[str]			 =					line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"    ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]    )    )
                            else:
                                   blocks.append(lines[line_idx]    )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     lowerCamelCase   :      Union[str, Any]			 =					sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : _re_identifier.search(_SCREAMING_SNAKE_CASE    ).groups()[0]    )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]    )
                     line_idx += 1

       if overwrite:
              with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
                     f.write("\n".join(_SCREAMING_SNAKE_CASE    )    )
       elif "\n".join(_SCREAMING_SNAKE_CASE    ) != content:
              return True

def      A      (      _SCREAMING_SNAKE_CASE = False    )	-> List[str]:
       lowerCamelCase   :      str			 =					[os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) for f in os.listdir(_SCREAMING_SNAKE_CASE    ) if f.endswith(".py"    )]
       lowerCamelCase   :      Union[str, Any]			 =					[sort_auto_mapping(_SCREAMING_SNAKE_CASE ,overwrite=_SCREAMING_SNAKE_CASE    ) for fname in fnames]

       if not overwrite and any(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      str			 =					[f for f, d in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) if d]
              raise ValueError(
                  f'''The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE    )}. Run `make style` to fix'''
                  " this."    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[str]  =   argparse.ArgumentParser()
       parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()

       sort_all_auto_mappings(not args.check_only)






