




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      A      (      _SCREAMING_SNAKE_CASE    )	-> Dict:
       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 		UpperCamelCase__	(nn.Module     ):

           '''simple docstring'''



           def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Any:
                  super().__init__()
                  lowerCamelCase   :      int			 =					module
                  lowerCamelCase   :      str			 =					nn.Sequential(
                      nn.Linear(module.in_features     ,  UpperCamelCase__     ,  bias=UpperCamelCase__     )     ,  nn.Linear(UpperCamelCase__     ,  module.out_features     ,  bias=UpperCamelCase__     )     ,  )
                  lowerCamelCase   :      Any			 =					(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     _lowercase     (    self     ,  UpperCamelCase__     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      int:
                  return self.module(UpperCamelCase__     ,  *UpperCamelCase__     ,  **UpperCamelCase__     ) + self.adapter(UpperCamelCase__     )




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

    '''simple docstring'''



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

    # Constant values
    lowerCamelCase_      :	int	     =      2.1_09_65_95_52_69_25_74

    lowerCamelCase_      :	Union[str, Any]	     =      """Hello my name is"""
    lowerCamelCase_      :	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"""     )
    lowerCamelCase_      :	Optional[Any]	     =      1_0







    def     _lowercase     (    self     )     ->      List[str]:
           # Models and tokenizer
           lowerCamelCase   :      str			 =					AutoTokenizer.from_pretrained(self.model_name     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



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

           # Models and tokenizer
           lowerCamelCase   :      Tuple			 =					AutoModelForCausalLM.from_pretrained(
               self.model_name     ,  torch_dtype=torch.floataa     ,  device_map="auto"     )
           lowerCamelCase   :      Union[str, Any]			 =					AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase__     ,  device_map="auto"     )







    def     _lowercase     (    self     )     ->      Dict:
           del self.model_fpaa
           del self.model_abit

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







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      int			 =					self.model_abit.config

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

           lowerCamelCase   :      Optional[Any]			 =					config.to_dict()
           lowerCamelCase   :      Tuple			 =					config.to_diff_dict()

           lowerCamelCase   :      Tuple			 =					config.to_json_string()







    def     _lowercase     (    self     )     ->      Any:
           from bitsandbytes.nn import Paramsabit

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

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







    def     _lowercase     (    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(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     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[Any]			 =					self.tokenizer(self.input_text     ,  return_tensors="pt"     )
           lowerCamelCase   :      int			 =					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     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Dict			 =					BitsAndBytesConfig()
           lowerCamelCase   :      Dict			 =					True

           lowerCamelCase   :      Optional[int]			 =					AutoModelForCausalLM.from_pretrained(
               self.model_name     ,  quantization_config=UpperCamelCase__     ,  device_map="auto"     )

           lowerCamelCase   :      Any			 =					self.tokenizer(self.input_text     ,  return_tensors="pt"     )
           lowerCamelCase   :      Optional[int]			 =					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     _lowercase     (    self     )     ->      str:
           with self.assertRaises(UpperCamelCase__     ), tempfile.TemporaryDirectory() as tmpdirname:
                  self.model_abit.save_pretrained(UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Dict			 =					BitsAndBytesConfig()

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







    def     _lowercase     (    self     )     ->      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   :      Tuple			 =					self.tokenizer(self.input_text     ,  return_tensors="pt"     )

           lowerCamelCase   :      Optional[int]			 =					self.model_fpaa.to(torch.floataa     )
           lowerCamelCase   :      Dict			 =					self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0     )     ,  max_new_tokens=10     )

           # Check this does not throw an error
           lowerCamelCase   :      Union[str, Any]			 =					self.model_fpaa.to("cpu"     )

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

           # Check this does not throw an error
           lowerCamelCase   :      Optional[int]			 =					self.model_fpaa.float()







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      List[str]			 =					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 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



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







    def     _lowercase     (    self     )     ->      List[Any]:
           gc.collect()
           torch.cuda.empty_cache()







    def     _lowercase     (    self     )     ->      Optional[int]:
           from transformers import TaForConditionalGeneration

           lowerCamelCase   :      int			 =					TaForConditionalGeneration._keep_in_fpaa_modules
           lowerCamelCase   :      List[Any]			 =					None

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

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







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

           from transformers import TaForConditionalGeneration

           # test with `t5-small`
           lowerCamelCase   :      List[str]			 =					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   :      str			 =					self.tokenizer(self.input_text     ,  return_tensors="pt"     ).to(0     )
           lowerCamelCase   :      Union[str, Any]			 =					model.generate(**UpperCamelCase__     )

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




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



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

           # Different types of model

           lowerCamelCase   :      Dict			 =					AutoModel.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase__     ,  device_map="auto"     )
           # Sequence classification model
           lowerCamelCase   :      Any			 =					AutoModelForSequenceClassification.from_pretrained(
               self.model_name     ,  load_in_abit=UpperCamelCase__     ,  device_map="auto"     )
           # CausalLM model
           lowerCamelCase   :      Optional[Any]			 =					AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase__     ,  device_map="auto"     )
           # Seq2seq model
           lowerCamelCase   :      Optional[int]			 =					AutoModelForSeqaSeqLM.from_pretrained(
               self.seq_to_seq_name     ,  load_in_abit=UpperCamelCase__     ,  device_map="auto"     )







    def     _lowercase     (    self     )     ->      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     _lowercase     (    self     )     ->      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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      List[str]:
           super().setUp()







    def     _lowercase     (    self     )     ->      str:
           del self.pipe

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







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




@require_torch_multi_gpu
class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



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







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[int]			 =					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   :      Dict			 =					self.tokenizer(self.input_text     ,  return_tensors="pt"     )

           # Second real batch
           lowerCamelCase   :      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=UpperCamelCase__     )     ,  self.EXPECTED_OUTPUTS     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Dict			 =					"facebook/opt-350m"
           super().setUp()







    def     _lowercase     (    self     )     ->      Dict:
           if version.parse(importlib.metadata.version("bitsandbytes"     )     ) < version.parse("0.37.0"     ):
                  return

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

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

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

           # Step 4: Check if the gradient is not None
           with torch.cuda.amp.autocast():
                  lowerCamelCase   :      Tuple			 =					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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	str	     =      """gpt2-xl"""
    lowerCamelCase_      :	int	     =      3.31_91_85_48_54_15_21_87












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)






