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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any     )							-> List[str]:







  """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 __snake_case    (						nn.Module		):




      def __init__(   self      :    List[str]	,     _lowercase      :    nn.Module	,     _lowercase      :    int				):



        """simple docstring"""






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




      def      __a						(   self      :    str	,     _lowercase      :    Tuple	,     *_lowercase      :    int	,     **_lowercase      :    Optional[Any]				):



        """simple docstring"""






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







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class __snake_case    (						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
   lowerCAmelCase_														=							"bigscience/bloom-1b7"

   # Constant values
   lowerCAmelCase_														=							2.109659552692574

   lowerCAmelCase_														=							"Hello my name is"
   lowerCAmelCase_														=							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_														=							10




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(self.model_name				)







class __snake_case    (						lowerCamelCase_		):




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     super().setUp()

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




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     del self.model_fpaa
     del self.model_abit

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




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_abit.config

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

     SCREAMING_SNAKE_CASE__      							=				config.to_dict()
     SCREAMING_SNAKE_CASE__      							=				config.to_diff_dict()

     SCREAMING_SNAKE_CASE__      							=				config.to_json_string()




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     from bitsandbytes.nn import Paramsabit

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

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




   def      __a						(   self      :    Optional[int]				):



     """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(_lowercase	,     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      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.tokenizer(self.input_text	,     return_tensors="""pt"""				)
     SCREAMING_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=_lowercase				)	,     self.EXPECTED_OUTPUTS				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				BitsAndBytesConfig()
     SCREAMING_SNAKE_CASE__      							=				True

     SCREAMING_SNAKE_CASE__      							=				AutoModelForCausalLM.from_pretrained(
         self.model_name	,     quantization_config=_lowercase	,     device_map="""auto"""				)

     SCREAMING_SNAKE_CASE__      							=				self.tokenizer(self.input_text	,     return_tensors="""pt"""				)
     SCREAMING_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=_lowercase				)	,     self.EXPECTED_OUTPUTS				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				BitsAndBytesConfig()

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




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     with self.assertRaises(_lowercase				):
       # Tries with `str`
       self.model_abit.to("""cpu"""				)

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

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

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

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

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

     SCREAMING_SNAKE_CASE__      							=				self.model_fpaa.to(torch.floataa				)
     SCREAMING_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
     SCREAMING_SNAKE_CASE__      							=				self.model_fpaa.to("""cpu"""				)

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

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




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""	,     load_in_abit=_lowercase	,     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 __snake_case    (						unittest.TestCase		):




   @classmethod
   def      __a						(   cls      :    List[str]				):



     """simple docstring"""






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




   def      __a						(   self      :    Any				):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     from transformers import TaForConditionalGeneration

     SCREAMING_SNAKE_CASE__      							=				TaForConditionalGeneration._keep_in_fpaa_modules
     SCREAMING_SNAKE_CASE__      							=				None

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

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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     import bitsandbytes as bnb

     from transformers import TaForConditionalGeneration

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

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

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







class __snake_case    (						lowerCamelCase_		):




   def      __a						(   self      :    int				):



     """simple docstring"""






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

     # Different types of model

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




   def      __a						(   self      :    List[Any]				):



     """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      __a						(   self      :    Tuple				):



     """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 __snake_case    (						lowerCamelCase_		):




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     super().setUp()




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     del self.pipe

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




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_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
     SCREAMING_SNAKE_CASE__      							=				self.pipe(self.input_text				)
     self.assertIn(pipeline_output[0]["""generated_text"""]	,     self.EXPECTED_OUTPUTS				)







@require_torch_multi_gpu
class __snake_case    (						lowerCamelCase_		):




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     super().setUp()




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForCausalLM.from_pretrained(
         self.model_name	,     load_in_abit=_lowercase	,     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
     SCREAMING_SNAKE_CASE__      							=				self.tokenizer(self.input_text	,     return_tensors="""pt"""				)

     # Second real batch
     SCREAMING_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=_lowercase				)	,     self.EXPECTED_OUTPUTS				)







class __snake_case    (						lowerCamelCase_		):




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""facebook/opt-350m"""
     super().setUp()




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






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

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

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

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

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

        # Step 3: dummy batch
     SCREAMING_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():
       SCREAMING_SNAKE_CASE__      							=				model.forward(**_lowercase				)
       out.logits.norm().backward()

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







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							"gpt2-xl"
   lowerCAmelCase_														=							3.3191854854152187

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[True] * max_number
  for i in range(2			,					isqrt(max_number - 1     ) + 1     ):
    if is_prime[i]:
      for j in range(i**2			,					__UpperCamelCase			,					__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__      							=				False

  return [i for i in range(2			,					__UpperCamelCase     ) if is_prime[i]]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


if __name__ == "__main__":
   print(F"""{solution() = }""")
