




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						(__A   :    Union[str, Any]       )   ->      Tuple:







     """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		:						Union[str, Any]					,						_snake_case		:						nn.Module					,						_snake_case		:						int):

            """simple docstring"""


            super().__init__()
            UpperCAmelCase_          =							module
            UpperCAmelCase_          =							nn.Sequential(
                nn.Linear(module.in_features					,						UpperCamelCase_					,						bias=UpperCamelCase_)					,						nn.Linear(UpperCamelCase_					,						module.out_features					,						bias=UpperCamelCase_)					,						)
            UpperCAmelCase_          =							(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 lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						str					,						*_snake_case		:						int					,						**_snake_case		:						List[str]):

            """simple docstring"""


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







@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
   UpperCAmelCase__  :    Union[str, Any]          =  '''bigscience/bloom-1b7'''

   # Constant values
   UpperCAmelCase__  :    List[Any]          =  2.109_6595_5269_2574

   UpperCAmelCase__  :    List[str]          =  '''Hello my name is'''
   UpperCAmelCase__  :    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'''		)
   UpperCAmelCase__  :    int          =  1_0







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(self.model_name)







class __snake_case			(			UpperCamelCase__		):







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        super().setUp()

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







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        del self.model_fpaa
        del self.model_abit

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







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_abit.config

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

        UpperCAmelCase_          =							config.to_dict()
        UpperCAmelCase_          =							config.to_diff_dict()

        UpperCAmelCase_          =							config.to_json_string()







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        from bitsandbytes.nn import Paramsabit

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

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







   def lowerCamelCase						(      self		:						Optional[Any]):

        """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(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 lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer(self.input_text					,						return_tensors='''pt''')
        UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							BitsAndBytesConfig()
        UpperCAmelCase_          =							True

        UpperCAmelCase_          =							AutoModelForCausalLM.from_pretrained(
            self.model_name					,						quantization_config=UpperCamelCase_					,						device_map='''auto''')

        UpperCAmelCase_          =							self.tokenizer(self.input_text					,						return_tensors='''pt''')
        UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							BitsAndBytesConfig()

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







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


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

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

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

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

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







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							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 __snake_case			(			unittest.TestCase		):







   @classmethod
   def lowerCamelCase						(      cls		:						Any):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        from transformers import TaForConditionalGeneration

        UpperCAmelCase_          =							TaForConditionalGeneration._keep_in_fpaa_modules
        UpperCAmelCase_          =							None

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

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







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        import bitsandbytes as bnb

        from transformers import TaForConditionalGeneration

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

        UpperCAmelCase_          =							self.tokenizer(self.input_text					,						return_tensors='''pt''').to(0)
        UpperCAmelCase_          =							model.generate(**UpperCamelCase_)

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







class __snake_case			(			UpperCamelCase__		):







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


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

        # Different types of model

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







   def lowerCamelCase						(      self		:						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 lowerCamelCase						(      self		:						Union[str, 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 __snake_case			(			UpperCamelCase__		):







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        super().setUp()







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        del self.pipe

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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







@require_torch_multi_gpu
class __snake_case			(			UpperCamelCase__		):







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        super().setUp()







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


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

        # Second real batch
        UpperCAmelCase_          =							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 __snake_case			(			UpperCamelCase__		):







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


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

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

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

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

        # Step 4: Check if the gradient is not None
        with torch.cuda.amp.autocast():
             UpperCAmelCase_          =							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 __snake_case			(			UpperCamelCase__		):
   UpperCAmelCase__  :    str          =  '''gpt2-xl'''
   UpperCAmelCase__  :    Optional[int]          =  3.3191_8548_5415_2187










from __future__ import annotations

from collections.abc import Iterator


class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int							)			->   None:





									"""simple docstring"""







									lowercase__            =      value
									lowercase__            =      None
									lowercase__            =      None


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Node							)			->   None:





									"""simple docstring"""







									lowercase__            =      tree

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Node | None							)			->   int:





									"""simple docstring"""







									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left							) + self.depth_first_search(node.right							)
									)


							def __iter__(							self:	List[str]							)			->   Iterator[int]:





									"""simple docstring"""







									yield self.depth_first_search(self.tree							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


