
"""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 	__a     (		__lowerCamelCase  ):
   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 A_	(nn.Module      ):




      '''simple docstring'''







      def __init__(		self				,   lowercase_				,   lowercase_  ):

         """simple docstring"""
         super().__init__()
         UpperCAmelCase_					:					List[str]			     =    module
         UpperCAmelCase_					:					Dict			     =    nn.Sequential(
             nn.Linear(module.in_features				,   lowercase_				,   bias=lowercase_  )				,   nn.Linear(lowercase_				,   module.out_features				,   bias=lowercase_  )				,   )
         UpperCAmelCase_					:					Any			     =    (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        UpperCamelCase__					(		self				,   lowercase_				,   *lowercase_				,   **lowercase_  ):

         """simple docstring"""
         return self.module(lowercase_				,   *lowercase_				,   **lowercase_  ) + self.adapter(lowercase_  )


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




  '''simple docstring'''
  # 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
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		"""bigscience/bloom-1b7"""

  # Constant values
  SCREAMING_SNAKE_CASE__			:						Any       =		2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""Hello my name is"""
  SCREAMING_SNAKE_CASE__			:						Optional[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"""      )
  SCREAMING_SNAKE_CASE__			:						Dict       =		10







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # Models and tokenizer
     UpperCAmelCase_					:					Union[str, Any]			     =    AutoTokenizer.from_pretrained(self.model_name  )




class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()

     # Models and tokenizer
     UpperCAmelCase_					:					Optional[Any]			     =    AutoModelForCausalLM.from_pretrained(
         self.model_name				,   torch_dtype=torch.floataa				,   device_map="auto"  )
     UpperCAmelCase_					:					str			     =    AutoModelForCausalLM.from_pretrained(self.model_name				,   load_in_abit=lowercase_				,   device_map="auto"  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     del self.model_fpaa
     del self.model_abit

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.model_abit.config

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

     UpperCAmelCase_					:					Optional[int]			     =    config.to_dict()
     UpperCAmelCase_					:					Union[str, Any]			     =    config.to_diff_dict()

     UpperCAmelCase_					:					Dict			     =    config.to_json_string()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     from bitsandbytes.nn import Paramsabit

     UpperCAmelCase_					:					str			     =    self.model_fpaa.get_memory_footprint()
     UpperCAmelCase_					:					Dict			     =    self.model_abit.get_memory_footprint()

     self.assertAlmostEqual(mem_fpaa / mem_abit				,   self.EXPECTED_RELATIVE_DIFFERENCE  )
     UpperCAmelCase_					:					List[str]			     =    get_some_linear_layer(self.model_abit  )
     self.assertTrue(linear.weight.__class__ == Paramsabit  )







  def        UpperCamelCase__					(		self  ):

     """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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.tokenizer(self.input_text				,   return_tensors="pt"  )
     UpperCAmelCase_					:					Optional[Any]			     =    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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    BitsAndBytesConfig()
     UpperCAmelCase_					:					Tuple			     =    True

     UpperCAmelCase_					:					int			     =    AutoModelForCausalLM.from_pretrained(
         self.model_name				,   quantization_config=lowercase_				,   device_map="auto"  )

     UpperCAmelCase_					:					Any			     =    self.tokenizer(self.input_text				,   return_tensors="pt"  )
     UpperCAmelCase_					:					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=lowercase_  )				,   self.EXPECTED_OUTPUTS  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     with self.assertRaises(lowercase_  ), tempfile.TemporaryDirectory() as tmpdirname:
        self.model_abit.save_pretrained(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    BitsAndBytesConfig()

     with self.assertRaises(lowercase_  ):
        UpperCAmelCase_					:					List[str]			     =    AutoModelForCausalLM.from_pretrained(
            self.model_name				,   quantization_config=lowercase_				,   load_in_abit=lowercase_				,   device_map="auto"				,   bnb_abit_quant_type="nf4"				,   )







  def        UpperCamelCase__					(		self  ):

     """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
     UpperCAmelCase_					:					Optional[int]			     =    self.tokenizer(self.input_text				,   return_tensors="pt"  )

     UpperCAmelCase_					:					Tuple			     =    self.model_fpaa.to(torch.floataa  )
     UpperCAmelCase_					:					Any			     =    self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0  )				,   max_new_tokens=10  )

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

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

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    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 A_	(unittest.TestCase      ):




  '''simple docstring'''







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    "t5-small"
     UpperCAmelCase_					:					Union[str, Any]			     =    "google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
     UpperCAmelCase_					:					Optional[Any]			     =    AutoTokenizer.from_pretrained(cls.model_name  )
     UpperCAmelCase_					:					List[Any]			     =    "Translate in German: Hello, my dog is cute"







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     gc.collect()
     torch.cuda.empty_cache()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     from transformers import TaForConditionalGeneration

     UpperCAmelCase_					:					Dict			     =    TaForConditionalGeneration._keep_in_fpaa_modules
     UpperCAmelCase_					:					int			     =    None

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

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     import bitsandbytes as bnb

     from transformers import TaForConditionalGeneration

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

     UpperCAmelCase_					:					Any			     =    self.tokenizer(self.input_text				,   return_tensors="pt"  ).to(0  )
     UpperCAmelCase_					:					Tuple			     =    model.generate(**lowercase_  )

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




class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()
     # model_name
     UpperCAmelCase_					:					List[Any]			     =    "bigscience/bloom-560m"
     UpperCAmelCase_					:					Optional[int]			     =    "t5-small"

     # Different types of model

     UpperCAmelCase_					:					str			     =    AutoModel.from_pretrained(self.model_name				,   load_in_abit=lowercase_				,   device_map="auto"  )
     # Sequence classification model
     UpperCAmelCase_					:					List[Any]			     =    AutoModelForSequenceClassification.from_pretrained(
         self.model_name				,   load_in_abit=lowercase_				,   device_map="auto"  )
     # CausalLM model
     UpperCAmelCase_					:					Optional[Any]			     =    AutoModelForCausalLM.from_pretrained(self.model_name				,   load_in_abit=lowercase_				,   device_map="auto"  )
     # Seq2seq model
     UpperCAmelCase_					:					Optional[Any]			     =    AutoModelForSeqaSeqLM.from_pretrained(
         self.seq_to_seq_name				,   load_in_abit=lowercase_				,   device_map="auto"  )







  def        UpperCamelCase__					(		self  ):

     """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        UpperCamelCase__					(		self  ):

     """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 A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     del self.pipe

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    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_					:					str			     =    self.pipe(self.input_text  )
     self.assertIn(pipeline_output[0]["generated_text"]				,   self.EXPECTED_OUTPUTS  )




@require_torch_multi_gpu
class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    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
     UpperCAmelCase_					:					List[Any]			     =    self.tokenizer(self.input_text				,   return_tensors="pt"  )

     # Second real batch
     UpperCAmelCase_					:					List[str]			     =    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 A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    "facebook/opt-350m"
     super().setUp()







  def        UpperCamelCase__					(		self  ):

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

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

        # Step 2: add adapters
     for _, module in model.named_modules():
        if "OPTAttention" in repr(type(lowercase_  )  ):
           UpperCAmelCase_					:					List[str]			     =    LoRALayer(module.q_proj				,   rank=16  )
           UpperCAmelCase_					:					Optional[Any]			     =    LoRALayer(module.k_proj				,   rank=16  )
           UpperCAmelCase_					:					List[Any]			     =    LoRALayer(module.v_proj				,   rank=16  )

        # Step 3: dummy batch
     UpperCAmelCase_					:					List[Any]			     =    self.tokenizer("Test batch "				,   return_tensors="pt"  ).to(0  )

     # Step 4: Check if the gradient is not None
     with torch.cuda.amp.autocast():
        UpperCAmelCase_					:					str			     =    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 A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		"""gpt2-xl"""
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7




"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


