





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     lowerCamelCase__			(__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 			lowerCAmelCase__( nn.Module    ):






              '''simple docstring'''


              def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Any:
                   super().__init__()
                   _SCREAMING_SNAKE_CASE       :	Dict						    =							module
                   _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							nn.Sequential(
                       nn.Linear(module.in_features    ,						__lowerCamelCase    ,						bias=__lowerCamelCase							)    ,						nn.Linear(__lowerCamelCase    ,						module.out_features    ,						bias=__lowerCamelCase							)    ,						)
                   _SCREAMING_SNAKE_CASE       :	List[Any]						    =							(2.0 / (5 * min(module.in_features    ,						module.out_features							))) ** 0.5
                   nn.init.normal_(self.adapter[0].weight    ,						std=__lowerCamelCase							)
                   nn.init.zeros_(self.adapter[1].weight							)
                   self.adapter.to(module.weight.device							)







              def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     Union[str, Any]:
                   return self.module(__lowerCamelCase    ,						*__lowerCamelCase    ,						**__lowerCamelCase							) + self.adapter(__lowerCamelCase							)





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






       '''simple docstring'''


       __snake_case 			=    'bigscience/bloom-1b7'

       # Constant values
       __snake_case 			=    2.109_6595_5269_2574

       __snake_case 			=    'Hello my name is'
       __snake_case 			=    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'    )
       __snake_case 			=    1_0




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            # Models and tokenizer
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							AutoTokenizer.from_pretrained(self.model_name							)





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            super().setUp()

            # Models and tokenizer
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							AutoModelForCausalLM.from_pretrained(
                self.model_name    ,						torch_dtype=torch.floataa    ,						device_map="auto"							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=__lowerCamelCase    ,						device_map="auto"							)




       def     UpperCamelCase_			(      self							)			->     int:
            del self.model_fpaa
            del self.model_abit

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




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.model_abit.config

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

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							config.to_dict()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							config.to_diff_dict()

            _SCREAMING_SNAKE_CASE       :	int						    =							config.to_json_string()




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            from bitsandbytes.nn import Paramsabit

            _SCREAMING_SNAKE_CASE       :	Any						    =							self.model_fpaa.get_memory_footprint()
            _SCREAMING_SNAKE_CASE       :	int						    =							self.model_abit.get_memory_footprint()

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




       def     UpperCamelCase_			(      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(__lowerCamelCase    ,						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							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.tokenizer(self.input_text    ,						return_tensors="pt"							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0							)    ,						max_new_tokens=1_0							)

            self.assertIn(self.tokenizer.decode(output_sequences[0]    ,						skip_special_tokens=__lowerCamelCase							)    ,						self.EXPECTED_OUTPUTS							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BitsAndBytesConfig()
            _SCREAMING_SNAKE_CASE       :	Any						    =							True

            _SCREAMING_SNAKE_CASE       :	str						    =							AutoModelForCausalLM.from_pretrained(
                self.model_name    ,						quantization_config=__lowerCamelCase    ,						device_map="auto"							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.tokenizer(self.input_text    ,						return_tensors="pt"							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model_abit_from_config.generate(
                input_ids=encoded_input["input_ids"].to(0							)    ,						max_new_tokens=1_0							)

            self.assertIn(self.tokenizer.decode(output_sequences[0]    ,						skip_special_tokens=__lowerCamelCase							)    ,						self.EXPECTED_OUTPUTS							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            with self.assertRaises(__lowerCamelCase							), tempfile.TemporaryDirectory() as tmpdirname:
                 self.model_abit.save_pretrained(__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BitsAndBytesConfig()

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




       def     UpperCamelCase_			(      self							)			->     Any:
            with self.assertRaises(__lowerCamelCase							):
                 # Tries with `str`
                 self.model_abit.to("cpu"							)

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

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

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

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

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

            _SCREAMING_SNAKE_CASE       :	Any						    =							self.model_fpaa.to(torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	str						    =							self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0							)    ,						max_new_tokens=1_0							)

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

            # Check this does not throw an error
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.model_fpaa.half()

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







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							AutoModelForSeqaSeqLM.from_pretrained("t5-small"    ,						load_in_abit=__lowerCamelCase    ,						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 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


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




       def     UpperCamelCase_			(      self							)			->     str:
            gc.collect()
            torch.cuda.empty_cache()




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            from transformers import TaForConditionalGeneration

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							TaForConditionalGeneration._keep_in_fpaa_modules
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							None

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

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







       def     UpperCamelCase_			(      self							)			->     Any:
            import bitsandbytes as bnb

            from transformers import TaForConditionalGeneration

            # test with `t5-small`
            _SCREAMING_SNAKE_CASE       :	int						    =							TaForConditionalGeneration.from_pretrained(self.model_name    ,						load_in_abit=__lowerCamelCase    ,						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       :	Tuple						    =							self.tokenizer(self.input_text    ,						return_tensors="pt"							).to(0							)
            _SCREAMING_SNAKE_CASE       :	int						    =							model.generate(**__lowerCamelCase							)

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





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     str:
            super().setUp()
            # model_name
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							"bigscience/bloom-560m"
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							"t5-small"

            # Different types of model

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




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            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							)			->     Dict:
            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 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     str:
            super().setUp()




       def     UpperCamelCase_			(      self							)			->     Dict:
            del self.pipe

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







       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Optional[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
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.pipe(self.input_text							)
            self.assertIn(pipeline_output[0]["generated_text"]    ,						self.EXPECTED_OUTPUTS							)





@require_torch_multi_gpu
class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Any:
            super().setUp()







       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	int						    =							AutoModelForCausalLM.from_pretrained(
                self.model_name    ,						load_in_abit=__lowerCamelCase    ,						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       :	int						    =							self.tokenizer(self.input_text    ,						return_tensors="pt"							)

            # Second real batch
            _SCREAMING_SNAKE_CASE       :	str						    =							model_parallel.generate(input_ids=encoded_input["input_ids"].to(0							)    ,						max_new_tokens=1_0							)
            self.assertIn(self.tokenizer.decode(output_parallel[0]    ,						skip_special_tokens=__lowerCamelCase							)    ,						self.EXPECTED_OUTPUTS							)





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"facebook/opt-350m"
            super().setUp()







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

            # Step 1: freeze all parameters
            _SCREAMING_SNAKE_CASE       :	Any						    =							AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=__lowerCamelCase							)

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

            for param in model.parameters():
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							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       :	str						    =							param.data.to(torch.floataa							)

        # Step 2: add adapters
            for _, module in model.named_modules():
                 if "OPTAttention" in repr(type(__lowerCamelCase							)							):
                      _SCREAMING_SNAKE_CASE       :	str						    =							LoRALayer(module.q_proj    ,						rank=1_6							)
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							LoRALayer(module.k_proj    ,						rank=1_6							)
                      _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							LoRALayer(module.v_proj    ,						rank=1_6							)

        # Step 3: dummy batch
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							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       :	Union[str, Any]						    =							model.forward(**__lowerCamelCase							)
                 out.logits.norm().backward()

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





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'gpt2-xl'
       __snake_case 			=    3.3191_8548_5415_2187






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)