


'''simple docstring'''







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   BioGptTokenizer
    a													=   False


    def        A   (  self      :							List[str]):
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           _A					:     List[str]     = [
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               'w</w>',
               'r</w>',
               't</w>',
               'lo',
               'low',
               'er</w>',
               'low</w>',
               'lowest</w>',
               'newer</w>',
               'wider</w>',
               '<unk>',
           ]
           _A					:     Any     = dict(zip(SCREAMING_SNAKE_CASE				,							range(len(SCREAMING_SNAKE_CASE))))
           _A					:     int     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

           _A					:     List[str]     = os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file'])
           _A					:     Optional[Any]     = os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['merges_file'])
           with open(self.vocab_file				,							'w') as fp:
                  fp.write(json.dumps(SCREAMING_SNAKE_CASE))
           with open(self.merges_file				,							'w') as fp:
                  fp.write('\n'.join(SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           _A					:     Any     = 'lower newer'
           _A					:     List[Any]     = 'lower newer'
           return input_text, output_text


    def        A   (  self      :							List[str]):
           _A					:     Union[str, Any]     = BioGptTokenizer(self.vocab_file				,							self.merges_file)

           _A					:     List[str]     = 'lower'
           _A					:     int     = ['low', 'er</w>']
           _A					:     Dict     = tokenizer.tokenize(SCREAMING_SNAKE_CASE)
           self.assertListEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           _A					:     int     = tokens + ['<unk>']
           _A					:     Optional[Any]     = [14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE)


    @slow
    def        A   (  self      :							str):
           _A					:     Dict     = BioGptTokenizer.from_pretrained('microsoft/biogpt')

           _A					:     str     = tokenizer.encode('sequence builders'				,							add_special_tokens=SCREAMING_SNAKE_CASE)
           _A					:     Any     = tokenizer.encode('multi-sequence build'				,							add_special_tokens=SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           self.assertTrue(encoded_sentence == [2] + text)
           self.assertTrue(encoded_pair == [2] + text + [2] + text_a)










'''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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       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      :							int				,							SCREAMING_SNAKE_CASE      :							nn.Module				,							SCREAMING_SNAKE_CASE      :							int):
              super().__init__()
              _A					:     Tuple     = module
              _A					:     str     = nn.Sequential(
                  nn.Linear(module.in_features				,							SCREAMING_SNAKE_CASE				,							bias=SCREAMING_SNAKE_CASE)				,							nn.Linear(SCREAMING_SNAKE_CASE				,							module.out_features				,							bias=SCREAMING_SNAKE_CASE)				,							)
              _A					:     Any     = (2.0 / (5 * min(module.in_features				,							module.out_features))) ** 0.5
              nn.init.normal_(self.adapter[0].weight				,							std=SCREAMING_SNAKE_CASE)
              nn.init.zeros_(self.adapter[1].weight)
              self.adapter.to(module.weight.device)


       def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Any				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							int):
              return self.module(SCREAMING_SNAKE_CASE				,							*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE) + self.adapter(SCREAMING_SNAKE_CASE)


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

    # Constant values
    a													=   2.109_6595_5269_2574

    a													=   "Hello my name is"
    a													=   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"	)
    a													=   10


    def        A   (  self      :							List[Any]):
           # Models and tokenizer
           _A					:     List[Any]     = AutoTokenizer.from_pretrained(self.model_name)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							List[str]):
           super().setUp()

           # Models and tokenizer
           _A					:     Tuple     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							torch_dtype=torch.floataa				,							device_map='auto')
           _A					:     str     = AutoModelForCausalLM.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')


    def        A   (  self      :							List[Any]):
           del self.model_fpaa
           del self.model_abit

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


    def        A   (  self      :							str):
           _A					:     Any     = self.model_abit.config

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

           _A					:     str     = config.to_dict()
           _A					:     Dict     = config.to_diff_dict()

           _A					:     int     = config.to_json_string()


    def        A   (  self      :							List[str]):
           from bitsandbytes.nn import Paramsabit

           _A					:     List[str]     = self.model_fpaa.get_memory_footprint()
           _A					:     List[Any]     = self.model_abit.get_memory_footprint()

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


    def        A   (  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(SCREAMING_SNAKE_CASE				,							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      :							Any):
           _A					:     List[str]     = self.tokenizer(self.input_text				,							return_tensors='pt')
           _A					:     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=SCREAMING_SNAKE_CASE)				,							self.EXPECTED_OUTPUTS)


    def        A   (  self      :							List[str]):
           _A					:     List[Any]     = BitsAndBytesConfig()
           _A					:     Optional[int]     = True

           _A					:     Union[str, Any]     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							quantization_config=SCREAMING_SNAKE_CASE				,							device_map='auto')

           _A					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt')
           _A					:     Any     = 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=SCREAMING_SNAKE_CASE)				,							self.EXPECTED_OUTPUTS)


    def        A   (  self      :							Union[str, Any]):
           with self.assertRaises(SCREAMING_SNAKE_CASE), tempfile.TemporaryDirectory() as tmpdirname:
                  self.model_abit.save_pretrained(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Any     = BitsAndBytesConfig()

           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  _A					:     Optional[Any]     = AutoModelForCausalLM.from_pretrained(
                      self.model_name				,							quantization_config=SCREAMING_SNAKE_CASE				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto'				,							bnb_abit_quant_type='nf4'				,							)


    def        A   (  self      :							Optional[Any]):
           with self.assertRaises(SCREAMING_SNAKE_CASE):
                  # Tries with `str`
                  self.model_abit.to('cpu')

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

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

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

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

           # Test if we did not break anything
           _A					:     str     = self.tokenizer(self.input_text				,							return_tensors='pt')

           _A					:     Optional[Any]     = self.model_fpaa.to(torch.floataa)
           _A					:     Optional[int]     = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0)				,							max_new_tokens=10)

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

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

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


    def        A   (  self      :							Dict):
           _A					:     Any     = AutoModelForSeqaSeqLM.from_pretrained('t5-small'				,							load_in_abit=SCREAMING_SNAKE_CASE				,							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        A   (  cls      :							Union[str, Any]):
           _A					:     Tuple     = 't5-small'
           _A					:     Union[str, Any]     = 'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
           _A					:     str     = AutoTokenizer.from_pretrained(cls.model_name)
           _A					:     Optional[int]     = 'Translate in German: Hello, my dog is cute'


    def        A   (  self      :							Any):
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							List[str]):
           from transformers import TaForConditionalGeneration

           _A					:     Union[str, Any]     = TaForConditionalGeneration._keep_in_fpaa_modules
           _A					:     List[str]     = None

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

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


    def        A   (  self      :							Dict):
           import bitsandbytes as bnb

           from transformers import TaForConditionalGeneration

           # test with `t5-small`
           _A					:     str     = TaForConditionalGeneration.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							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))

           _A					:     int     = self.tokenizer(self.input_text				,							return_tensors='pt').to(0)
           _A					:     Union[str, Any]     = model.generate(**SCREAMING_SNAKE_CASE)

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




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Union[str, Any]):
           super().setUp()
           # model_name
           _A					:     Dict     = 'bigscience/bloom-560m'
           _A					:     List[str]     = 't5-small'

           # Different types of model

           _A					:     Optional[Any]     = AutoModel.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           # Sequence classification model
           _A					:     Tuple     = AutoModelForSequenceClassification.from_pretrained(
               self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           # CausalLM model
           _A					:     List[Any]     = AutoModelForCausalLM.from_pretrained(self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')
           # Seq2seq model
           _A					:     Dict     = AutoModelForSeqaSeqLM.from_pretrained(
               self.seq_to_seq_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							device_map='auto')


    def        A   (  self      :							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        A   (  self      :							Any):
           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			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           super().setUp()


    def        A   (  self      :							Any):
           del self.pipe

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


    def        A   (  self      :							Optional[int]):
           _A					:     Dict     = 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
           _A					:     Union[str, Any]     = self.pipe(self.input_text)
           self.assertIn(pipeline_output[0]['generated_text']				,							self.EXPECTED_OUTPUTS)




@require_torch_multi_gpu
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							List[Any]):
           super().setUp()


    def        A   (  self      :							Any):
           _A					:     int     = AutoModelForCausalLM.from_pretrained(
               self.model_name				,							load_in_abit=SCREAMING_SNAKE_CASE				,							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
           _A					:     Tuple     = self.tokenizer(self.input_text				,							return_tensors='pt')

           # Second real batch
           _A					:     Union[str, 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=SCREAMING_SNAKE_CASE)				,							self.EXPECTED_OUTPUTS)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           _A					:     Any     = 'facebook/opt-350m'
           super().setUp()


    def        A   (  self      :							List[Any]):
           if version.parse(importlib.metadata.version('bitsandbytes')) < version.parse('0.37.0'):
                  return

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

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

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

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

        # Step 3: dummy batch
           _A					:     Union[str, Any]     = self.tokenizer('Test batch '				,							return_tensors='pt').to(0)

           # Step 4: Check if the gradient is not None
           with torch.cuda.amp.autocast():
                  _A					:     Union[str, Any]     = model.forward(**SCREAMING_SNAKE_CASE)
                  out.logits.norm().backward()

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




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "gpt2-xl"
    a													=   3.3191_8548_5415_2187






