






"""simple docstring"""



import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )




class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def __init__(		self      :	List[str]							,			snake_case__      :	str							,			snake_case__      :	int=1_3							,			snake_case__      :	List[Any]=7							,			snake_case__      :	Union[str, Any]=True							,			snake_case__      :	Tuple=True							,			snake_case__      :	List[str]=True							,			snake_case__      :	int=True							,			snake_case__      :	Union[str, Any]=9_9							,			snake_case__      :	Dict=3_2							,			snake_case__      :	Dict=5							,			snake_case__      :	int=4							,			snake_case__      :	List[Any]=3_7							,			snake_case__      :	Union[str, Any]="gelu"							,			snake_case__      :	str=0.1							,			snake_case__      :	Any=0.1							,			snake_case__      :	str=5_1_2							,			snake_case__      :	Optional[Any]=1_6							,			snake_case__      :	Dict=2							,			snake_case__      :	int=0.02							,			snake_case__      :	str=4							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							parent
        UpperCAmelCase__						:    str							=							batch_size
        UpperCAmelCase__						:    Union[str, Any]							=							seq_length
        UpperCAmelCase__						:    int							=							is_training
        UpperCAmelCase__						:    str							=							use_attention_mask
        UpperCAmelCase__						:    str							=							use_token_type_ids
        UpperCAmelCase__						:    str							=							use_labels
        UpperCAmelCase__						:    Union[str, Any]							=							vocab_size
        UpperCAmelCase__						:    List[Any]							=							hidden_size
        UpperCAmelCase__						:    Optional[int]							=							num_hidden_layers
        UpperCAmelCase__						:    Union[str, Any]							=							num_attention_heads
        UpperCAmelCase__						:    List[str]							=							intermediate_size
        UpperCAmelCase__						:    Any							=							hidden_act
        UpperCAmelCase__						:    Optional[Any]							=							hidden_dropout_prob
        UpperCAmelCase__						:    Optional[int]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Union[str, Any]							=							max_position_embeddings
        UpperCAmelCase__						:    List[str]							=							type_vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							type_sequence_label_size
        UpperCAmelCase__						:    Union[str, Any]							=							initializer_range
        UpperCAmelCase__						:    str							=							num_choices
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size							)

        UpperCAmelCase__						:    Optional[int]							=							None
        if self.use_attention_mask:
             UpperCAmelCase__						:    Union[str, Any]							=							random_attention_mask([self.batch_size, self.seq_length]							)

        UpperCAmelCase__						:    Optional[Any]							=							None
        if self.use_token_type_ids:
             UpperCAmelCase__						:    Tuple							=							ids_tensor([self.batch_size, self.seq_length]							,			self.type_vocab_size							)

        UpperCAmelCase__						:    Tuple							=							AlbertConfig(
            vocab_size=self.vocab_size							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			type_vocab_size=self.type_vocab_size							,			is_decoder=snake_case__							,			initializer_range=self.initializer_range							,			)

        return config, input_ids, token_type_ids, attention_mask




   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.prepare_config_and_inputs()
        UpperCAmelCase__						:    List[str]							=							config_and_inputs
        UpperCAmelCase__						:    List[str]							=							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict




@require_flax
class 			lowerCAmelCase__			(    __snake_case							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							FlaxAlbertModelTester(self							)




   @slow
   def       __a							(		self      :	int							):


        '''simple docstring'''






        for model_class_name in self.all_model_classes:
             UpperCAmelCase__						:    List[Any]							=							model_class_name.from_pretrained("albert-base-v2"							)
             UpperCAmelCase__						:    List[Any]							=							model(np.ones((1, 1)							)							)
             self.assertIsNotNone(snake_case__							)




@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							FlaxAlbertModel.from_pretrained("albert-base-v2"							)
        UpperCAmelCase__						:    Tuple							=							np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
        UpperCAmelCase__						:    Optional[int]							=							np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
        UpperCAmelCase__						:    str							=							model(snake_case__							,			attention_mask=snake_case__							)[0]
        UpperCAmelCase__						:    Dict							=							(1, 1_1, 7_6_8)
        self.assertEqual(output.shape							,			snake_case__							)
        UpperCAmelCase__						:    List[str]							=							np.array(
            [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]							)

        self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,			snake_case__							,			atol=1e-4							)							)




'''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_							(  snake_case__		):




    '''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 					A							(				nn.Module		):





           def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






               """simple docstring"""






               super().__init__()
               A			:  List[Any]   =		module
               A			:  Any   =		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			:  List[str]   =		(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 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     *SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )					->							str:






               """simple docstring"""






               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 					A							(				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
     __magic_name__          =						'''bigscience/bloom-1b7'''

     # Constant values
     __magic_name__          =						2.1_09_65_95_52_69_25_74

     __magic_name__          =						'''Hello my name is'''
     __magic_name__          =						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'''		)
     __magic_name__          =						10





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Any   =		AutoTokenizer.from_pretrained(self.model_name      )





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         super().setUp()

         # Models and tokenizer
         A			:  Optional[Any]   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     torch_dtype=torch.floataa		,     device_map='''auto'''      )
         A			:  List[Any]   =		AutoModelForCausalLM.from_pretrained(self.model_name		,     load_in_abit=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.model_fpaa
         del self.model_abit

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





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Any   =		self.model_abit.config

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

         A			:  List[str]   =		config.to_dict()
         A			:  List[Any]   =		config.to_diff_dict()

         A			:  Union[str, Any]   =		config.to_json_string()





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         from bitsandbytes.nn import Paramsabit

         A			:  Optional[Any]   =		self.model_fpaa.get_memory_footprint()
         A			:  Any   =		self.model_abit.get_memory_footprint()

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





     def 							__lowerCAmelCase				(				self      )					->							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(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 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  List[str]   =		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 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  str   =		BitsAndBytesConfig()
         A			:  Optional[Any]   =		True

         A			:  int   =		AutoModelForCausalLM.from_pretrained(
             self.model_name		,     quantization_config=SCREAMING_SNAKE_CASE		,     device_map='''auto'''      )

         A			:  Union[str, Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )
         A			:  Tuple   =		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 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		BitsAndBytesConfig()

         with self.assertRaises(SCREAMING_SNAKE_CASE      ):
             A			:  int   =		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 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         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			:  int   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         A			:  Dict   =		self.model_fpaa.to(torch.floataa      )
         A			:  Union[str, Any]   =		self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0      )		,     max_new_tokens=10      )

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

         # Check this does not throw an error
         A			:  List[str]   =		self.model_fpaa.half()

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





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Tuple   =		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 					A							(				unittest.TestCase		):





     @classmethod
     def 							__lowerCAmelCase				(				cls      )					->							Union[str, Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         from transformers import TaForConditionalGeneration

         A			:  int   =		TaForConditionalGeneration._keep_in_fpaa_modules
         A			:  Optional[Any]   =		None

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

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





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         A			:  List[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			:  List[Any]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      ).to(0      )
         A			:  Any   =		model.generate(**SCREAMING_SNAKE_CASE      )

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





class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()
         # model_name
         A			:  Tuple   =		'''bigscience/bloom-560m'''
         A			:  int   =		'''t5-small'''

         # Different types of model

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





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """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      )					->							List[str]:






         """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							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         del self.pipe

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





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






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





@require_torch_multi_gpu
class 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		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			:  Optional[int]   =		self.tokenizer(self.input_text		,     return_tensors='''pt'''      )

         # Second real batch
         A			:  Optional[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 					A							(				__snake_case		):





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  int   =		'''facebook/opt-350m'''
         super().setUp()





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






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

         # Step 1: freeze all parameters
         A			:  Tuple   =		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[int]   =		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			:  Tuple   =		LoRALayer(module.q_proj		,     rank=16      )
                 A			:  Tuple   =		LoRALayer(module.k_proj		,     rank=16      )
                 A			:  str   =		LoRALayer(module.v_proj		,     rank=16      )

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

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
             A			:  str   =		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 					A							(				__snake_case		):
     __magic_name__          =						'''gpt2-xl'''
     __magic_name__          =						3.31_91_85_48_54_15_21_87




