


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(   a__):
				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					:      List[str]				,    _a					:      nn.Module				,    _a					:      int  )							->     int:


																	"""simple docstring"""





																	super().__init__()
																	_SCREAMING_SNAKE_CASE			  =module
																	_SCREAMING_SNAKE_CASE			  =nn.Sequential(
																	    nn.Linear(module.in_features				,    UpperCamelCase_				,    bias=UpperCamelCase_  )				,    nn.Linear(UpperCamelCase_				,    module.out_features				,    bias=UpperCamelCase_  )				,    )
																	_SCREAMING_SNAKE_CASE			  =(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 							__UpperCamelCase				(       self					:      Any				,    _a					:      List[Any]				,    *_a					:      Dict				,    **_a					:      Optional[int]  )							->     List[str]:


																	"""simple docstring"""





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






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						A__							(		unittest.TestCase  ):
						UpperCAmelCase             =						"bigscience/bloom-1b7"

						# Constant values
						UpperCAmelCase             =						2.1_09_65_95_52_69_25_74

						UpperCAmelCase             =						"Hello my name is"
						UpperCAmelCase             =						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             =						10




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =AutoTokenizer.from_pretrained(self.model_name  )






class 						A__							(		UpperCamelCase__  ):




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Optional[int]:


										"""simple docstring"""





										super().setUp()

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




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


										"""simple docstring"""





										del self.model_fpaa
										del self.model_abit

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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.model_abit.config

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

										_SCREAMING_SNAKE_CASE			  =config.to_dict()
										_SCREAMING_SNAKE_CASE			  =config.to_diff_dict()

										_SCREAMING_SNAKE_CASE			  =config.to_json_string()




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										from bitsandbytes.nn import Paramsabit

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

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Tuple:


										"""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 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer(self.input_text				,    return_tensors='''pt'''  )
										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitsAndBytesConfig()
										_SCREAMING_SNAKE_CASE			  =True

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

										_SCREAMING_SNAKE_CASE			  =self.tokenizer(self.input_text				,    return_tensors='''pt'''  )
										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Dict  )							->     int:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =BitsAndBytesConfig()

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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Optional[Any]:


										"""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
										_SCREAMING_SNAKE_CASE			  =self.tokenizer(self.input_text				,    return_tensors='''pt'''  )

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

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

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

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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =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 						A__							(		unittest.TestCase  ):




						@classmethod
						def 							__UpperCamelCase				(       cls					:      Tuple  )							->     int:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Any:


										"""simple docstring"""





										from transformers import TaForConditionalGeneration

										_SCREAMING_SNAKE_CASE			  =TaForConditionalGeneration._keep_in_fpaa_modules
										_SCREAMING_SNAKE_CASE			  =None

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

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




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Tuple:


										"""simple docstring"""





										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

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

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

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






class 						A__							(		UpperCamelCase__  ):




						def 							__UpperCamelCase				(       self					:      int  )							->     int:


										"""simple docstring"""





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

										# Different types of model

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




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Union[str, 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 							__UpperCamelCase				(       self					:      Dict  )							->     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 						A__							(		UpperCamelCase__  ):




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Dict:


										"""simple docstring"""





										super().setUp()




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Optional[int]:


										"""simple docstring"""





										del self.pipe

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




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


										"""simple docstring"""





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






@require_torch_multi_gpu
class 						A__							(		UpperCamelCase__  ):




						def 							__UpperCamelCase				(       self					:      int  )							->     List[Any]:


										"""simple docstring"""





										super().setUp()




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


										"""simple docstring"""





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

										# Second real batch
										_SCREAMING_SNAKE_CASE			  =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 						A__							(		UpperCamelCase__  ):




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


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Tuple:


										"""simple docstring"""





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

										# Step 1: freeze all parameters
										_SCREAMING_SNAKE_CASE			  =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():
														_SCREAMING_SNAKE_CASE			  =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			  =param.data.to(torch.floataa  )

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

        # Step 3: dummy batch
										_SCREAMING_SNAKE_CASE			  =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			  =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 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"gpt2-xl"
						UpperCAmelCase             =						3.31_91_85_48_54_15_21_87



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )