

"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__UpperCamelCase  :      List[str]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      Any							      =		{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "marian"
					lowercase__				 =       ["past_key_values"]
					lowercase__				 =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]=5_8_1_0_1   ,lowercase_			:				Any=None   ,lowercase_			:				str=1_0_2_4   ,lowercase_			:				Union[str, Any]=1_2   ,lowercase_			:				List[Any]=4_0_9_6   ,lowercase_			:				List[str]=1_6   ,lowercase_			:				int=1_2   ,lowercase_			:				Dict=4_0_9_6   ,lowercase_			:				List[str]=1_6   ,lowercase_			:				Optional[Any]=0.0   ,lowercase_			:				Optional[int]=0.0   ,lowercase_			:				str=True   ,lowercase_			:				Tuple=True   ,lowercase_			:				List[str]="gelu"   ,lowercase_			:				List[str]=1_0_2_4   ,lowercase_			:				List[str]=0.1   ,lowercase_			:				List[Any]=0.0   ,lowercase_			:				List[str]=0.0   ,lowercase_			:				Tuple=0.02   ,lowercase_			:				Optional[Any]=5_8_1_0_0   ,lowercase_			:				List[Any]=False   ,lowercase_			:				Any=5_8_1_0_0   ,lowercase_			:				List[str]=0   ,lowercase_			:				Tuple=0   ,lowercase_			:				Tuple=True   ,**lowercase_			:				str   ,):
						lowerCAmelCase__	:						List[Any]  				=	vocab_size
						lowerCAmelCase__	:						str  				=	decoder_vocab_size or vocab_size
						lowerCAmelCase__	:						int  				=	max_position_embeddings
						lowerCAmelCase__	:						Optional[Any]  				=	d_model
						lowerCAmelCase__	:						Optional[Any]  				=	encoder_ffn_dim
						lowerCAmelCase__	:						Optional[int]  				=	encoder_layers
						lowerCAmelCase__	:						Optional[Any]  				=	encoder_attention_heads
						lowerCAmelCase__	:						Union[str, Any]  				=	decoder_ffn_dim
						lowerCAmelCase__	:						str  				=	decoder_layers
						lowerCAmelCase__	:						Dict  				=	decoder_attention_heads
						lowerCAmelCase__	:						Union[str, Any]  				=	dropout
						lowerCAmelCase__	:						Any  				=	attention_dropout
						lowerCAmelCase__	:						int  				=	activation_dropout
						lowerCAmelCase__	:						Any  				=	activation_function
						lowerCAmelCase__	:						Optional[int]  				=	init_std
						lowerCAmelCase__	:						int  				=	encoder_layerdrop
						lowerCAmelCase__	:						List[Any]  				=	decoder_layerdrop
						lowerCAmelCase__	:						Tuple  				=	use_cache
						lowerCAmelCase__	:						str  				=	encoder_layers
						lowerCAmelCase__	:						List[str]  				=	scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__	:						List[str]  				=	share_encoder_decoder_embeddings
						super().__init__(
						    pad_token_id=lowerCamelCase_   ,eos_token_id=lowerCamelCase_   ,is_encoder_decoder=lowerCamelCase_   ,decoder_start_token_id=lowerCamelCase_   ,forced_eos_token_id=lowerCamelCase_   ,**lowerCamelCase_   ,)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def        __lowerCAmelCase (       self			:				List[str]		):

						if self.task in ["default", "seq2seq-lm"]:
							lowerCAmelCase__	:						str  				=	OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
							    ]		)

							if self.use_past:
								lowerCAmelCase__	:						Optional[Any]  				=	{0: '''batch'''}
								lowerCAmelCase__	:						Tuple  				=	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							else:
								lowerCAmelCase__	:						int  				=	{0: '''batch''', 1: '''decoder_sequence'''}
								lowerCAmelCase__	:						Union[str, Any]  				=	{0: '''batch''', 1: '''decoder_sequence'''}

							if self.use_past:
								self.fill_with_past_key_values_(lowerCamelCase_   ,direction='''inputs'''		)
						elif self.task == "causal-lm":
							# TODO: figure this case out.
							lowerCAmelCase__	:						Dict  				=	OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
							    ]		)
							if self.use_past:
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	self.num_layers
								for i in range(lowerCamelCase_		):
									lowerCAmelCase__	:						Any  				=	{0: '''batch''', 2: '''past_sequence + sequence'''}
									lowerCAmelCase__	:						List[str]  				=	{0: '''batch''', 2: '''past_sequence + sequence'''}
						else:
							lowerCAmelCase__	:						Union[str, Any]  				=	OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
							        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
							        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
							    ]		)

						return common_inputs







					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def        __lowerCAmelCase (       self			:				Tuple		):

						if self.task in ["default", "seq2seq-lm"]:
							lowerCAmelCase__	:						Optional[int]  				=	super().outputs
						else:
							lowerCAmelCase__	:						Optional[int]  				=	super(lowerCamelCase_   ,self		).outputs
							if self.use_past:
								lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	self.num_layers
								for i in range(lowerCamelCase_		):
									lowerCAmelCase__	:						Union[str, Any]  				=	{0: '''batch''', 2: '''past_sequence + sequence'''}
									lowerCAmelCase__	:						Optional[Any]  				=	{0: '''batch''', 2: '''past_sequence + sequence'''}
						return common_outputs







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Tuple   ,lowercase_			:				Tuple = -1   ,lowercase_			:				Optional[int] = -1   ,lowercase_			:				Union[str, Any] = False   ,lowercase_			:				Optional[int] = None   ,):
						lowerCAmelCase__	:						Optional[Any]  				=	self._generate_dummy_inputs_for_encoder_and_decoder(
						    lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_		)

						# Generate decoder inputs
						lowerCAmelCase__	:						Optional[int]  				=	seq_length if not self.use_past else 1
						lowerCAmelCase__	:						Union[str, Any]  				=	self._generate_dummy_inputs_for_encoder_and_decoder(
						    lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_		)
						lowerCAmelCase__	:						Optional[int]  				=	{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
						lowerCAmelCase__	:						Optional[Any]  				=	dict(**lowerCamelCase_   ,**lowerCamelCase_		)

						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
							else:
								import torch
							lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	common_inputs['''input_ids'''].shape
							lowerCAmelCase__	:						str  				=	common_inputs['''decoder_input_ids'''].shape[1]
							lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.num_attention_heads
							lowerCAmelCase__	:						Dict  				=	(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							lowerCAmelCase__	:						Optional[Any]  				=	decoder_seq_length + 3
							lowerCAmelCase__	:						str  				=	(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							lowerCAmelCase__	:						List[str]  				=	torch.cat(
							    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_   ,lowerCamelCase_		)]   ,dim=1		)

							lowerCAmelCase__	:						List[Any]  				=	[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.num_layers
							lowerCAmelCase__	:						List[str]  				=	min(lowerCamelCase_   ,lowerCamelCase_		)
							lowerCAmelCase__	:						int  				=	max(lowerCamelCase_   ,lowerCamelCase_		) - min_num_layers
							lowerCAmelCase__	:						Optional[Any]  				=	'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

							for _ in range(lowerCamelCase_		):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(lowerCamelCase_		),
								        torch.zeros(lowerCamelCase_		),
								        torch.zeros(lowerCamelCase_		),
								        torch.zeros(lowerCamelCase_		),
								    )		)
							# TODO: test this.
							lowerCAmelCase__	:						int  				=	encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
							for _ in range(lowerCamelCase_   ,lowerCamelCase_		):
								common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_		), torch.zeros(lowerCamelCase_		))		)
						return common_inputs







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Dict   ,lowercase_			:				List[str] = -1   ,lowercase_			:				List[Any] = -1   ,lowercase_			:				List[str] = False   ,lowercase_			:				Optional[Any] = None   ,):
						lowerCAmelCase__	:						List[Any]  				=	self._generate_dummy_inputs_for_encoder_and_decoder(
						    lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_		)

						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
							else:
								import torch
							lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	common_inputs['''input_ids'''].shape
							# Not using the same length for past_key_values
							lowerCAmelCase__	:						Optional[Any]  				=	seqlen + 2
							lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.num_layers
							lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	self.num_attention_heads
							lowerCAmelCase__	:						Optional[Any]  				=	(
							    batch,
							    num_encoder_attention_heads,
							    past_key_values_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)

							lowerCAmelCase__	:						Dict  				=	common_inputs['''attention_mask'''].dtype
							lowerCAmelCase__	:						Dict  				=	torch.cat(
							    [common_inputs['''attention_mask'''], torch.ones(lowerCamelCase_   ,lowerCamelCase_   ,dtype=lowerCamelCase_		)]   ,dim=1		)
							lowerCAmelCase__	:						Any  				=	[
							    (torch.zeros(lowerCamelCase_		), torch.zeros(lowerCamelCase_		)) for _ in range(lowerCamelCase_		)
							]
						return common_inputs







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any] = -1   ,lowercase_			:				Any = -1   ,lowercase_			:				int = False   ,lowercase_			:				List[Any] = None   ,):
						lowerCAmelCase__	:						int  				=	compute_effective_axis_dimension(
						    lowerCamelCase_   ,fixed_dimension=OnnxConfig.default_fixed_batch   ,num_token_to_add=0		)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						lowerCAmelCase__	:						Union[str, Any]  				=	tokenizer.num_special_tokens_to_add(lowerCamelCase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	compute_effective_axis_dimension(
						    lowerCamelCase_   ,fixed_dimension=OnnxConfig.default_fixed_sequence   ,num_token_to_add=lowerCamelCase_		)

						# Generate dummy inputs according to compute batch and sequence
						lowerCAmelCase__	:						List[str]  				=	[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
						lowerCAmelCase__	:						List[str]  				=	dict(tokenizer(lowerCamelCase_   ,return_tensors=lowerCamelCase_		)		)
						return common_inputs







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Dict   ,lowercase_			:				int = -1   ,lowercase_			:				Dict = -1   ,lowercase_			:				List[Any] = False   ,lowercase_			:				List[Any] = None   ,):

						if self.task in ["default", "seq2seq-lm"]:
							lowerCAmelCase__	:						Optional[Any]  				=	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
							    lowerCamelCase_   ,batch_size=lowerCamelCase_   ,seq_length=lowerCamelCase_   ,is_pair=lowerCamelCase_   ,framework=lowerCamelCase_		)

						else:
							lowerCAmelCase__	:						Dict  				=	self._generate_dummy_inputs_for_causal_lm(
							    lowerCamelCase_   ,batch_size=lowerCamelCase_   ,seq_length=lowerCamelCase_   ,is_pair=lowerCamelCase_   ,framework=lowerCamelCase_		)

						return common_inputs







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Tuple   ,lowercase_			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):

						if self.task in ["default", "seq2seq-lm"]:
							lowerCAmelCase__	:						Dict  				=	super()._flatten_past_key_values_(lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_		)
						else:
							lowerCAmelCase__	:						Union[str, Any]  				=	super(lowerCamelCase_   ,self		)._flatten_past_key_values_(
							    lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_		)







					@property
					def        __lowerCAmelCase (       self			:				Dict		):
						return 1E-4









import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)






def     a(      A       :    List[str]       )		->       List[str]:
					"""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 	_lowercase							(   nn.Module	):





					"""simple docstring"""




					def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
										"""simple docstring"""

										super().__init__()
										a					     =   module
										a					     =   nn.Sequential(
										    nn.Linear(module.in_features		,       lowerCamelCase_		,       bias=lowerCamelCase_		)		,       nn.Linear(lowerCamelCase_		,       module.out_features		,       bias=lowerCamelCase_		)		,       )
										a					     =   (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_		):
										"""simple docstring"""

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



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	_lowercase							(   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_659_552_692_574

	__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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   AutoTokenizer.from_pretrained(self.model_name		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# Models and tokenizer
						a					     =   AutoModelForCausalLM.from_pretrained(
						    self.model_name		,       torch_dtype=torch.floataa		,       device_map="auto"		)
						a					     =   AutoModelForCausalLM.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						del self.model_fpaa
						del self.model_abit

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_abit.config

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

						a					     =   config.to_dict()
						a					     =   config.to_diff_dict()

						a					     =   config.to_json_string()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from bitsandbytes.nn import Paramsabit

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import TaPreTrainedModel

						self.model_fpaa.get_memory_footprint()
						self.model_abit.get_memory_footprint()

						for name, module in self.model_abit.named_modules():
											if isinstance(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		):
						"""simple docstring"""

						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)
						a					     =   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=lowerCamelCase_		)		,       self.EXPECTED_OUTPUTS		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   BitsAndBytesConfig()
						a					     =   True

						a					     =   AutoModelForCausalLM.from_pretrained(
						    self.model_name		,       quantization_config=lowerCamelCase_		,       device_map="auto"		)

						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)
						a					     =   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=lowerCamelCase_		)		,       self.EXPECTED_OUTPUTS		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   BitsAndBytesConfig()

						with self.assertRaises(lowerCamelCase_		):
											a					     =   AutoModelForCausalLM.from_pretrained(
											    self.model_name		,       quantization_config=lowerCamelCase_		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		,       bnb_abit_quant_type="nf4"		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						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
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)

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

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

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@classmethod
	def      UpperCamelCase_       (cls		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from transformers import TaForConditionalGeneration

						a					     =   TaForConditionalGeneration._keep_in_fpaa_modules
						a					     =   None

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

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

						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		).to(0		)
						a					     =   model.generate(**lowerCamelCase_		)

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




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						# Different types of model

						a					     =   AutoModel.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						# Sequence classification model
						a					     =   AutoModelForSequenceClassification.from_pretrained(
						    self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						# CausalLM model
						a					     =   AutoModelForCausalLM.from_pretrained(self.model_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)
						# Seq2seq model
						a					     =   AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name		,       load_in_abit=lowerCamelCase_		,       device_map="auto"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						del self.base_model
						del self.sequence_model
						del self.model_abit
						del self.seq_to_seq_model

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						from bitsandbytes.nn import Paramsabit

						self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit		)

						# Other heads should be nn.Parameter
						self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter		)
						self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter		)
						self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						del self.pipe

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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					     =   self.pipe(self.input_text		)
						self.assertIn(pipeline_output[0]["generated_text"]		,       self.EXPECTED_OUTPUTS		)




@require_torch_multi_gpu
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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
						a					     =   self.tokenizer(self.input_text		,       return_tensors="pt"		)

						# Second real batch
						a					     =   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=lowerCamelCase_		)		,       self.EXPECTED_OUTPUTS		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "facebook/opt-350m"
						super().setUp()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

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

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

        # Step 3: dummy batch
						a					     =   self.tokenizer("Test batch "		,       return_tensors="pt"		).to(0		)

						# Step 4: Check if the gradient is not None
						with torch.cuda.amp.autocast():
											a					     =   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 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"gpt2-xl"
	__A							=							3.3_191_854_854_152_187
