





"""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       a__					(					_SCREAMING_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        _lowerCamelCase      (       nn.Module ):


												def __init__(self   ,							__a   ,							__a		) ->   Tuple:
														super().__init__()
														UpperCamelCase   =	module
														UpperCamelCase   =	nn.Sequential(
														    nn.Linear(module.in_features   ,							__a   ,							bias=__a		)   ,							nn.Linear(__a   ,							module.out_features   ,							bias=__a		)   ,							)
														UpperCamelCase   =	(2.0 / (5 * min(module.in_features   ,							module.out_features		))) ** 0.5
														nn.init.normal_(self.adapter[0].weight   ,							std=__a		)
														nn.init.zeros_(self.adapter[1].weight		)
														self.adapter.to(module.weight.device		)




												def     snake_case_   (self   ,							__a   ,							*__a   ,							**__a		) ->   int:
														return self.module(__a   ,							*__a   ,							**__a		) + self.adapter(__a		)





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

						# Constant values
						UpperCAmelCase_ =			2.109659552692574

						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     snake_case_   (self		) ->   str:
								# Models and tokenizer
								UpperCamelCase   =	AutoTokenizer.from_pretrained(self.model_name		)





class        _lowerCamelCase      (       _lowercase ):


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

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


						def     snake_case_   (self		) ->   str:
								del self.model_fpaa
								del self.model_abit

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


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.model_abit.config

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

								UpperCamelCase   =	config.to_dict()
								UpperCamelCase   =	config.to_diff_dict()

								UpperCamelCase   =	config.to_json_string()


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

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

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


						def     snake_case_   (self		) ->   Dict:
								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(__a   ,							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     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)
								UpperCamelCase   =	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=__a		)   ,							self.EXPECTED_OUTPUTS		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	BitsAndBytesConfig()
								UpperCamelCase   =	True

								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(
								    self.model_name   ,							quantization_config=__a   ,							device_map="auto"		)

								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)
								UpperCamelCase   =	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=__a		)   ,							self.EXPECTED_OUTPUTS		)


						def     snake_case_   (self		) ->   Dict:
								with self.assertRaises(__a		), tempfile.TemporaryDirectory() as tmpdirname:
										self.model_abit.save_pretrained(__a		)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	BitsAndBytesConfig()

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


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

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

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

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

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

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

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

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

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

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




						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("t5-small"   ,							load_in_abit=__a   ,							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 ):


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


						def     snake_case_   (self		) ->   Any:
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self		) ->   Optional[Any]:
								from transformers import TaForConditionalGeneration

								UpperCamelCase   =	TaForConditionalGeneration._keep_in_fpaa_modules
								UpperCamelCase   =	None

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

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




						def     snake_case_   (self		) ->   str:
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								UpperCamelCase   =	TaForConditionalGeneration.from_pretrained(self.model_name   ,							load_in_abit=__a   ,							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		)		)

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

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





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Optional[Any]:
								super().setUp()
								# model_name
								UpperCamelCase   =	"bigscience/bloom-560m"
								UpperCamelCase   =	"t5-small"

								# Different types of model

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


						def     snake_case_   (self		) ->   Tuple:
								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     snake_case_   (self		) ->   Optional[int]:
								from bitsandbytes.nn import Paramsabit

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

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





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   int:
								super().setUp()


						def     snake_case_   (self		) ->   Any:
								del self.pipe

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




						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	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
								UpperCamelCase   =	self.pipe(self.input_text		)
								self.assertIn(pipeline_output[0]["generated_text"]   ,							self.EXPECTED_OUTPUTS		)





@require_torch_multi_gpu
class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   List[Any]:
								super().setUp()




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(
								    self.model_name   ,							load_in_abit=__a   ,							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
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)

								# Second real batch
								UpperCamelCase   =	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=__a		)   ,							self.EXPECTED_OUTPUTS		)





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	"facebook/opt-350m"
								super().setUp()




						def     snake_case_   (self		) ->   List[Any]:
								if version.parse(importlib.metadata.version("bitsandbytes"		)		) < version.parse("0.37.0"		):
										return

								# Step 1: freeze all parameters
								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(self.model_name   ,							load_in_abit=__a		)

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

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

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

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

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
										UpperCamelCase   =	model.forward(**__a		)
										out.logits.norm().backward()

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





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"gpt2-xl"
						UpperCAmelCase_ =			3.3191854854152187






"""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       a__					(					_SCREAMING_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        _lowerCamelCase      (       nn.Module ):


												def __init__(self   ,							__a   ,							__a		) ->   Tuple:
														super().__init__()
														UpperCamelCase   =	module
														UpperCamelCase   =	nn.Sequential(
														    nn.Linear(module.in_features   ,							__a   ,							bias=__a		)   ,							nn.Linear(__a   ,							module.out_features   ,							bias=__a		)   ,							)
														UpperCamelCase   =	(2.0 / (5 * min(module.in_features   ,							module.out_features		))) ** 0.5
														nn.init.normal_(self.adapter[0].weight   ,							std=__a		)
														nn.init.zeros_(self.adapter[1].weight		)
														self.adapter.to(module.weight.device		)




												def     snake_case_   (self   ,							__a   ,							*__a   ,							**__a		) ->   int:
														return self.module(__a   ,							*__a   ,							**__a		) + self.adapter(__a		)





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

						# Constant values
						UpperCAmelCase_ =			2.109659552692574

						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     snake_case_   (self		) ->   str:
								# Models and tokenizer
								UpperCamelCase   =	AutoTokenizer.from_pretrained(self.model_name		)





class        _lowerCamelCase      (       _lowercase ):


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

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


						def     snake_case_   (self		) ->   str:
								del self.model_fpaa
								del self.model_abit

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


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.model_abit.config

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

								UpperCamelCase   =	config.to_dict()
								UpperCamelCase   =	config.to_diff_dict()

								UpperCamelCase   =	config.to_json_string()


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

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

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


						def     snake_case_   (self		) ->   Dict:
								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(__a   ,							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     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)
								UpperCamelCase   =	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=__a		)   ,							self.EXPECTED_OUTPUTS		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	BitsAndBytesConfig()
								UpperCamelCase   =	True

								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(
								    self.model_name   ,							quantization_config=__a   ,							device_map="auto"		)

								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)
								UpperCamelCase   =	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=__a		)   ,							self.EXPECTED_OUTPUTS		)


						def     snake_case_   (self		) ->   Dict:
								with self.assertRaises(__a		), tempfile.TemporaryDirectory() as tmpdirname:
										self.model_abit.save_pretrained(__a		)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	BitsAndBytesConfig()

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


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

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

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

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

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

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

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

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

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

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




						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("t5-small"   ,							load_in_abit=__a   ,							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 ):


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


						def     snake_case_   (self		) ->   Any:
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self		) ->   Optional[Any]:
								from transformers import TaForConditionalGeneration

								UpperCamelCase   =	TaForConditionalGeneration._keep_in_fpaa_modules
								UpperCamelCase   =	None

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

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




						def     snake_case_   (self		) ->   str:
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								UpperCamelCase   =	TaForConditionalGeneration.from_pretrained(self.model_name   ,							load_in_abit=__a   ,							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		)		)

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

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





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Optional[Any]:
								super().setUp()
								# model_name
								UpperCamelCase   =	"bigscience/bloom-560m"
								UpperCamelCase   =	"t5-small"

								# Different types of model

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


						def     snake_case_   (self		) ->   Tuple:
								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     snake_case_   (self		) ->   Optional[int]:
								from bitsandbytes.nn import Paramsabit

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

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





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   int:
								super().setUp()


						def     snake_case_   (self		) ->   Any:
								del self.pipe

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




						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	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
								UpperCamelCase   =	self.pipe(self.input_text		)
								self.assertIn(pipeline_output[0]["generated_text"]   ,							self.EXPECTED_OUTPUTS		)





@require_torch_multi_gpu
class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   List[Any]:
								super().setUp()




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(
								    self.model_name   ,							load_in_abit=__a   ,							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
								UpperCamelCase   =	self.tokenizer(self.input_text   ,							return_tensors="pt"		)

								# Second real batch
								UpperCamelCase   =	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=__a		)   ,							self.EXPECTED_OUTPUTS		)





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	"facebook/opt-350m"
								super().setUp()




						def     snake_case_   (self		) ->   List[Any]:
								if version.parse(importlib.metadata.version("bitsandbytes"		)		) < version.parse("0.37.0"		):
										return

								# Step 1: freeze all parameters
								UpperCamelCase   =	AutoModelForCausalLM.from_pretrained(self.model_name   ,							load_in_abit=__a		)

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

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

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

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

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
										UpperCamelCase   =	model.forward(**__a		)
										out.logits.norm().backward()

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





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"gpt2-xl"
						UpperCAmelCase_ =			3.3191854854152187






