




'''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 __UpperCAmelCase	(   _UpperCAmelCase		:   Any						)      ->      Union[str, Any]:
			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 		SCREAMING_SNAKE_CASE__  (			nn.Module	):





									def __init__(      self						:    Dict      ,		a_						:    nn.Module      ,		a_						:    int							):



												"""simple docstring"""




												super().__init__()
												__snake_case			=		module
												__snake_case			=		nn.Sequential(
												    nn.Linear(module.in_features      ,		a_      ,		bias=a_							)      ,		nn.Linear(a_      ,		module.out_features      ,		bias=a_							)      ,		)
												__snake_case			=		(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 							A (      self						:    str      ,		a_						:    List[Any]      ,		*a_						:    int      ,		**a_						:    int							):



												"""simple docstring"""




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



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

			# Constant values
			__SCREAMING_SNAKE_CASE    =			2.109_6595_5269_2574

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





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		AutoTokenizer.from_pretrained(self.model_name							)







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						super().setUp()

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





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						del self.model_fpaa
						del self.model_abit

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





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		self.model_abit.config

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

						__snake_case			=		config.to_dict()
						__snake_case			=		config.to_diff_dict()

						__snake_case			=		config.to_json_string()





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						from bitsandbytes.nn import Paramsabit

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

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





			def 							A (      self						:    int							):



						"""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(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 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		self.tokenizer(self.input_text      ,		return_tensors="pt"							)
						__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=a_							)      ,		self.EXPECTED_OUTPUTS							)





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		BitsAndBytesConfig()
						__snake_case			=		True

						__snake_case			=		AutoModelForCausalLM.from_pretrained(
						    self.model_name      ,		quantization_config=a_      ,		device_map="auto"							)

						__snake_case			=		self.tokenizer(self.input_text      ,		return_tensors="pt"							)
						__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=a_							)      ,		self.EXPECTED_OUTPUTS							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




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





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		BitsAndBytesConfig()

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





			def 							A (      self						:    int							):



						"""simple docstring"""




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

						__snake_case			=		self.model_fpaa.to(torch.floataa							)
						__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
						__snake_case			=		self.model_fpaa.to("cpu"							)

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

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






			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		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 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			@classmethod
			def 							A (      cls						:    Optional[int]							):



						"""simple docstring"""




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





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




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





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						from transformers import TaForConditionalGeneration

						__snake_case			=		TaForConditionalGeneration._keep_in_fpaa_modules
						__snake_case			=		None

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

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






			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

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

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

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







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




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

						# Different types of model

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





			def 							A (      self						:    Dict							):



						"""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 							A (      self						:    int							):



						"""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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def 							A (      self						:    str							):



						"""simple docstring"""




						super().setUp()





			def 							A (      self						:    str							):



						"""simple docstring"""




						del self.pipe

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






			def 							A (      self						:    Tuple							):



						"""simple docstring"""




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







@require_torch_multi_gpu
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def 							A (      self						:    Any							):



						"""simple docstring"""




						super().setUp()






			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




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

						# Second real batch
						__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=a_							)      ,		self.EXPECTED_OUTPUTS							)







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def 							A (      self						:    int							):



						"""simple docstring"""




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






			def 							A (      self						:    Dict							):



						"""simple docstring"""




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

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

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

        # Step 3: dummy batch
						__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():
									__snake_case			=		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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			"""gpt2-xl"""
			__SCREAMING_SNAKE_CASE    =			3.3191_8548_5415_2187









import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	OmegaConf.load(lowercase_      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase_      )      )      )
						return config




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







						if conf_path is None:
												__UpperCAmelCase			:							Optional[int]	    =	'''./model_checkpoints/vqgan_only.yaml'''
						__UpperCAmelCase			:							Dict	    =	load_config(lowercase_      ,       display=lowercase_      )
						__UpperCAmelCase			:							Tuple	    =	VQModel(**config.model.params      )
						if ckpt_path is None:
												__UpperCAmelCase			:							Dict	    =	'''./model_checkpoints/vqgan_only.pt'''
						__UpperCAmelCase			:							int	    =	torch.load(lowercase_      ,       map_location=lowercase_      )
						if ".ckpt" in ckpt_path:
												__UpperCAmelCase			:							Union[str, Any]	    =	sd['''state_dict''']
						model.load_state_dict(lowercase_      ,       strict=lowercase_      )
						model.to(lowercase_      )
						del sd
						return model




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	model.encode(lowercase_      )
						print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
						__UpperCAmelCase			:							List[str]	    =	model.decode(lowercase_      )
						return xrec




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	string.rsplit('''.'''      ,       1      )
						if reload:
												__UpperCAmelCase			:							Optional[int]	    =	importlib.import_module(lowercase_      )
												importlib.reload(lowercase_      )
						return getattr(importlib.import_module(lowercase_      ,       package=lowercase_      )      ,       cls      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







						if "target" not in config:
												raise KeyError('''Expected key `target` to instantiate.'''      )
						return get_obj_from_str(config['''target''']      )(**config.get('''params'''      ,       {}      )      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=True      ,       lowercase_=True      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	instantiate_from_config(lowercase_      )
						if sd is not None:
												model.load_state_dict(lowercase_      )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}





def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						if ckpt:
												__UpperCAmelCase			:							Optional[int]	    =	torch.load(lowercase_      ,       map_location='''cpu'''      )
												__UpperCAmelCase			:							Union[str, Any]	    =	pl_sd['''global_step''']
												print(f"loaded model from global step {global_step}."      )
						else:
												__UpperCAmelCase			:							Union[str, Any]	    =	{'''state_dict''': None}
												__UpperCAmelCase			:							Optional[int]	    =	None
						__UpperCAmelCase			:							str	    =	load_model_from_config(config.model      ,       pl_sd['''state_dict''']      ,       gpu=lowercase_      ,       eval_mode=lowercase_      )['''model''']
						return model, global_step

