
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_		(							__SCREAMING_SNAKE_CASE							):
	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   ):




		'''simple docstring'''


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





		def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    *snake_case		,    **snake_case       ):
			return self.module(snake_case		,    *snake_case		,    **snake_case       ) + self.adapter(snake_case       )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       """bigscience/bloom-1b7"""

	# Constant values
	_UpperCamelCase     :     Union[str, Any]							 =       2.109659552692574

	_UpperCamelCase     :     Any							 =       """Hello my name is"""
	_UpperCamelCase     :     Dict							 =       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     :     Union[str, Any]							 =       10





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		# Models and tokenizer
		lowercase       =    AutoTokenizer.from_pretrained(self.model_name       )







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().setUp()

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		del self.model_fpaa
		del self.model_abit

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_abit.config

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

		lowercase       =    config.to_dict()
		lowercase       =    config.to_diff_dict()

		lowercase       =    config.to_json_string()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from bitsandbytes.nn import Paramsabit

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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(snake_case		,    torch.nn.Linear       ):
				if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
					# 4-bit parameters are packed in uint8 variables
					self.assertTrue(module.weight.dtype == torch.uinta       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.tokenizer(self.input_text		,    return_tensors='pt'       )
		lowercase       =    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=snake_case       )		,    self.EXPECTED_OUTPUTS       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    BitsAndBytesConfig()
		lowercase       =    True

		lowercase       =    AutoModelForCausalLM.from_pretrained(
		    self.model_name		,    quantization_config=snake_case		,    device_map='auto'       )

		lowercase       =    self.tokenizer(self.input_text		,    return_tensors='pt'       )
		lowercase       =    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=snake_case       )		,    self.EXPECTED_OUTPUTS       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		with self.assertRaises(snake_case       ), tempfile.TemporaryDirectory() as tmpdirname:
			self.model_abit.save_pretrained(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    BitsAndBytesConfig()

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		with self.assertRaises(snake_case       ):
			# Tries with `str`
			self.model_abit.to('cpu'       )

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

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

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

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

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

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

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

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    AutoModelForSeqaSeqLM.from_pretrained('t5-small'		,    load_in_abit=snake_case		,    device_map='auto'       )
		self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa       )







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		gc.collect()
		torch.cuda.empty_cache()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import TaForConditionalGeneration

		lowercase       =    TaForConditionalGeneration._keep_in_fpaa_modules
		lowercase       =    None

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		import bitsandbytes as bnb

		from transformers import TaForConditionalGeneration

		# test with `t5-small`
		lowercase       =    TaForConditionalGeneration.from_pretrained(self.model_name		,    load_in_abit=snake_case		,    device_map='auto'       )

		# there was a bug with decoders - this test checks that it is fixed
		self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q		,    bnb.nn.Linearabit       )       )

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

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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().setUp()
		# model_name
		lowercase       =    'bigscience/bloom-560m'
		lowercase       =    't5-small'

		# Different types of model

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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 	SCREAMING_SNAKE_CASE__						(       self       ):
		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_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().setUp()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		del self.pipe

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    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
		lowercase       =    self.pipe(self.input_text       )
		self.assertIn(pipeline_output[0]['generated_text']		,    self.EXPECTED_OUTPUTS       )







@require_torch_multi_gpu
class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().setUp()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    AutoModelForCausalLM.from_pretrained(
		    self.model_name		,    load_in_abit=snake_case		,    device_map='balanced'       )

		# Check correct device map
		self.assertEqual(set(model_parallel.hf_device_map.values()       )		,    {0, 1}       )

		# Check that inference pass works on the model
		lowercase       =    self.tokenizer(self.input_text		,    return_tensors='pt'       )

		# Second real batch
		lowercase       =    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=snake_case       )		,    self.EXPECTED_OUTPUTS       )







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'facebook/opt-350m'
		super().setUp()





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

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

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

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

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

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

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

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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       """gpt2-xl"""
	_UpperCamelCase     :     Optional[Any]							 =       3.3191854854152187



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							UpperCAmelCase_		(							):
	lowercase       =    ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch '
	        'helper utility that will spawn up '
	        'multiple distributed processes'
	    )							)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores' ,							type=__SCREAMING_SNAKE_CASE ,							default=1 ,							help='Number of TPU cores to use (1 or 8).'							)

	# positional
	parser.add_argument(
	    'training_script' ,							type=__SCREAMING_SNAKE_CASE ,							help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    ) ,							)

	# rest from the training program
	parser.add_argument('training_script_args' ,							nargs=__SCREAMING_SNAKE_CASE							)

	return parser.parse_args()






def 							UpperCAmelCase_		(							):
	lowercase       =    parse_args()

	# Import training_script as a module.
	lowercase       =    Path(args.training_script							)
	sys.path.append(str(script_fpath.parent.resolve()							)							)
	lowercase       =    script_fpath.stem
	lowercase       =    importlib.import_module(__SCREAMING_SNAKE_CASE							)

	# Patch sys.argv
	lowercase       =    [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores							)]
	xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
	main()

