
import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

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






def     lowerCAmelCase_		(   _snake_case :    Optional[Any]  )							->						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  _snake_case   (	nn.Module							):







							def __init__(							self		,  _a		,  _a						):
								super().__init__()
								__magic_name__						:						Optional[int]        =	module
								__magic_name__						:						Optional[int]        =	nn.Sequential(
								    nn.Linear(module.in_features		,  __SCREAMING_SNAKE_CASE		,  bias=__SCREAMING_SNAKE_CASE						)		,  nn.Linear(__SCREAMING_SNAKE_CASE		,  module.out_features		,  bias=__SCREAMING_SNAKE_CASE						)		,  )
								__magic_name__						:						List[Any]        =	(2.0 / (5 * min(module.in_features		,  module.out_features						))) ** 0.5
								nn.init.normal_(self.adapter[0].weight		,  std=__SCREAMING_SNAKE_CASE						)
								nn.init.zeros_(self.adapter[1].weight						)
								self.adapter.to(module.weight.device						)







							def      SCREAMING_SNAKE_CASE       (							self		,  _a		,  *_a		,  **_a						):
								return self.module(__SCREAMING_SNAKE_CASE		,  *__SCREAMING_SNAKE_CASE		,  **__SCREAMING_SNAKE_CASE						) + self.adapter(__SCREAMING_SNAKE_CASE						)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class  _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
			UpperCamelCase__        =       'bigscience/bloom-1b7'

			# Constant values
			UpperCamelCase__        =       2.1_09_65_95_52_69_25_74

			UpperCamelCase__        =       'Hello my name is'
			UpperCamelCase__        =       set()
			EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I'							)
			EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n'							)
			EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University'							)
			UpperCamelCase__        =       10







			def      SCREAMING_SNAKE_CASE       (							self						):
				# Models and tokenizer
				__magic_name__						:						int        =	AutoTokenizer.from_pretrained(self.model_name						)







class  _snake_case   (	a_							):







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

				# Models and tokenizer
				__magic_name__						:						str        =	AutoModelForCausalLM.from_pretrained(
				    self.model_name		,  torch_dtype=torch.floataa		,  device_map="auto"						)
				__magic_name__						:						Any        =	AutoModelForCausalLM.from_pretrained(self.model_name		,  load_in_abit=__SCREAMING_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						):
				__magic_name__						:						Optional[int]        =	self.model_abit.config

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

				__magic_name__						:						List[Any]        =	config.to_dict()
				__magic_name__						:						Any        =	config.to_diff_dict()

				__magic_name__						:						Any        =	config.to_json_string()







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

				__magic_name__						:						str        =	self.model_fpaa.get_memory_footprint()
				__magic_name__						:						List[str]        =	self.model_abit.get_memory_footprint()

				self.assertAlmostEqual(mem_fpaa / mem_abit		,  self.EXPECTED_RELATIVE_DIFFERENCE						)
				__magic_name__						:						Optional[Any]        =	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(__SCREAMING_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						):
				__magic_name__						:						int        =	self.tokenizer(self.input_text		,  return_tensors="pt"						)
				__magic_name__						:						str        =	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=__SCREAMING_SNAKE_CASE						)		,  self.EXPECTED_OUTPUTS						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Any        =	BitsAndBytesConfig()
				__magic_name__						:						Optional[int]        =	True

				__magic_name__						:						Union[str, Any]        =	AutoModelForCausalLM.from_pretrained(
				    self.model_name		,  quantization_config=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)

				__magic_name__						:						Any        =	self.tokenizer(self.input_text		,  return_tensors="pt"						)
				__magic_name__						:						Any        =	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=__SCREAMING_SNAKE_CASE						)		,  self.EXPECTED_OUTPUTS						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				with self.assertRaises(__SCREAMING_SNAKE_CASE						), tempfile.TemporaryDirectory() as tmpdirname:
					self.model_abit.save_pretrained(__SCREAMING_SNAKE_CASE						)







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Dict        =	BitsAndBytesConfig()

				with self.assertRaises(__SCREAMING_SNAKE_CASE						):
					__magic_name__						:						Optional[Any]        =	AutoModelForCausalLM.from_pretrained(
					    self.model_name		,  quantization_config=__SCREAMING_SNAKE_CASE		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"		,  bnb_abit_quant_type="nf4"		,  )







			def      SCREAMING_SNAKE_CASE       (							self						):
				with self.assertRaises(__SCREAMING_SNAKE_CASE						):
					# Tries with `str`
					self.model_abit.to("cpu"						)

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

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

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

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

				# Test if we did not break anything
				__magic_name__						:						List[Any]        =	self.tokenizer(self.input_text		,  return_tensors="pt"						)

				__magic_name__						:						Dict        =	self.model_fpaa.to(torch.floataa						)
				__magic_name__						:						List[str]        =	self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0						)		,  max_new_tokens=10						)

				# Check this does not throw an error
				__magic_name__						:						List[str]        =	self.model_fpaa.to("cpu"						)

				# Check this does not throw an error
				__magic_name__						:						Optional[int]        =	self.model_fpaa.half()

				# Check this does not throw an error
				__magic_name__						:						str        =	self.model_fpaa.float()







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Any        =	AutoModelForSeqaSeqLM.from_pretrained("t5-small"		,  load_in_abit=__SCREAMING_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  _snake_case   (	unittest.TestCase							):







			@classmethod
			def      SCREAMING_SNAKE_CASE       (							cls						):
				__magic_name__						:						List[Any]        =	"t5-small"
				__magic_name__						:						List[Any]        =	"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
				__magic_name__						:						int        =	AutoTokenizer.from_pretrained(cls.model_name						)
				__magic_name__						:						Optional[Any]        =	"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

				__magic_name__						:						Optional[Any]        =	TaForConditionalGeneration._keep_in_fpaa_modules
				__magic_name__						:						Union[str, Any]        =	None

				# test with `t5-small`
				__magic_name__						:						Optional[int]        =	TaForConditionalGeneration.from_pretrained(self.model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)
				__magic_name__						:						Optional[int]        =	self.tokenizer(self.input_text		,  return_tensors="pt"						).to(0						)
				__magic_name__						:						Optional[Any]        =	model.generate(**__SCREAMING_SNAKE_CASE						)

				# test with `flan-t5-small`
				__magic_name__						:						List[str]        =	TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)
				__magic_name__						:						Union[str, Any]        =	self.tokenizer(self.input_text		,  return_tensors="pt"						).to(0						)
				__magic_name__						:						int        =	model.generate(**__SCREAMING_SNAKE_CASE						)
				__magic_name__						:						Union[str, Any]        =	modules







			def      SCREAMING_SNAKE_CASE       (							self						):
				import bitsandbytes as bnb

				from transformers import TaForConditionalGeneration

				# test with `t5-small`
				__magic_name__						:						Any        =	TaForConditionalGeneration.from_pretrained(self.model_name		,  load_in_abit=__SCREAMING_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						)						)

				__magic_name__						:						List[Any]        =	self.tokenizer(self.input_text		,  return_tensors="pt"						).to(0						)
				__magic_name__						:						List[str]        =	model.generate(**__SCREAMING_SNAKE_CASE						)

				# test with `flan-t5-small`
				__magic_name__						:						List[Any]        =	TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)
				__magic_name__						:						Tuple        =	self.tokenizer(self.input_text		,  return_tensors="pt"						).to(0						)
				__magic_name__						:						Tuple        =	model.generate(**__SCREAMING_SNAKE_CASE						)







class  _snake_case   (	a_							):







			def      SCREAMING_SNAKE_CASE       (							self						):
				super().setUp()
				# model_name
				__magic_name__						:						List[Any]        =	"bigscience/bloom-560m"
				__magic_name__						:						List[str]        =	"t5-small"

				# Different types of model

				__magic_name__						:						int        =	AutoModel.from_pretrained(self.model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)
				# Sequence classification model
				__magic_name__						:						Optional[Any]        =	AutoModelForSequenceClassification.from_pretrained(
				    self.model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)
				# CausalLM model
				__magic_name__						:						str        =	AutoModelForCausalLM.from_pretrained(self.model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE		,  device_map="auto"						)
				# Seq2seq model
				__magic_name__						:						Union[str, Any]        =	AutoModelForSeqaSeqLM.from_pretrained(
				    self.seq_to_seq_name		,  load_in_abit=__SCREAMING_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  _snake_case   (	a_							):







			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						):
				__magic_name__						:						Dict        =	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
				__magic_name__						:						Optional[int]        =	self.pipe(self.input_text						)
				self.assertIn(pipeline_output[0]["generated_text"]		,  self.EXPECTED_OUTPUTS						)







@require_torch_multi_gpu
class  _snake_case   (	a_							):







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







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Optional[int]        =	AutoModelForCausalLM.from_pretrained(
				    self.model_name		,  load_in_abit=__SCREAMING_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
				__magic_name__						:						Optional[Any]        =	self.tokenizer(self.input_text		,  return_tensors="pt"						)

				# Second real batch
				__magic_name__						:						Any        =	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=__SCREAMING_SNAKE_CASE						)		,  self.EXPECTED_OUTPUTS						)







class  _snake_case   (	a_							):







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						Any        =	"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
				__magic_name__						:						Union[str, Any]        =	AutoModelForCausalLM.from_pretrained(self.model_name		,  load_in_abit=__SCREAMING_SNAKE_CASE						)

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

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

						   # Step 2: add adapters
				for _, module in model.named_modules():
					if "OPTAttention" in repr(type(__SCREAMING_SNAKE_CASE						)						):
						__magic_name__						:						List[str]        =	LoRALayer(module.q_proj		,  rank=16						)
						__magic_name__						:						Optional[int]        =	LoRALayer(module.k_proj		,  rank=16						)
						__magic_name__						:						str        =	LoRALayer(module.v_proj		,  rank=16						)

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

				# Step 4: Check if the gradient is not None
				with torch.cuda.amp.autocast():
					__magic_name__						:						List[Any]        =	model.forward(**__SCREAMING_SNAKE_CASE						)
					out.logits.norm().backward()

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







class  _snake_case   (	a_							):
			UpperCamelCase__        =       'gpt2-xl'
			UpperCamelCase__        =       3.31_91_85_48_54_15_21_87

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def 	__A		(      ):



 """simple docstring"""


 __a							=	ArgumentParser("Accelerate CLI tool"				,					usage="accelerate <command> [<args>]"				,					allow_abbrev=_A       )
 __a							=	parser.add_subparsers(help="accelerate command helpers"       )

 # Register commands
 get_config_parser(subparsers=_A       )
 env_command_parser(subparsers=_A       )
 launch_command_parser(subparsers=_A       )
 tpu_command_parser(subparsers=_A       )
 test_command_parser(subparsers=_A       )

 # Let's go
 __a							=	parser.parse_args()

 if not hasattr(_A				,					"func"       ):
  parser.print_help()
  exit(1       )

 # Run
 args.func(_A       )


if __name__ == "__main__":
  main()


