



'''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				(							_lowercase					) ->			Optional[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 		lowerCamelCase__      (						nn.Module     ):




							'''simple docstring'''



							def __init__(  self				:	List[str]     ,  UpperCamelCase_				:	nn.Module     ,  UpperCamelCase_				:	int					)  ->       int:



								'''simple docstring'''





								super().__init__()
								_lowercase					:       Dict       =		module
								_lowercase					:       Optional[Any]       =		nn.Sequential(
								    nn.Linear(module.in_features     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)     ,  nn.Linear(UpperCamelCase_     ,  module.out_features     ,  bias=UpperCamelCase_					)     ,  )
								_lowercase					:       Union[str, Any]       =		(2.0 / (5 * min(module.in_features     ,  module.out_features					))) ** 0.5
								nn.init.normal_(self.adapter[0].weight     ,  std=UpperCamelCase_					)
								nn.init.zeros_(self.adapter[1].weight					)
								self.adapter.to(module.weight.device					)




							def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	List[Any]     ,  *UpperCamelCase_				:	Dict     ,  **UpperCamelCase_				:	Optional[int]					)  ->       List[str]:



								'''simple docstring'''





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



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




					'''simple docstring'''



					A_						    =						"""bigscience/bloom-1b7"""

					# Constant values
					A_						    =						2.1_09_65_95_52_69_25_74

					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				:	Optional[Any]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		AutoTokenizer.from_pretrained(self.model_name					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						super().setUp()

						# Models and tokenizer
						_lowercase					:       Union[str, Any]       =		AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  torch_dtype=torch.floataa     ,  device_map='auto'					)
						_lowercase					:       int       =		AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Union[str, Any]:



						'''simple docstring'''





						del self.model_fpaa
						del self.model_abit

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




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		self.model_abit.config

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

						_lowercase					:       int       =		config.to_dict()
						_lowercase					:       Union[str, Any]       =		config.to_diff_dict()

						_lowercase					:       Optional[Any]       =		config.to_json_string()




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Tuple:



						'''simple docstring'''





						from bitsandbytes.nn import Paramsabit

						_lowercase					:       Any       =		self.model_fpaa.get_memory_footprint()
						_lowercase					:       str       =		self.model_abit.get_memory_footprint()

						self.assertAlmostEqual(mem_fpaa / mem_abit     ,  self.EXPECTED_RELATIVE_DIFFERENCE					)
						_lowercase					:       Optional[Any]       =		get_some_linear_layer(self.model_abit					)
						self.assertTrue(linear.weight.__class__ == Paramsabit					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Tuple:



						'''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(UpperCamelCase_     ,  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				:	Optional[int]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)
						_lowercase					:       List[Any]       =		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=UpperCamelCase_					)     ,  self.EXPECTED_OUTPUTS					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		BitsAndBytesConfig()
						_lowercase					:       Optional[int]       =		True

						_lowercase					:       Union[str, Any]       =		AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  quantization_config=UpperCamelCase_     ,  device_map='auto'					)

						_lowercase					:       str       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)
						_lowercase					:       Tuple       =		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=UpperCamelCase_					)     ,  self.EXPECTED_OUTPUTS					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       int:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       int       =		BitsAndBytesConfig()

						with self.assertRaises(UpperCamelCase_					):
							_lowercase					:       int       =		AutoModelForCausalLM.from_pretrained(
							    self.model_name     ,  quantization_config=UpperCamelCase_     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'     ,  bnb_abit_quant_type='nf4'     ,  )




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						with self.assertRaises(UpperCamelCase_					):
							# Tries with `str`
							self.model_abit.to('cpu'					)

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

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

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

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

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

						_lowercase					:       Union[str, Any]       =		self.model_fpaa.to(torch.floataa					)
						_lowercase					:       Tuple       =		self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0					)     ,  max_new_tokens=10					)

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

						# Check this does not throw an error
						_lowercase					:       Dict       =		self.model_fpaa.half()

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




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Tuple       =		AutoModelForSeqaSeqLM.from_pretrained('t5-small'     ,  load_in_abit=UpperCamelCase_     ,  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     ):




					'''simple docstring'''



					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		't5-small'
						_lowercase					:       Tuple       =		'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
						_lowercase					:       Optional[int]       =		AutoTokenizer.from_pretrained(cls.model_name					)
						_lowercase					:       Union[str, Any]       =		'Translate in German: Hello, my dog is cute'




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Any:



						'''simple docstring'''





						from transformers import TaForConditionalGeneration

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

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

						# test with `flan-t5-small`
						_lowercase					:       Dict       =		TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						_lowercase					:       str       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					).to(0					)
						_lowercase					:       int       =		model.generate(**UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		modules




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

						# test with `t5-small`
						_lowercase					:       List[Any]       =		TaForConditionalGeneration.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  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					:       Optional[Any]       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					).to(0					)
						_lowercase					:       List[Any]       =		model.generate(**UpperCamelCase_					)

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






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						super().setUp()
						# model_name
						_lowercase					:       str       =		'bigscience/bloom-560m'
						_lowercase					:       str       =		't5-small'

						# Different types of model

						_lowercase					:       Tuple       =		AutoModel.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						# Sequence classification model
						_lowercase					:       Optional[int]       =		AutoModelForSequenceClassification.from_pretrained(
						    self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						# CausalLM model
						_lowercase					:       Tuple       =		AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)
						# Seq2seq model
						_lowercase					:       List[Any]       =		AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name     ,  load_in_abit=UpperCamelCase_     ,  device_map='auto'					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Union[str, Any]:



						'''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				:	Dict					)  ->       Any:



						'''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 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Dict:



						'''simple docstring'''





						super().setUp()




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						del self.pipe

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




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       str       =		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					:       int       =		self.pipe(self.input_text					)
						self.assertIn(pipeline_output[0]['generated_text']     ,  self.EXPECTED_OUTPUTS					)






@require_torch_multi_gpu
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[Any]:



						'''simple docstring'''





						super().setUp()




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		AutoModelForCausalLM.from_pretrained(
						    self.model_name     ,  load_in_abit=UpperCamelCase_     ,  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					:       int       =		self.tokenizer(self.input_text     ,  return_tensors='pt'					)

						# Second real batch
						_lowercase					:       str       =		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=UpperCamelCase_					)     ,  self.EXPECTED_OUTPUTS					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		'facebook/opt-350m'
						super().setUp()




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





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

						# Step 1: freeze all parameters
						_lowercase					:       Optional[int]       =		AutoModelForCausalLM.from_pretrained(self.model_name     ,  load_in_abit=UpperCamelCase_					)

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

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

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

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

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

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






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""gpt2-xl"""
					A_						    =						3.31_91_85_48_54_15_21_87





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase					) ->			bool:
	return str(_lowercase					) == str(_lowercase					)[::-1]







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return int(_lowercase					) + int(str(_lowercase					)[::-1]					)







def 						__UpperCamelCase				(							_lowercase = 1_0000					) ->			int:
	_lowercase					:       List[str]       =		[]
	for num in range(1,				_lowercase					):
		_lowercase					:       Tuple       =		0
		_lowercase					:       Tuple       =		num
		while iterations < 50:
			_lowercase					:       Union[str, Any]       =		sum_reverse(_lowercase					)
			iterations += 1
			if is_palindrome(_lowercase					):
				break
		else:
			lychrel_nums.append(_lowercase					)
	return len(_lowercase					)


if __name__ == "__main__":
		print(F'''{solution() = }''')
