

"""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     __SCREAMING_SNAKE_CASE    ( A_					):
	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			):






												"""simple docstring"""



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







												def 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							Optional[Any]  ,*lowercase_     :							str  ,**lowercase_     :							Union[str, Any]						):
													return self.module(lowercase_  ,*lowercase_  ,**lowercase_						) + self.adapter(lowercase_						)







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






					"""simple docstring"""



					lowercase__												=			"bigscience/bloom-1b7"

					# Constant values
					lowercase__												=			2.109659552692574

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







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						# Models and tokenizer
						lowerCAmelCase__      :		int    							=      AutoTokenizer.from_pretrained(self.model_name						)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							str						):
						super().setUp()

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







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						del self.model_fpaa
						del self.model_abit

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







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Union[str, Any]    							=      self.model_abit.config

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

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

						lowerCAmelCase__      :		Union[str, Any]    							=      config.to_json_string()







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						from bitsandbytes.nn import Paramsabit

						lowerCAmelCase__      :		str    							=      self.model_fpaa.get_memory_footprint()
						lowerCAmelCase__      :		int    							=      self.model_abit.get_memory_footprint()

						self.assertAlmostEqual(mem_fpaa / mem_abit  ,self.EXPECTED_RELATIVE_DIFFERENCE						)
						lowerCAmelCase__      :		List[str]    							=      get_some_linear_layer(self.model_abit						)
						self.assertTrue(linear.weight.__class__ == Paramsabit						)







					def 			__lowerCAmelCase					(			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(lowercase_  ,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 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		int    							=      self.tokenizer(self.input_text  ,return_tensors='''pt'''						)
						lowerCAmelCase__      :		Union[str, Any]    							=      self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0						)  ,max_new_tokens=1_0						)

						self.assertIn(self.tokenizer.decode(output_sequences[0]  ,skip_special_tokens=lowercase_						)  ,self.EXPECTED_OUTPUTS						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Optional[Any]    							=      BitsAndBytesConfig()
						lowerCAmelCase__      :		List[Any]    							=      True

						lowerCAmelCase__      :		Dict    							=      AutoModelForCausalLM.from_pretrained(
						    self.model_name  ,quantization_config=lowercase_  ,device_map='''auto'''						)

						lowerCAmelCase__      :		Dict    							=      self.tokenizer(self.input_text  ,return_tensors='''pt'''						)
						lowerCAmelCase__      :		List[Any]    							=      model_abit_from_config.generate(
						    input_ids=encoded_input['''input_ids'''].to(0						)  ,max_new_tokens=1_0						)

						self.assertIn(self.tokenizer.decode(output_sequences[0]  ,skip_special_tokens=lowercase_						)  ,self.EXPECTED_OUTPUTS						)







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						with self.assertRaises(lowercase_						), tempfile.TemporaryDirectory() as tmpdirname:
							self.model_abit.save_pretrained(lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__      :		Dict    							=      BitsAndBytesConfig()

						with self.assertRaises(lowercase_						):
							lowerCAmelCase__      :		Any    							=      AutoModelForCausalLM.from_pretrained(
							    self.model_name  ,quantization_config=lowercase_  ,load_in_abit=lowercase_  ,device_map='''auto'''  ,bnb_abit_quant_type='''nf4'''  ,)







					def 			__lowerCAmelCase					(			self     :							Dict						):
						with self.assertRaises(lowercase_						):
							# Tries with `str`
							self.model_abit.to('''cpu'''						)

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

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

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

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

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

						lowerCAmelCase__      :		Any    							=      self.model_fpaa.to(torch.floataa						)
						lowerCAmelCase__      :		Dict    							=      self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0						)  ,max_new_tokens=1_0						)

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

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

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







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Any    							=      AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''  ,load_in_abit=lowercase_  ,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			):






					"""simple docstring"""



					@classmethod
					def 			__lowerCAmelCase					(			cls     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      '''t5-small'''
						lowerCAmelCase__      :		List[str]    							=      '''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
						lowerCAmelCase__      :		Tuple    							=      AutoTokenizer.from_pretrained(cls.model_name						)
						lowerCAmelCase__      :		Tuple    							=      '''Translate in German: Hello, my dog is cute'''







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						gc.collect()
						torch.cuda.empty_cache()







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						from transformers import TaForConditionalGeneration

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

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

						# test with `flan-t5-small`
						lowerCAmelCase__      :		Any    							=      TaForConditionalGeneration.from_pretrained(
						    self.dense_act_model_name  ,load_in_abit=lowercase_  ,device_map='''auto'''						)
						lowerCAmelCase__      :		Dict    							=      self.tokenizer(self.input_text  ,return_tensors='''pt'''						).to(0						)
						lowerCAmelCase__      :		Tuple    							=      model.generate(**lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      modules







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						import bitsandbytes as bnb

						from transformers import TaForConditionalGeneration

						# test with `t5-small`
						lowerCAmelCase__      :		Union[str, Any]    							=      TaForConditionalGeneration.from_pretrained(self.model_name  ,load_in_abit=lowercase_  ,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						)						)

						lowerCAmelCase__      :		List[str]    							=      self.tokenizer(self.input_text  ,return_tensors='''pt'''						).to(0						)
						lowerCAmelCase__      :		Tuple    							=      model.generate(**lowercase_						)

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







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						super().setUp()
						# model_name
						lowerCAmelCase__      :		str    							=      '''bigscience/bloom-560m'''
						lowerCAmelCase__      :		str    							=      '''t5-small'''

						# Different types of model

						lowerCAmelCase__      :		str    							=      AutoModel.from_pretrained(self.model_name  ,load_in_abit=lowercase_  ,device_map='''auto'''						)
						# Sequence classification model
						lowerCAmelCase__      :		Optional[Any]    							=      AutoModelForSequenceClassification.from_pretrained(
						    self.model_name  ,load_in_abit=lowercase_  ,device_map='''auto'''						)
						# CausalLM model
						lowerCAmelCase__      :		Union[str, Any]    							=      AutoModelForCausalLM.from_pretrained(self.model_name  ,load_in_abit=lowercase_  ,device_map='''auto'''						)
						# Seq2seq model
						lowerCAmelCase__      :		Optional[Any]    							=      AutoModelForSeqaSeqLM.from_pretrained(
						    self.seq_to_seq_name  ,load_in_abit=lowercase_  ,device_map='''auto'''						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						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 			__lowerCAmelCase					(			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 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Tuple						):
						super().setUp()







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						del self.pipe

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







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__      :		List[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
						lowerCAmelCase__      :		Optional[int]    							=      self.pipe(self.input_text						)
						self.assertIn(pipeline_output[0]['''generated_text''']  ,self.EXPECTED_OUTPUTS						)







@require_torch_multi_gpu
class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							int						):
						super().setUp()







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[str]    							=      AutoModelForCausalLM.from_pretrained(
						    self.model_name  ,load_in_abit=lowercase_  ,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
						lowerCAmelCase__      :		Tuple    							=      self.tokenizer(self.input_text  ,return_tensors='''pt'''						)

						# Second real batch
						lowerCAmelCase__      :		str    							=      model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0						)  ,max_new_tokens=1_0						)
						self.assertIn(self.tokenizer.decode(output_parallel[0]  ,skip_special_tokens=lowercase_						)  ,self.EXPECTED_OUTPUTS						)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Any    							=      '''facebook/opt-350m'''
						super().setUp()







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						if version.parse(importlib.metadata.version('''bitsandbytes'''						)						) < version.parse('''0.37.0'''						):
							return

						# Step 1: freeze all parameters
						lowerCAmelCase__      :		List[Any]    							=      AutoModelForCausalLM.from_pretrained(self.model_name  ,load_in_abit=lowercase_						)

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

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

        # Step 2: add adapters
						for _, module in model.named_modules():
							if "OPTAttention" in repr(type(lowercase_						)						):
								lowerCAmelCase__      :		Optional[Any]    							=      LoRALayer(module.q_proj  ,rank=1_6						)
								lowerCAmelCase__      :		Union[str, Any]    							=      LoRALayer(module.k_proj  ,rank=1_6						)
								lowerCAmelCase__      :		Optional[Any]    							=      LoRALayer(module.v_proj  ,rank=1_6						)

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

						# Step 4: Check if the gradient is not None
						with torch.cuda.amp.autocast():
							lowerCAmelCase__      :		Union[str, Any]    							=      model.forward(**lowercase_						)
							out.logits.norm().backward()

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







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"gpt2-xl"
					lowercase__												=			3.3191854854152187







"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__UpperCamelCase    : int				 =						input('''Enter numbers separated by a comma:\n''').strip()
							__UpperCamelCase    : Optional[Any]				 =						[int(item) for item in user_input.split(''',''')]
							print(F'''{cocktail_shaker_sort(unsorted) = }''')




