




"""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					(      A__:     List[str]  )	->  List[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 __lowercase(   nn.Module					):




										'''simple docstring'''


										def __init__(     self    ,						__a    ,						__a  ):
													super().__init__()
													__lowerCamelCase       :						List[Any]													=					module
													__lowerCamelCase       :						Any													=					nn.Sequential(
													    nn.Linear(module.in_features    ,						__a    ,						bias=__a  )    ,						nn.Linear(__a    ,						module.out_features    ,						bias=__a  )    ,						)
													__lowerCamelCase       :						List[str]													=					(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      snake_case_					(     self    ,						__a    ,						*__a    ,						**__a  ):
													return self.module(__a    ,						*__a    ,						**__a  ) + self.adapter(__a  )

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




					'''simple docstring'''


					__a    :    Any  		=			'bigscience/bloom-1b7'

					# Constant values
					__a    :    List[str]  		=			2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

					__a    :    int  		=			'Hello my name is'
					__a    :    Tuple  		=			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    :    Any  		=			10
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Any													=					AutoTokenizer.from_pretrained(self.model_name  )



class __lowercase(   __snake_case					):




					'''simple docstring'''


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

								# Models and tokenizer
								__lowerCamelCase       :						Optional[Any]													=					AutoModelForCausalLM.from_pretrained(
								    self.model_name    ,						torch_dtype=torch.floataa    ,						device_map='auto'  )
								__lowerCamelCase       :						List[Any]													=					AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=__a    ,						device_map='auto'  )
					def      snake_case_					(     self  ):
								del self.model_fpaa
								del self.model_abit

								gc.collect()
								torch.cuda.empty_cache()
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Any													=					self.model_abit.config

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

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

								__lowerCamelCase       :						Union[str, Any]													=					config.to_json_string()
					def      snake_case_					(     self  ):
								from bitsandbytes.nn import Paramsabit

								__lowerCamelCase       :						Optional[Any]													=					self.model_fpaa.get_memory_footprint()
								__lowerCamelCase       :						Any													=					self.model_abit.get_memory_footprint()

								self.assertAlmostEqual(mem_fpaa / mem_abit    ,						self.EXPECTED_RELATIVE_DIFFERENCE  )
								__lowerCamelCase       :						Union[str, Any]													=					get_some_linear_layer(self.model_abit  )
								self.assertTrue(linear.weight.__class__ == Paramsabit  )
					def      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(__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      snake_case_					(     self  ):
								__lowerCamelCase       :						List[Any]													=					self.tokenizer(self.input_text    ,						return_tensors='pt'  )
								__lowerCamelCase       :						List[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=__a  )    ,						self.EXPECTED_OUTPUTS  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						str													=					BitsAndBytesConfig()
								__lowerCamelCase       :						Optional[Any]													=					True

								__lowerCamelCase       :						int													=					AutoModelForCausalLM.from_pretrained(
								    self.model_name    ,						quantization_config=__a    ,						device_map='auto'  )

								__lowerCamelCase       :						Union[str, Any]													=					self.tokenizer(self.input_text    ,						return_tensors='pt'  )
								__lowerCamelCase       :						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=__a  )    ,						self.EXPECTED_OUTPUTS  )
					def      snake_case_					(     self  ):






								with self.assertRaises(__a  ), tempfile.TemporaryDirectory() as tmpdirname:
											self.model_abit.save_pretrained(__a  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Union[str, Any]													=					BitsAndBytesConfig()

								with self.assertRaises(__a  ):
											__lowerCamelCase       :						int													=					AutoModelForCausalLM.from_pretrained(
											    self.model_name    ,						quantization_config=__a    ,						load_in_abit=__a    ,						device_map='auto'    ,						bnb_abit_quant_type='nf4'    ,						)
					def      snake_case_					(     self  ):






								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
								__lowerCamelCase       :						int													=					self.tokenizer(self.input_text    ,						return_tensors='pt'  )

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

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

								# Check this does not throw an error
								__lowerCamelCase       :						List[str]													=					self.model_fpaa.half()

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





					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Tuple													=					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 __lowercase(   unittest.TestCase					):




					'''simple docstring'''


					@classmethod
					def      snake_case_					(     cls  ):
								__lowerCamelCase       :						int													=					'''t5-small'''
								__lowerCamelCase       :						Any													=					'''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
								__lowerCamelCase       :						List[Any]													=					AutoTokenizer.from_pretrained(cls.model_name  )
								__lowerCamelCase       :						List[str]													=					'''Translate in German: Hello, my dog is cute'''
					def      snake_case_					(     self  ):
								gc.collect()
								torch.cuda.empty_cache()
					def      snake_case_					(     self  ):
								from transformers import TaForConditionalGeneration

								__lowerCamelCase       :						int													=					TaForConditionalGeneration._keep_in_fpaa_modules
								__lowerCamelCase       :						Optional[Any]													=					None

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

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





					def      snake_case_					(     self  ):
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								__lowerCamelCase       :						List[str]													=					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  )  )

								__lowerCamelCase       :						List[Any]													=					self.tokenizer(self.input_text    ,						return_tensors='pt'  ).to(0  )
								__lowerCamelCase       :						Any													=					model.generate(**__a  )

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



class __lowercase(   __snake_case					):




					'''simple docstring'''


					def      snake_case_					(     self  ):
								super().setUp()
								# model_name
								__lowerCamelCase       :						Tuple													=					'''bigscience/bloom-560m'''
								__lowerCamelCase       :						int													=					'''t5-small'''

								# Different types of model

								__lowerCamelCase       :						Optional[int]													=					AutoModel.from_pretrained(self.model_name    ,						load_in_abit=__a    ,						device_map='auto'  )
								# Sequence classification model
								__lowerCamelCase       :						Optional[Any]													=					AutoModelForSequenceClassification.from_pretrained(
								    self.model_name    ,						load_in_abit=__a    ,						device_map='auto'  )
								# CausalLM model
								__lowerCamelCase       :						List[str]													=					AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=__a    ,						device_map='auto'  )
								# Seq2seq model
								__lowerCamelCase       :						Optional[int]													=					AutoModelForSeqaSeqLM.from_pretrained(
								    self.seq_to_seq_name    ,						load_in_abit=__a    ,						device_map='auto'  )
					def      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      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 __lowercase(   __snake_case					):




					'''simple docstring'''


					def      snake_case_					(     self  ):
								super().setUp()
					def      snake_case_					(     self  ):
								del self.pipe

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





					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Optional[Any]													=					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       :						str													=					self.pipe(self.input_text  )
								self.assertIn(pipeline_output[0]['generated_text']    ,						self.EXPECTED_OUTPUTS  )



@require_torch_multi_gpu
class __lowercase(   __snake_case					):




					'''simple docstring'''


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





					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Tuple													=					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
								__lowerCamelCase       :						Optional[int]													=					self.tokenizer(self.input_text    ,						return_tensors='pt'  )

								# Second real batch
								__lowerCamelCase       :						Optional[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=__a  )    ,						self.EXPECTED_OUTPUTS  )



class __lowercase(   __snake_case					):




					'''simple docstring'''


					def      snake_case_					(     self  ):
								__lowerCamelCase       :						int													=					'''facebook/opt-350m'''
								super().setUp()





					def      snake_case_					(     self  ):






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

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

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

        # 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       :						str													=					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 __lowercase(   __snake_case					):




					'''simple docstring'''


					__a    :    Any  		=			'gpt2-xl'
					__a    :    Tuple  		=			3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7





'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		a[left_index]
    A			:  List[str]   =		left_index + 1
    for j in range(left_index + 1						,							snake_case__		):
        if a[j] < pivot:
            A,    A			:  Optional[int]   =		a[i], a[j]
            i += 1
    A,    A			:  str   =		a[i - 1], a[left_index]
    return i - 1







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if left < right:
        A			:  Optional[Any]   =		random.randint(snake_case__						,							right - 1		)
        A,    A			:  List[str]   =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        A			:  Any   =		partition(snake_case__						,							snake_case__						,							snake_case__		)
        quick_sort_random(
            snake_case__						,							snake_case__						,							snake_case__		)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            snake_case__						,							pivot_index + 1						,							snake_case__		)  # recursive quicksort to the right of the pivot point







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    A			:  Any   =		input('''Enter numbers separated by a comma:\n'''		).strip()
    A			:  List[str]   =		[int(snake_case__		) for item in user_input.split(''','''		)]

    quick_sort_random(snake_case__						,							0						,							len(snake_case__		)		)

    print(snake_case__		)


if __name__ == "__main__":
      main()




