




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_   (	lowerCamelCase   ):
				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			):

													def __init__(		self   :List[Any]    , __snake_case   :nn.Module    , __snake_case   :int ):
																	'''simple docstring'''

																	super().__init__()
																	__magic_name__	:	Optional[Any]								=module
																	__magic_name__	:	Union[str, Any]								=nn.Sequential(
																	    nn.Linear(module.in_features    , __snake_case    , bias=__snake_case )    , nn.Linear(__snake_case    , module.out_features    , bias=__snake_case )    , )
																	__magic_name__	:	str								=(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     A__						(		self   :Any    , __snake_case   :List[str]    , *__snake_case   :Optional[int]    , **__snake_case   :Optional[Any] ):
																	'''simple docstring'''

																	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			):
						# 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     						=					2.109659552692574

						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     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=AutoTokenizer.from_pretrained(self.model_name )




class 		__A      (			UpperCamelCase__			):

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										super().setUp()

										# Models and tokenizer
										__magic_name__	:	Tuple								=AutoModelForCausalLM.from_pretrained(
										    self.model_name    , torch_dtype=torch.floataa    , device_map="""auto""" )
										__magic_name__	:	Union[str, Any]								=AutoModelForCausalLM.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										del self.model_fpaa
										del self.model_abit

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

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.model_abit.config

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

										__magic_name__	:	List[str]								=config.to_dict()
										__magic_name__	:	Optional[int]								=config.to_diff_dict()

										__magic_name__	:	Dict								=config.to_json_string()

						def     A__						(		self   :Any ):
										'''simple docstring'''

										from bitsandbytes.nn import Paramsabit

										__magic_name__	:	Tuple								=self.model_fpaa.get_memory_footprint()
										__magic_name__	:	Tuple								=self.model_abit.get_memory_footprint()

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

						def     A__						(		self   :int ):
										'''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(__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     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.tokenizer(self.input_text    , return_tensors="""pt""" )
										__magic_name__	:	Optional[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=__snake_case )    , self.EXPECTED_OUTPUTS )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	str								=BitsAndBytesConfig()
										__magic_name__	:	Optional[Any]								=True

										__magic_name__	:	List[str]								=AutoModelForCausalLM.from_pretrained(
										    self.model_name    , quantization_config=__snake_case    , device_map="""auto""" )

										__magic_name__	:	Dict								=self.tokenizer(self.input_text    , return_tensors="""pt""" )
										__magic_name__	:	List[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=__snake_case )    , self.EXPECTED_OUTPUTS )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	int								=BitsAndBytesConfig()

										with self.assertRaises(__snake_case ):
														__magic_name__	:	Tuple								=AutoModelForCausalLM.from_pretrained(
														    self.model_name    , quantization_config=__snake_case    , load_in_abit=__snake_case    , device_map="""auto"""    , bnb_abit_quant_type="""nf4"""    , )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										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
										__magic_name__	:	Optional[int]								=self.tokenizer(self.input_text    , return_tensors="""pt""" )

										__magic_name__	:	List[str]								=self.model_fpaa.to(torch.floataa )
										__magic_name__	:	Any								=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__	:	Tuple								=self.model_fpaa.to("""cpu""" )

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

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


						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=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			):

						@classmethod
						def     A__						(		cls   :str ):
										'''simple docstring'''

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

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

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

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										from transformers import TaForConditionalGeneration

										__magic_name__	:	str								=TaForConditionalGeneration._keep_in_fpaa_modules
										__magic_name__	:	int								=None

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

										# test with `flan-t5-small`
										__magic_name__	:	Optional[int]								=TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name    , load_in_abit=__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(**__snake_case )
										__magic_name__	:	int								=modules


						def     A__						(		self   :str ):
										'''simple docstring'''

										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										__magic_name__	:	Optional[int]								=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 ) )

										__magic_name__	:	str								=self.tokenizer(self.input_text    , return_tensors="""pt""" ).to(0 )
										__magic_name__	:	str								=model.generate(**__snake_case )

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




class 		__A      (			UpperCamelCase__			):

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										super().setUp()
										# model_name
										__magic_name__	:	Union[str, Any]								="""bigscience/bloom-560m"""
										__magic_name__	:	Tuple								="""t5-small"""

										# Different types of model

										__magic_name__	:	Union[str, Any]								=AutoModel.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										# Sequence classification model
										__magic_name__	:	Dict								=AutoModelForSequenceClassification.from_pretrained(
										    self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										# CausalLM model
										__magic_name__	:	Union[str, Any]								=AutoModelForCausalLM.from_pretrained(self.model_name    , load_in_abit=__snake_case    , device_map="""auto""" )
										# Seq2seq model
										__magic_name__	:	Dict								=AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name    , load_in_abit=__snake_case    , device_map="""auto""" )

						def     A__						(		self   :List[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     A__						(		self   :int ):
										'''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 		__A      (			UpperCamelCase__			):

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										super().setUp()

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										del self.pipe

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


						def     A__						(		self   :int ):
										'''simple docstring'''

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




@require_torch_multi_gpu
class 		__A      (			UpperCamelCase__			):

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										super().setUp()


						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=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
										__magic_name__	:	Tuple								=self.tokenizer(self.input_text    , return_tensors="""pt""" )

										# Second real batch
										__magic_name__	:	Tuple								=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      (			UpperCamelCase__			):

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								="""facebook/opt-350m"""
										super().setUp()


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

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

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

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

        # Step 3: dummy batch
										__magic_name__	:	int								=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__	:	Optional[Any]								=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      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""gpt2-xl"""
						UpperCamelCase     						=					3.3191854854152187









def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=[]
				__magic_name__	:	int								=[]
				__magic_name__	:	str								={
				    """^""": 3,
				    """*""": 2,
				    """/""": 2,
				    """%""": 2,
				    """+""": 1,
				    """-""": 1,
				}  # Priority of each operator
				__magic_name__	:	Dict								=len(lowerCamelCase   ) if (len(lowerCamelCase   ) > 7) else 7

				# Print table header for output
				print(
				    """Symbol""".center(8   )	,     """Stack""".center(lowerCamelCase   )	,     """Postfix""".center(lowerCamelCase   )	,     sep=""" | """	,     )
				print("""-""" * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase   ) == 0:
																stack.append(lowerCamelCase   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(lowerCamelCase   )  # push x to stack

								print(
								    x.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				while len(lowerCamelCase   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    """ """.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				return "".join(lowerCamelCase   )  # return Postfix as str




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(lowerCamelCase   )   ):
								if infix[i] == "(":
												__magic_name__	:	str								=""")"""  # change "(" to ")"
								elif infix[i] == ")":
												__magic_name__	:	int								="""("""  # change ")" to "("

				return (infix_2_postfix("""""".join(lowerCamelCase   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     input("\nEnter an Infix Equation = ")  # Input an Infix equation
							UpperCAmelCase_		:				Dict									=     "".join(Infix.split())  # Remove spaces from the input
							print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")




