


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					(							lowerCAmelCase__	):
					'''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        lowercase	(      nn.Module	):




														def __init__(       self     ,A__     ,A__):
																			super().__init__()
																			lowercase					=		module
																			lowercase					=		nn.Sequential(
																			    nn.Linear(module.in_features     ,A__     ,bias=A__)     ,nn.Linear(A__     ,module.out_features     ,bias=A__)     ,)
																			lowercase					=		(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 				A__   (       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	):
							# 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
							lowercase_ : Any       ='''bigscience/bloom-1b7'''

							# Constant values
							lowercase_ : str       =2.109_659_552_692_574

							lowercase_ : str       ='''Hello my name is'''
							lowercase_ : Union[str, Any]       =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_ : Optional[Any]       =10




							def 				A__   (       self):
												# Models and tokenizer
												lowercase					=		AutoTokenizer.from_pretrained(self.model_name)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




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

												# Models and tokenizer
												lowercase					=		AutoModelForCausalLM.from_pretrained(
												    self.model_name     ,torch_dtype=torch.floataa     ,device_map='''auto''')
												lowercase					=		AutoModelForCausalLM.from_pretrained(self.model_name     ,load_in_abit=A__     ,device_map='''auto''')




							def 				A__   (       self):
												del self.model_fpaa
												del self.model_abit

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




							def 				A__   (       self):
												lowercase					=		self.model_abit.config

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

												lowercase					=		config.to_dict()
												lowercase					=		config.to_diff_dict()

												lowercase					=		config.to_json_string()




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

												lowercase					=		self.model_fpaa.get_memory_footprint()
												lowercase					=		self.model_abit.get_memory_footprint()

												self.assertAlmostEqual(mem_fpaa / mem_abit     ,self.EXPECTED_RELATIVE_DIFFERENCE)
												lowercase					=		get_some_linear_layer(self.model_abit)
												self.assertTrue(linear.weight.__class__ == Paramsabit)




							def 				A__   (       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 				A__   (       self):
												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''')
												lowercase					=		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=A__)     ,self.EXPECTED_OUTPUTS)




							def 				A__   (       self):
												lowercase					=		BitsAndBytesConfig()
												lowercase					=		True

												lowercase					=		AutoModelForCausalLM.from_pretrained(
												    self.model_name     ,quantization_config=A__     ,device_map='''auto''')

												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''')
												lowercase					=		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=A__)     ,self.EXPECTED_OUTPUTS)




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




							def 				A__   (       self):
												lowercase					=		BitsAndBytesConfig()

												with self.assertRaises(A__):
																	lowercase					=		AutoModelForCausalLM.from_pretrained(
																	    self.model_name     ,quantization_config=A__     ,load_in_abit=A__     ,device_map='''auto'''     ,bnb_abit_quant_type='''nf4'''     ,)




							def 				A__   (       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
												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''')

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

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

												# Check this does not throw an error
												lowercase					=		self.model_fpaa.half()

												# Check this does not throw an error
												lowercase					=		self.model_fpaa.float()





							def 				A__   (       self):
												lowercase					=		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	):




							@classmethod
							def 				A__   (       cls):
												lowercase					=		'''t5-small'''
												lowercase					=		'''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
												lowercase					=		AutoTokenizer.from_pretrained(cls.model_name)
												lowercase					=		'''Translate in German: Hello, my dog is cute'''




							def 				A__   (       self):
												gc.collect()
												torch.cuda.empty_cache()




							def 				A__   (       self):
												from transformers import TaForConditionalGeneration

												lowercase					=		TaForConditionalGeneration._keep_in_fpaa_modules
												lowercase					=		None

												# test with `t5-small`
												lowercase					=		TaForConditionalGeneration.from_pretrained(self.model_name     ,load_in_abit=A__     ,device_map='''auto''')
												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''').to(0)
												lowercase					=		model.generate(**A__)

												# test with `flan-t5-small`
												lowercase					=		TaForConditionalGeneration.from_pretrained(
												    self.dense_act_model_name     ,load_in_abit=A__     ,device_map='''auto''')
												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''').to(0)
												lowercase					=		model.generate(**A__)
												lowercase					=		modules





							def 				A__   (       self):
												import bitsandbytes as bnb

												from transformers import TaForConditionalGeneration

												# test with `t5-small`
												lowercase					=		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))

												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''').to(0)
												lowercase					=		model.generate(**A__)

												# test with `flan-t5-small`
												lowercase					=		TaForConditionalGeneration.from_pretrained(
												    self.dense_act_model_name     ,load_in_abit=A__     ,device_map='''auto''')
												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''').to(0)
												lowercase					=		model.generate(**A__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def 				A__   (       self):
												super().setUp()
												# model_name
												lowercase					=		'''bigscience/bloom-560m'''
												lowercase					=		'''t5-small'''

												# Different types of model

												lowercase					=		AutoModel.from_pretrained(self.model_name     ,load_in_abit=A__     ,device_map='''auto''')
												# Sequence classification model
												lowercase					=		AutoModelForSequenceClassification.from_pretrained(
												    self.model_name     ,load_in_abit=A__     ,device_map='''auto''')
												# CausalLM model
												lowercase					=		AutoModelForCausalLM.from_pretrained(self.model_name     ,load_in_abit=A__     ,device_map='''auto''')
												# Seq2seq model
												lowercase					=		AutoModelForSeqaSeqLM.from_pretrained(
												    self.seq_to_seq_name     ,load_in_abit=A__     ,device_map='''auto''')




							def 				A__   (       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 				A__   (       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	(      SCREAMING_SNAKE_CASE__	):




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




							def 				A__   (       self):
												del self.pipe

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





							def 				A__   (       self):
												lowercase					=		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					=		self.pipe(self.input_text)
												self.assertIn(pipeline_output[0]['''generated_text''']     ,self.EXPECTED_OUTPUTS)







@require_torch_multi_gpu
class        lowercase	(      SCREAMING_SNAKE_CASE__	):




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





							def 				A__   (       self):
												lowercase					=		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
												lowercase					=		self.tokenizer(self.input_text     ,return_tensors='''pt''')

												# Second real batch
												lowercase					=		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=A__)     ,self.EXPECTED_OUTPUTS)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def 				A__   (       self):
												lowercase					=		'''facebook/opt-350m'''
												super().setUp()





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

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

        # Step 2: add adapters
												for _, module in model.named_modules():
																	if "OPTAttention" in repr(type(A__)):
																						lowercase					=		LoRALayer(module.q_proj     ,rank=1_6)
																						lowercase					=		LoRALayer(module.k_proj     ,rank=1_6)
																						lowercase					=		LoRALayer(module.v_proj     ,rank=1_6)

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

												# Step 4: Check if the gradient is not None
												with torch.cuda.amp.autocast():
																	lowercase					=		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	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Any       ='''gpt2-xl'''
							lowercase_ : Union[str, Any]       =3.3_191_854_854_152_187






import os



def  UpperCamelCase					(							lowerCAmelCase__ = "input.txt"	):
					'''simple docstring'''

					with open(os.path.join(os.path.dirname(lowerCAmelCase__	)				,    lowerCAmelCase__	)	) as input_file:
										lowercase					=		[
										    [int(lowerCAmelCase__	) for element in line.split(''','''	)]
										    for line in input_file.readlines()
										]

					lowercase					=		len(lowerCAmelCase__	)
					lowercase					=		len(matrix[0]	)

					lowercase					=		[[-1 for _ in range(lowerCAmelCase__	)] for _ in range(lowerCAmelCase__	)]
					for i in range(lowerCAmelCase__	):
										lowercase					=		matrix[i][0]

					for j in range(1				,    lowerCAmelCase__	):
										for i in range(lowerCAmelCase__	):
															lowercase					=		minimal_path_sums[i][j - 1] + matrix[i][j]

										for i in range(1				,    lowerCAmelCase__	):
															lowercase					=		min(
															    minimal_path_sums[i][j]				,    minimal_path_sums[i - 1][j] + matrix[i][j]	)

										for i in range(rows - 2				,    -1				,    -1	):
															lowercase					=		min(
															    minimal_path_sums[i][j]				,    minimal_path_sums[i + 1][j] + matrix[i][j]	)

					return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums	)


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




