


'''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 A_       (					__SCREAMING_SNAKE_CASE			:       Tuple							)  ->	List[str]:



					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		):




									def __init__(   self       ,   lowercase       ,   lowercase							)      ->    Tuple:


														'''simple docstring'''






														super().__init__()
														__SCREAMING_SNAKE_CASE       :				Union[str, Any]       =			module
														__SCREAMING_SNAKE_CASE       :				str       =			nn.Sequential(
														    nn.Linear(module.in_features       ,   snake_case__       ,   bias=snake_case__							)       ,   nn.Linear(snake_case__       ,   module.out_features       ,   bias=snake_case__							)       ,   )
														__SCREAMING_SNAKE_CASE       :				Tuple       =			(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       _snake_case (   self       ,   lowercase       ,   *lowercase       ,   **lowercase							)      ->    Tuple:


														'''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   SCREAMING_SNAKE_CASE_						(			unittest.TestCase		):
			__a       :   List[Any]       =	"""bigscience/bloom-1b7"""

			# Constant values
			__a       :   List[Any]       =	2.109_659_552_692_574

			__a       :   Optional[Any]       =	"""Hello my name is"""
			__a       :   List[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'''		)
			__a       :   Dict       =	10




			def       _snake_case (   self							)      ->    Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Tuple       =			AutoTokenizer.from_pretrained(self.model_name							)






class   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):




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


								'''simple docstring'''






								super().setUp()

								# Models and tokenizer
								__SCREAMING_SNAKE_CASE       :				Tuple       =			AutoModelForCausalLM.from_pretrained(
								    self.model_name       ,   torch_dtype=torch.floataa       ,   device_map='''auto'''							)
								__SCREAMING_SNAKE_CASE       :				Any       =			AutoModelForCausalLM.from_pretrained(self.model_name       ,   load_in_abit=snake_case__       ,   device_map='''auto'''							)




			def       _snake_case (   self							)      ->    Optional[Any]:


								'''simple docstring'''






								del self.model_fpaa
								del self.model_abit

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




			def       _snake_case (   self							)      ->    Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				List[Any]       =			self.model_abit.config

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

								__SCREAMING_SNAKE_CASE       :				List[Any]       =			config.to_dict()
								__SCREAMING_SNAKE_CASE       :				Dict       =			config.to_diff_dict()

								__SCREAMING_SNAKE_CASE       :				List[str]       =			config.to_json_string()




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


								'''simple docstring'''






								from bitsandbytes.nn import Paramsabit

								__SCREAMING_SNAKE_CASE       :				List[Any]       =			self.model_fpaa.get_memory_footprint()
								__SCREAMING_SNAKE_CASE       :				Dict       =			self.model_abit.get_memory_footprint()

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




			def       _snake_case (   self							)      ->    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(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       _snake_case (   self							)      ->    Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				List[str]       =			self.tokenizer(self.input_text       ,   return_tensors='''pt'''							)
								__SCREAMING_SNAKE_CASE       :				Optional[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=snake_case__							)       ,   self.EXPECTED_OUTPUTS							)




			def       _snake_case (   self							)      ->    Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				List[Any]       =			BitsAndBytesConfig()
								__SCREAMING_SNAKE_CASE       :				List[str]       =			True

								__SCREAMING_SNAKE_CASE       :				List[str]       =			AutoModelForCausalLM.from_pretrained(
								    self.model_name       ,   quantization_config=snake_case__       ,   device_map='''auto'''							)

								__SCREAMING_SNAKE_CASE       :				Tuple       =			self.tokenizer(self.input_text       ,   return_tensors='''pt'''							)
								__SCREAMING_SNAKE_CASE       :				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=snake_case__							)       ,   self.EXPECTED_OUTPUTS							)




			def       _snake_case (   self							)      ->    List[Any]:


								'''simple docstring'''






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




			def       _snake_case (   self							)      ->    List[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Any       =			BitsAndBytesConfig()

								with self.assertRaises(snake_case__							):
													__SCREAMING_SNAKE_CASE       :				List[str]       =			AutoModelForCausalLM.from_pretrained(
													    self.model_name       ,   quantization_config=snake_case__       ,   load_in_abit=snake_case__       ,   device_map='''auto'''       ,   bnb_abit_quant_type='''nf4'''       ,   )




			def       _snake_case (   self							)      ->    Optional[Any]:


								'''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
								__SCREAMING_SNAKE_CASE       :				Optional[Any]       =			self.tokenizer(self.input_text       ,   return_tensors='''pt'''							)

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

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

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

								# Check this does not throw an error
								__SCREAMING_SNAKE_CASE       :				Optional[int]       =			self.model_fpaa.float()




			def       _snake_case (   self							)      ->    List[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Optional[int]       =			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   SCREAMING_SNAKE_CASE_						(			unittest.TestCase		):




			@classmethod
			def       _snake_case (   cls							)      ->    Tuple:


								'''simple docstring'''






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




			def       _snake_case (   self							)      ->    List[Any]:


								'''simple docstring'''






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




			def       _snake_case (   self							)      ->    Any:


								'''simple docstring'''






								from transformers import TaForConditionalGeneration

								__SCREAMING_SNAKE_CASE       :				List[str]       =			TaForConditionalGeneration._keep_in_fpaa_modules
								__SCREAMING_SNAKE_CASE       :				str       =			None

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

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




			def       _snake_case (   self							)      ->    str:


								'''simple docstring'''






								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								__SCREAMING_SNAKE_CASE       :				Tuple       =			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							)							)

								__SCREAMING_SNAKE_CASE       :				Tuple       =			self.tokenizer(self.input_text       ,   return_tensors='''pt'''							).to(0							)
								__SCREAMING_SNAKE_CASE       :				Optional[int]       =			model.generate(**snake_case__							)

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






class   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):




			def       _snake_case (   self							)      ->    List[Any]:


								'''simple docstring'''






								super().setUp()
								# model_name
								__SCREAMING_SNAKE_CASE       :				List[str]       =			"""bigscience/bloom-560m"""
								__SCREAMING_SNAKE_CASE       :				List[str]       =			"""t5-small"""

								# Different types of model

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




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


								'''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       _snake_case (   self							)      ->    str:


								'''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   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):




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


								'''simple docstring'''






								super().setUp()




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


								'''simple docstring'''






								del self.pipe

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




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				List[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
								__SCREAMING_SNAKE_CASE       :				Union[str, Any]       =			self.pipe(self.input_text							)
								self.assertIn(pipeline_output[0]['''generated_text''']       ,   self.EXPECTED_OUTPUTS							)






@require_torch_multi_gpu
class   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):




			def       _snake_case (   self							)      ->    Dict:


								'''simple docstring'''






								super().setUp()




			def       _snake_case (   self							)      ->    Optional[int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Union[str, 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
								__SCREAMING_SNAKE_CASE       :				Tuple       =			self.tokenizer(self.input_text       ,   return_tensors='''pt'''							)

								# Second real batch
								__SCREAMING_SNAKE_CASE       :				Dict       =			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=snake_case__							)       ,   self.EXPECTED_OUTPUTS							)






class   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):




			def       _snake_case (   self							)      ->    int:


								'''simple docstring'''






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




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


								'''simple docstring'''






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

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

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

        # Step 3: dummy batch
								__SCREAMING_SNAKE_CASE       :				Optional[Any]       =			self.tokenizer('''Test batch '''       ,   return_tensors='''pt'''							).to(0							)

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
													__SCREAMING_SNAKE_CASE       :				Optional[int]       =			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   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):
			__a       :   Optional[Any]       =	"""gpt2-xl"""
			__a       :   str       =	3.3_191_854_854_152_187









def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if n_term == "":
														return []
							lowercase_	:   list  				=     []
							for temp in range(int(lowercase				)				):
														series.append(f"""1/{temp + 1}""" if series else """1"""				)
							return series


if __name__ == "__main__":
							UpperCAmelCase_							=				input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))