



"""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   snake_case__     (	__lowerCamelCase       :					int			):
			"""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  __SCREAMING_SNAKE_CASE			(   nn.Module     ):


							'''simple docstring'''





							def __init__( self							:						str,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Dict  )->      int:
										super().__init__()
										lowerCamelCase__	:	Tuple				=module
										lowerCamelCase__	:	int				=nn.Sequential(
										    nn.Linear(module.in_features,						_lowerCamelCase,						bias=_lowerCamelCase  ),						nn.Linear(_lowerCamelCase,						module.out_features,						bias=_lowerCamelCase  ),						)
										lowerCamelCase__	:	int				=(2.0 / (5 * min(module.in_features,						module.out_features  ))) ** 0.5
										nn.init.normal_(self.adapter[0].weight,						std=_lowerCamelCase  )
										nn.init.zeros_(self.adapter[1].weight  )
										self.adapter.to(module.weight.device  )




							def 	snake_case ( self							:						List[str],						lowerCamelCase							:						Tuple,						*lowerCamelCase							:						Optional[int],						**lowerCamelCase							:						int  )->      Dict:
										return self.module(_lowerCamelCase,						*_lowerCamelCase,						**_lowerCamelCase  ) + self.adapter(_lowerCamelCase  )

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


					'''simple docstring'''





					_a         =							"bigscience/bloom-1b7"

					# Constant values
					_a         =							2.109659552692574

					_a         =							"Hello my name is"
					_a         =							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         =							1_0



					def 	snake_case ( self							:						Optional[int]  )->      List[str]:
								lowerCamelCase__	:	Union[str, Any]				=AutoTokenizer.from_pretrained(self.model_name  )





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase__     ):


					'''simple docstring'''





					def 	snake_case ( self							:						List[str]  )->      str:
								super().setUp()

								# Models and tokenizer
								lowerCamelCase__	:	List[str]				=AutoModelForCausalLM.from_pretrained(
								    self.model_name,						torch_dtype=torch.floataa,						device_map='''auto'''  )
								lowerCamelCase__	:	List[str]				=AutoModelForCausalLM.from_pretrained(self.model_name,						load_in_abit=_lowerCamelCase,						device_map='''auto'''  )



					def 	snake_case ( self							:						Optional[int]  )->      int:
								del self.model_fpaa
								del self.model_abit

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



					def 	snake_case ( self							:						Tuple  )->      str:
								lowerCamelCase__	:	Union[str, Any]				=self.model_abit.config

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

								lowerCamelCase__	:	Optional[Any]				=config.to_dict()
								lowerCamelCase__	:	Optional[Any]				=config.to_diff_dict()

								lowerCamelCase__	:	Optional[int]				=config.to_json_string()



					def 	snake_case ( self							:						List[Any]  )->      Union[str, Any]:
								from bitsandbytes.nn import Paramsabit

								lowerCamelCase__	:	List[Any]				=self.model_fpaa.get_memory_footprint()
								lowerCamelCase__	:	str				=self.model_abit.get_memory_footprint()

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



					def 	snake_case ( self							:						List[str]  )->      Tuple:
								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(_lowerCamelCase,						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							:						Union[str, Any]  )->      Tuple:
								lowerCamelCase__	:	Tuple				=self.tokenizer(self.input_text,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Tuple				=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=_lowerCamelCase  ),						self.EXPECTED_OUTPUTS  )



					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	Optional[int]				=BitsAndBytesConfig()
								lowerCamelCase__	:	List[str]				=True

								lowerCamelCase__	:	Tuple				=AutoModelForCausalLM.from_pretrained(
								    self.model_name,						quantization_config=_lowerCamelCase,						device_map='''auto'''  )

								lowerCamelCase__	:	Dict				=self.tokenizer(self.input_text,						return_tensors='''pt'''  )
								lowerCamelCase__	:	Union[str, 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=_lowerCamelCase  ),						self.EXPECTED_OUTPUTS  )



					def 	snake_case ( self							:						List[str]  )->      Any:





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



					def 	snake_case ( self							:						Any  )->      Dict:
								lowerCamelCase__	:	int				=BitsAndBytesConfig()

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



					def 	snake_case ( self							:						Union[str, Any]  )->      Optional[int]:





								with self.assertRaises(_lowerCamelCase  ):
											# Tries with `str`
											self.model_abit.to('''cpu'''  )

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

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

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

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

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

								lowerCamelCase__	:	Tuple				=self.model_fpaa.to(torch.floataa  )
								lowerCamelCase__	:	Optional[int]				=self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0  ),						max_new_tokens=10  )

								# Check this does not throw an error
								lowerCamelCase__	:	Union[str, Any]				=self.model_fpaa.to('''cpu'''  )

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

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




					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	Optional[int]				=AutoModelForSeqaSeqLM.from_pretrained('''t5-small''',						load_in_abit=_lowerCamelCase,						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 	snake_case ( cls							:						int  )->      List[Any]:
								lowerCamelCase__	:	Dict				='''t5-small'''
								lowerCamelCase__	:	int				='''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
								lowerCamelCase__	:	Optional[int]				=AutoTokenizer.from_pretrained(cls.model_name  )
								lowerCamelCase__	:	Any				='''Translate in German: Hello, my dog is cute'''



					def 	snake_case ( self							:						Tuple  )->      str:
								gc.collect()
								torch.cuda.empty_cache()



					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								from transformers import TaForConditionalGeneration

								lowerCamelCase__	:	Optional[Any]				=TaForConditionalGeneration._keep_in_fpaa_modules
								lowerCamelCase__	:	List[Any]				=None

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

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




					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

								# test with `t5-small`
								lowerCamelCase__	:	Dict				=TaForConditionalGeneration.from_pretrained(self.model_name,						load_in_abit=_lowerCamelCase,						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__	:	Dict				=self.tokenizer(self.input_text,						return_tensors='''pt'''  ).to(0  )
								lowerCamelCase__	:	int				=model.generate(**_lowerCamelCase  )

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





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase__     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								super().setUp()
								# model_name
								lowerCamelCase__	:	Any				='''bigscience/bloom-560m'''
								lowerCamelCase__	:	List[Any]				='''t5-small'''

								# Different types of model

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



					def 	snake_case ( self							:						Any  )->      Union[str, 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 	snake_case ( self							:						Any  )->      str:
								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__     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								super().setUp()



					def 	snake_case ( self							:						int  )->      Dict:
								del self.pipe

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




					def 	snake_case ( self							:						int  )->      Union[str, Any]:
								lowerCamelCase__	:	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__	:	Dict				=self.pipe(self.input_text  )
								self.assertIn(pipeline_output[0]['''generated_text'''],						self.EXPECTED_OUTPUTS  )





@require_torch_multi_gpu
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase__     ):


					'''simple docstring'''





					def 	snake_case ( self							:						int  )->      Optional[Any]:
								super().setUp()




					def 	snake_case ( self							:						int  )->      Optional[Any]:
								lowerCamelCase__	:	Optional[Any]				=AutoModelForCausalLM.from_pretrained(
								    self.model_name,						load_in_abit=_lowerCamelCase,						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__	:	Dict				=self.tokenizer(self.input_text,						return_tensors='''pt'''  )

								# Second real batch
								lowerCamelCase__	:	List[str]				=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=_lowerCamelCase  ),						self.EXPECTED_OUTPUTS  )





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase__     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[Any]  )->      int:
								lowerCamelCase__	:	List[Any]				='''facebook/opt-350m'''
								super().setUp()




					def 	snake_case ( self							:						Any  )->      str:





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

								# Step 1: freeze all parameters
								lowerCamelCase__	:	Any				=AutoModelForCausalLM.from_pretrained(self.model_name,						load_in_abit=_lowerCamelCase  )

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

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

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

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

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
											lowerCamelCase__	:	Dict				=model.forward(**_lowerCamelCase  )
											out.logits.norm().backward()

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





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase__     ):


					'''simple docstring'''





					_a         =							"gpt2-xl"
					_a         =							3.3191854854152187









"""simple docstring"""






import colorsys

from PIL import Image  # type: ignore






def   snake_case__     (	__lowerCamelCase       :					float							,	__lowerCamelCase       :					float							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=x
			lowerCamelCase__	:	Any				=y
			for step in range(__lowerCamelCase			):  # noqa: B007
						lowerCamelCase__	:	List[Any]				=a * a - b * b + x
						lowerCamelCase__	:	Optional[int]				=2 * a * b + y
						lowerCamelCase__	:	Union[str, Any]				=a_new

						# divergence happens for all complex number with an absolute value
						# greater than 4
						if a * a + b * b > 4:
									break
			return step / (max_step - 1)






def   snake_case__     (	__lowerCamelCase       :					float			):
			"""simple docstring"""
			if distance == 1:
						return (0, 0, 0)
			else:
						return (255, 255, 255)






def   snake_case__     (	__lowerCamelCase       :					float			):
			"""simple docstring"""
			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(__lowerCamelCase							,	1							,	1			)			)






def   snake_case__     (	__lowerCamelCase       :					int = 800							,	__lowerCamelCase       :					int = 600							,	__lowerCamelCase       :					float = -0.6							,	__lowerCamelCase       :					float = 0							,	__lowerCamelCase       :					float = 3.2							,	__lowerCamelCase       :					int = 50							,	__lowerCamelCase       :					bool = True							,	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=Image.new('''RGB'''							,	(image_width, image_height)			)
			lowerCamelCase__	:	Optional[int]				=img.load()

			# loop through the image-coordinates
			for image_x in range(__lowerCamelCase			):
						for image_y in range(__lowerCamelCase			):
									# determine the figure-coordinates based on the image-coordinates
									lowerCamelCase__	:	Optional[Any]				=figure_width / image_width * image_height
									lowerCamelCase__	:	Dict				=figure_center_x + (image_x / image_width - 0.5) * figure_width
									lowerCamelCase__	:	Optional[int]				=figure_center_y + (image_y / image_height - 0.5) * figure_height

									lowerCamelCase__	:	Any				=get_distance(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												lowerCamelCase__	:	int				=get_color_coded_rgb(__lowerCamelCase			)
									else:
												lowerCamelCase__	:	Optional[int]				=get_black_and_white_rgb(__lowerCamelCase			)

			return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		_lowercase				:				Optional[Any]   		=		get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()




