'''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       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""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 						A   (   nn.Module   ):





												def __init__(      self				:							Tuple     ,		__a				:							nn.Module     ,		__a				:							int	)							->	Optional[int]:
													super().__init__()
													__UpperCAmelCase							=			module
													__UpperCAmelCase							=			nn.Sequential(
													    nn.Linear(module.in_features     ,		__a     ,		bias=__a	)     ,		nn.Linear(__a     ,		module.out_features     ,		bias=__a	)     ,		)
													__UpperCAmelCase							=			(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				:							List[Any]     ,		__a				:							List[Any]     ,		*__a				:							Tuple     ,		**__a				:							Dict	)							->	str:
													return self.module(__a     ,		*__a     ,		**__a	) + self.adapter(__a	)






@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
							a_        =      '''bigscience/bloom-1b7'''

							# Constant values
							a_        =      2.109_659_552_692_574

							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				:							str	)							->	str:
								# Models and tokenizer
								__UpperCAmelCase							=			AutoTokenizer.from_pretrained(self.model_name	)






class 						A   (   UpperCAmelCase   ):





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

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





							def       snake_case__						(      self				:							str	)							->	Tuple:
								del self.model_fpaa
								del self.model_abit

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





							def       snake_case__						(      self				:							Optional[int]	)							->	List[str]:
								__UpperCAmelCase							=			self.model_abit.config

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

								__UpperCAmelCase							=			config.to_dict()
								__UpperCAmelCase							=			config.to_diff_dict()

								__UpperCAmelCase							=			config.to_json_string()





							def       snake_case__						(      self				:							Tuple	)							->	Any:
								from bitsandbytes.nn import Paramsabit

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

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





							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[Any]:
								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				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.tokenizer(self.input_text     ,		return_tensors='''pt'''	)
								__UpperCAmelCase							=			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       snake_case__						(      self				:							Union[str, Any]	)							->	str:
								__UpperCAmelCase							=			BitsAndBytesConfig()
								__UpperCAmelCase							=			True

								__UpperCAmelCase							=			AutoModelForCausalLM.from_pretrained(
								    self.model_name     ,		quantization_config=__a     ,		device_map='''auto'''	)

								__UpperCAmelCase							=			self.tokenizer(self.input_text     ,		return_tensors='''pt'''	)
								__UpperCAmelCase							=			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       snake_case__						(      self				:							Optional[Any]	)							->	str:
								with self.assertRaises(__a	), tempfile.TemporaryDirectory() as tmpdirname:
									self.model_abit.save_pretrained(__a	)





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								__UpperCAmelCase							=			BitsAndBytesConfig()

								with self.assertRaises(__a	):
									__UpperCAmelCase							=			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				:							List[Any]	)							->	Optional[int]:
								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
								__UpperCAmelCase							=			self.tokenizer(self.input_text     ,		return_tensors='''pt'''	)

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

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

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

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






							def       snake_case__						(      self				:							str	)							->	Dict:
								__UpperCAmelCase							=			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 						A   (   unittest.TestCase   ):





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





							def       snake_case__						(      self				:							Dict	)							->	str:
								gc.collect()
								torch.cuda.empty_cache()





							def       snake_case__						(      self				:							List[Any]	)							->	Dict:
								from transformers import TaForConditionalGeneration

								__UpperCAmelCase							=			TaForConditionalGeneration._keep_in_fpaa_modules
								__UpperCAmelCase							=			None

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

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






							def       snake_case__						(      self				:							str	)							->	List[Any]:
								import bitsandbytes as bnb

								from transformers import TaForConditionalGeneration

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

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

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






class 						A   (   UpperCAmelCase   ):





							def       snake_case__						(      self				:							str	)							->	List[Any]:
								super().setUp()
								# model_name
								__UpperCAmelCase							=			'''bigscience/bloom-560m'''
								__UpperCAmelCase							=			'''t5-small'''

								# Different types of model

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





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





							def       snake_case__						(      self				:							Optional[int]	)							->	Union[str, Any]:
								del self.pipe

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






							def       snake_case__						(      self				:							int	)							->	List[str]:
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			self.pipe(self.input_text	)
								self.assertIn(pipeline_output[0]['''generated_text''']     ,		self.EXPECTED_OUTPUTS	)






@require_torch_multi_gpu
class 						A   (   UpperCAmelCase   ):





							def       snake_case__						(      self				:							int	)							->	int:
								super().setUp()






							def       snake_case__						(      self				:							Dict	)							->	Any:
								__UpperCAmelCase							=			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
								__UpperCAmelCase							=			self.tokenizer(self.input_text     ,		return_tensors='''pt'''	)

								# Second real batch
								__UpperCAmelCase							=			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 						A   (   UpperCAmelCase   ):





							def       snake_case__						(      self				:							Union[str, Any]	)							->	int:
								__UpperCAmelCase							=			'''facebook/opt-350m'''
								super().setUp()






							def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
								if version.parse(importlib.metadata.version('''bitsandbytes'''	)	) < version.parse('''0.37.0'''	):
									return

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

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

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

								# Step 4: Check if the gradient is not None
								with torch.cuda.amp.autocast():
									__UpperCAmelCase							=			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 						A   (   UpperCAmelCase   ):
							a_        =      '''gpt2-xl'''
							a_        =      3.3_191_854_854_152_187

'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowerCAmelCase	:       str						       =	299_792_458

# Symbols
__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase       ,	__lowerCAmelCase	:       Any						       =	symbols("ct x y z")





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	if velocity > c:
		raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''       )
	elif velocity < 1:
		# Usually the speed should be much higher than 1 (c order of magnitude)
		raise ValueError('''Speed must be greater than or equal to 1!'''       )

	return velocity / c





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return 1 / sqrt(1 - beta(UpperCamelCase__       ) ** 2       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float       ):


	"""simple docstring"""
	return np.array(
	    [
	        [gamma(UpperCamelCase__       ), -gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), 0, 0],
	        [-gamma(UpperCamelCase__       ) * beta(UpperCamelCase__       ), gamma(UpperCamelCase__       ), 0, 0],
	        [0, 0, 1, 0],
	        [0, 0, 0, 1],
	    ]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   np.ndarray | None = None       ):


	"""simple docstring"""
	# Ensure event is not empty
	if event is None:
		__UpperCAmelCase							=			np.array([ct, x, y, z]       )  # Symbolic four vector
	else:
		event[0] *= c  # x0 is ct (speed of light * time)

	return transformation_matrix(UpperCamelCase__       ) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Example of symbolic vector:
					__lowerCAmelCase	:       Dict						       =	transform(29_979_245)
					print("Example of four vector: ")
					print(F"""ct' = {four_vector[0]}""")
					print(F"""x' = {four_vector[1]}""")
					print(F"""y' = {four_vector[2]}""")
					print(F"""z' = {four_vector[3]}""")

					# Substitute symbols with numerical values
					__lowerCAmelCase	:       Union[str, Any]						       =	{ct: c, x: 1, y: 1, z: 1}
					__lowerCAmelCase	:       Optional[int]						       =	[four_vector[i].subs(sub_dict) for i in range(4)]

					print(F"""\n{numerical_vector}""")
