






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_   (__A      )				->						Optional[Any]:



					"""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 							snake_case_       (    nn.Module      ):

									"""simple docstring"""



									def __init__(self:					Tuple ,					__UpperCAmelCase:					nn.Module ,					__UpperCAmelCase:					int   )    ->     Union[str, Any]:




														'''simple docstring'''



														super().__init__()
														__a					:						Any      =					module
														__a					:						Union[str, Any]      =					nn.Sequential(
														    nn.Linear(module.in_features ,					__UpperCAmelCase ,					bias=__UpperCAmelCase   ) ,					nn.Linear(__UpperCAmelCase ,					module.out_features ,					bias=__UpperCAmelCase   ) ,					)
														__a					:						Optional[int]      =					(2.0 / (5 * min(module.in_features ,					module.out_features   ))) ** 0.5
														nn.init.normal_(self.adapter[0].weight ,					std=__UpperCAmelCase   )
														nn.init.zeros_(self.adapter[1].weight   )
														self.adapter.to(module.weight.device   )



									def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					List[Any] ,					*__UpperCAmelCase:					int ,					**__UpperCAmelCase:					Tuple   )    ->     Union[str, Any]:




														'''simple docstring'''



														return self.module(__UpperCAmelCase ,					*__UpperCAmelCase ,					**__UpperCAmelCase   ) + self.adapter(__UpperCAmelCase   )




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

						"""simple docstring"""



						snake_case__        =      """bigscience/bloom-1b7"""

						# Constant values
						snake_case__        =      2.109_659_552_692_574

						snake_case__        =      """Hello my name is"""
						snake_case__        =      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"""      )
						snake_case__        =      10



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




											'''simple docstring'''



											__a					:						str      =					AutoTokenizer.from_pretrained(self.model_name   )




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



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




											'''simple docstring'''



											super().setUp()

											# Models and tokenizer
											__a					:						Optional[Any]      =					AutoModelForCausalLM.from_pretrained(
											    self.model_name ,					torch_dtype=torch.floataa ,					device_map="auto"   )
											__a					:						Any      =					AutoModelForCausalLM.from_pretrained(self.model_name ,					load_in_abit=__UpperCAmelCase ,					device_map="auto"   )



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											del self.model_fpaa
											del self.model_abit

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



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     str:




											'''simple docstring'''



											__a					:						Dict      =					self.model_abit.config

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

											__a					:						List[str]      =					config.to_dict()
											__a					:						str      =					config.to_diff_dict()

											__a					:						Optional[Any]      =					config.to_json_string()



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											from bitsandbytes.nn import Paramsabit

											__a					:						Union[str, Any]      =					self.model_fpaa.get_memory_footprint()
											__a					:						List[str]      =					self.model_abit.get_memory_footprint()

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



						def        UpperCAmelCase__					(self:					int   )    ->     Optional[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(__UpperCAmelCase ,					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        UpperCAmelCase__					(self:					Any   )    ->     Tuple:




											'''simple docstring'''



											__a					:						List[Any]      =					self.tokenizer(self.input_text ,					return_tensors="pt"   )
											__a					:						List[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=__UpperCAmelCase   ) ,					self.EXPECTED_OUTPUTS   )



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




											'''simple docstring'''



											__a					:						Optional[int]      =					BitsAndBytesConfig()
											__a					:						Dict      =					True

											__a					:						Union[str, Any]      =					AutoModelForCausalLM.from_pretrained(
											    self.model_name ,					quantization_config=__UpperCAmelCase ,					device_map="auto"   )

											__a					:						List[str]      =					self.tokenizer(self.input_text ,					return_tensors="pt"   )
											__a					:						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=__UpperCAmelCase   ) ,					self.EXPECTED_OUTPUTS   )



						def        UpperCAmelCase__					(self:					str   )    ->     Optional[Any]:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Tuple   )    ->     Dict:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					BitsAndBytesConfig()

											with self.assertRaises(__UpperCAmelCase   ):
																__a					:						Union[str, Any]      =					AutoModelForCausalLM.from_pretrained(
																    self.model_name ,					quantization_config=__UpperCAmelCase ,					load_in_abit=__UpperCAmelCase ,					device_map="auto" ,					bnb_abit_quant_type="nf4" ,					)



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




											'''simple docstring'''



											with self.assertRaises(__UpperCAmelCase   ):
																# Tries with `str`
																self.model_abit.to("cpu"   )

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

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

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

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

											# Test if we did not break anything
											__a					:						Optional[Any]      =					self.tokenizer(self.input_text ,					return_tensors="pt"   )

											__a					:						List[Any]      =					self.model_fpaa.to(torch.floataa   )
											__a					:						List[Any]      =					self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0   ) ,					max_new_tokens=10   )

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

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

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



						def        UpperCAmelCase__					(self:					str   )    ->     int:




											'''simple docstring'''



											__a					:						Tuple      =					AutoModelForSeqaSeqLM.from_pretrained("t5-small" ,					load_in_abit=__UpperCAmelCase ,					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 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@classmethod
						def        UpperCAmelCase__					(cls:					Any   )    ->     List[str]:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     Optional[Any]:




											'''simple docstring'''



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



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




											'''simple docstring'''



											from transformers import TaForConditionalGeneration

											__a					:						Tuple      =					TaForConditionalGeneration._keep_in_fpaa_modules
											__a					:						Union[str, Any]      =					None

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

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



						def        UpperCAmelCase__					(self:					Dict   )    ->     Optional[int]:




											'''simple docstring'''



											import bitsandbytes as bnb

											from transformers import TaForConditionalGeneration

											# test with `t5-small`
											__a					:						Optional[Any]      =					TaForConditionalGeneration.from_pretrained(self.model_name ,					load_in_abit=__UpperCAmelCase ,					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   )   )

											__a					:						Optional[Any]      =					self.tokenizer(self.input_text ,					return_tensors="pt"   ).to(0   )
											__a					:						Union[str, Any]      =					model.generate(**__UpperCAmelCase   )

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




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



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




											'''simple docstring'''



											super().setUp()
											# model_name
											__a					:						int      =					"bigscience/bloom-560m"
											__a					:						Optional[int]      =					"t5-small"

											# Different types of model

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



						def        UpperCAmelCase__					(self:					str   )    ->     Optional[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        UpperCAmelCase__					(self:					Dict   )    ->     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 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					int   )    ->     List[Any]:




											'''simple docstring'''



											super().setUp()



						def        UpperCAmelCase__					(self:					Dict   )    ->     Any:




											'''simple docstring'''



											del self.pipe

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



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




											'''simple docstring'''



											__a					:						Union[str, 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
											__a					:						Any      =					self.pipe(self.input_text   )
											self.assertIn(pipeline_output[0]["generated_text"] ,					self.EXPECTED_OUTPUTS   )




@require_torch_multi_gpu
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     Dict:




											'''simple docstring'''



											super().setUp()



						def        UpperCAmelCase__					(self:					str   )    ->     Tuple:




											'''simple docstring'''



											__a					:						Any      =					AutoModelForCausalLM.from_pretrained(
											    self.model_name ,					load_in_abit=__UpperCAmelCase ,					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
											__a					:						Optional[Any]      =					self.tokenizer(self.input_text ,					return_tensors="pt"   )

											# Second real batch
											__a					:						Dict      =					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=__UpperCAmelCase   ) ,					self.EXPECTED_OUTPUTS   )




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



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




											'''simple docstring'''



											__a					:						str      =					"facebook/opt-350m"
											super().setUp()



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




											'''simple docstring'''



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

											# Step 1: freeze all parameters
											__a					:						Optional[Any]      =					AutoModelForCausalLM.from_pretrained(self.model_name ,					load_in_abit=__UpperCAmelCase   )

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

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

        # Step 2: add adapters
											for _, module in model.named_modules():
																if "OPTAttention" in repr(type(__UpperCAmelCase   )   ):
																					__a					:						Optional[int]      =					LoRALayer(module.q_proj ,					rank=16   )
																					__a					:						Dict      =					LoRALayer(module.k_proj ,					rank=16   )
																					__a					:						Tuple      =					LoRALayer(module.v_proj ,					rank=16   )

        # Step 3: dummy batch
											__a					:						Optional[int]      =					self.tokenizer("Test batch " ,					return_tensors="pt"   ).to(0   )

											# Step 4: Check if the gradient is not None
											with torch.cuda.amp.autocast():
																__a					:						Optional[int]      =					model.forward(**__UpperCAmelCase   )
																out.logits.norm().backward()

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




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """gpt2-xl"""
						snake_case__        =      3.3_191_854_854_152_187













from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





