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							:   Optional[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 		a__						(					nn.Module					):

							def __init__(    self      :       str,_A      :       nn.Module,_A      :       int    ):







									"""simple docstring"""






									super().__init__()
									SCREAMING_SNAKE_CASE_  :		str				      =    module
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    nn.Sequential(
									    nn.Linear(module.in_features,_A,bias=_A    ),nn.Linear(_A,module.out_features,bias=_A    ),)
									SCREAMING_SNAKE_CASE_  :		str				      =    (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 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Dict,*_A      :       int,**_A      :       Dict    ):







									"""simple docstring"""






									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_6595_5269_2574

					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	       =							10

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(self.model_name    )







class 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							super().setUp()

							# Models and tokenizer
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AutoModelForCausalLM.from_pretrained(
							    self.model_name,torch_dtype=torch.floataa,device_map="auto"    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    AutoModelForCausalLM.from_pretrained(self.model_name,load_in_abit=_A,device_map="auto"    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							del self.model_fpaa
							del self.model_abit

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

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.model_abit.config

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

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

							SCREAMING_SNAKE_CASE_  :		int				      =    config.to_json_string()

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							from bitsandbytes.nn import Paramsabit

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

							self.assertAlmostEqual(mem_fpaa / mem_abit,self.EXPECTED_RELATIVE_DIFFERENCE    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    get_some_linear_layer(self.model_abit    )
							self.assertTrue(linear.weight.__class__ == Paramsabit    )

					def 				__UpperCamelCase   (    self      :       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(_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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.tokenizer(self.input_text,return_tensors="pt"    )
							SCREAMING_SNAKE_CASE_  :		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=_A    ),self.EXPECTED_OUTPUTS    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		str				      =    AutoModelForCausalLM.from_pretrained(
							    self.model_name,quantization_config=_A,device_map="auto"    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.tokenizer(self.input_text,return_tensors="pt"    )
							SCREAMING_SNAKE_CASE_  :		int				      =    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=_A    ),self.EXPECTED_OUTPUTS    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    BitsAndBytesConfig()

							with self.assertRaises(_A    ):
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    AutoModelForCausalLM.from_pretrained(
									    self.model_name,quantization_config=_A,load_in_abit=_A,device_map="auto",bnb_abit_quant_type="nf4",)

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							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
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.tokenizer(self.input_text,return_tensors="pt"    )

							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_fpaa.to(torch.floataa    )
							SCREAMING_SNAKE_CASE_  :		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
							SCREAMING_SNAKE_CASE_  :		str				      =    self.model_fpaa.to("cpu"    )

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

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






					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    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 				__UpperCamelCase   (    cls      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							from transformers import TaForConditionalGeneration

							SCREAMING_SNAKE_CASE_  :		Any				      =    TaForConditionalGeneration._keep_in_fpaa_modules
							SCREAMING_SNAKE_CASE_  :		Dict				      =    None

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

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






					def 				__UpperCamelCase   (    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=_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    )    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.tokenizer(self.input_text,return_tensors="pt"    ).to(0    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model.generate(**_A    )

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







class 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






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

							# Different types of model

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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""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      :       Any    ):







							"""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 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().setUp()

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							del self.pipe

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






					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		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_  :		List[str]				      =    self.pipe(self.input_text    )
							self.assertIn(pipeline_output[0]["generated_text"],self.EXPECTED_OUTPUTS    )







@require_torch_multi_gpu
class 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							super().setUp()






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.tokenizer(self.input_text,return_tensors="pt"    )

							# Second real batch
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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=_A    ),self.EXPECTED_OUTPUTS    )







class 		a__						(					A__					):

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    "facebook/opt-350m"
							super().setUp()






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






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

							# Step 1: freeze all parameters
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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():
									SCREAMING_SNAKE_CASE_  :		Any				      =    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_  :		List[Any]				      =    param.data.to(torch.floataa    )

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

        # Step 3: dummy batch
							SCREAMING_SNAKE_CASE_  :		int				      =    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_  :		int				      =    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__						(					A__					):
					A	       =							'gpt2-xl'
					A	       =							3.3191_8548_5415_2187




from __future__ import annotations

UpperCAmelCase_      : Tuple  					=       []







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int  ,    __A :			int				)	->		bool:

       """simple docstring"""

       for i in range(len(__A				)				):
              if board[row][i] == 1:
                     return False
       for i in range(len(__A				)				):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    -1  ,    -1				)				):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    len(__A				)				)				):
              if board[i][j] == 1:
                     return False
       return True







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int				)	->		bool:

       """simple docstring"""

       if row >= len(__A				):
              solution.append(__A				)
              printboard(__A				)
              print()
              return True
       for i in range(len(__A				)				):
              if is_safe(__A  ,    __A  ,    __A				):
                     a_					:		Any							=  1
                     solve(__A  ,    row + 1				)
                     a_					:		Tuple							=  0
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]				)	->		None:

       """simple docstring"""

       for i in range(len(__A				)				):
              for j in range(len(__A				)				):
                     if board[i][j] == 1:
                            print('Q'  ,    end=' '				)
                     else:
                            print('.'  ,    end=' '				)
              print()


# n=int(input("The no. of queens"))
UpperCAmelCase_      : List[str]  					=       8
UpperCAmelCase_      : str  					=       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


