

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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    List[Any]:
				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  ,   A_  ,   A_		):







															'''simple docstring'''


															super().__init__()
															_UpperCAmelCase    :						Optional[int]							=      module
															_UpperCAmelCase    :						Dict							=      nn.Sequential(
															    nn.Linear(module.in_features  ,   A_  ,   bias=A_		)  ,   nn.Linear(A_  ,   module.out_features  ,   bias=A_		)  ,   )
															_UpperCAmelCase    :						Dict							=      (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  ,   A_  ,   *A_  ,   **A_		):







															'''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
						_lowercase        =     "bigscience/bloom-1b7"

						# Constant values
						_lowercase        =     2.1_09_65_95_52_69_25_74

						_lowercase        =     "Hello my name is"
						_lowercase        =     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"			)
						_lowercase        =     1_0


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										super().setUp()

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										del self.model_fpaa
										del self.model_abit

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

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

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

										_UpperCAmelCase    :						int							=      config.to_json_string()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										from bitsandbytes.nn import Paramsabit

										_UpperCAmelCase    :						Any							=      self.model_fpaa.get_memory_footprint()
										_UpperCAmelCase    :						Tuple							=      self.model_abit.get_memory_footprint()

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


						def     _UpperCAmelCase			(    self		):







										'''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		):







										'''simple docstring'''


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







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      BitsAndBytesConfig()
										_UpperCAmelCase    :						List[str]							=      True

										_UpperCAmelCase    :						Optional[int]							=      AutoModelForCausalLM.from_pretrained(
										    self.model_name  ,   quantization_config=A_  ,   device_map="auto"		)

										_UpperCAmelCase    :						Dict							=      self.tokenizer(self.input_text  ,   return_tensors="pt"		)
										_UpperCAmelCase    :						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=A_		)  ,   self.EXPECTED_OUTPUTS		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      BitsAndBytesConfig()

										with self.assertRaises(A_		):
														_UpperCAmelCase    :						Dict							=      AutoModelForCausalLM.from_pretrained(
														    self.model_name  ,   quantization_config=A_  ,   load_in_abit=A_  ,   device_map="auto"  ,   bnb_abit_quant_type="nf4"  ,   )


						def     _UpperCAmelCase			(    self		):







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

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

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

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

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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      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		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										from transformers import TaForConditionalGeneration

										_UpperCAmelCase    :						Union[str, Any]							=      TaForConditionalGeneration._keep_in_fpaa_modules
										_UpperCAmelCase    :						Dict							=      None

										# test with `t5-small`
										_UpperCAmelCase    :						Dict							=      TaForConditionalGeneration.from_pretrained(self.model_name  ,   load_in_abit=A_  ,   device_map="auto"		)
										_UpperCAmelCase    :						Any							=      self.tokenizer(self.input_text  ,   return_tensors="pt"		).to(0		)
										_UpperCAmelCase    :						Any							=      model.generate(**A_		)

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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										_UpperCAmelCase    :						Union[str, Any]							=      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    :						int							=      self.tokenizer(self.input_text  ,   return_tensors="pt"		).to(0		)
										_UpperCAmelCase    :						List[Any]							=      model.generate(**A_		)

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







class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										# Different types of model

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


						def     _UpperCAmelCase			(    self		):







										'''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		):







										'''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					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										super().setUp()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										del self.pipe

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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







@require_torch_multi_gpu
class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										super().setUp()





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      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    :						Tuple							=      self.tokenizer(self.input_text  ,   return_tensors="pt"		)

										# Second real batch
										_UpperCAmelCase    :						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					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

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

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

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

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
														_UpperCAmelCase    :						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					(		UpperCAmelCase			):
						_lowercase        =     "gpt2-xl"
						_lowercase        =     3.31_91_85_48_54_15_21_87





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[int]							=      len(lowerCAmelCase	) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Dict							=      0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      dp[0][j - 2] if pattern[j - 1] == "*" else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1					, lowerCAmelCase	):
								for j in range(1					, lowerCAmelCase	):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase    :						List[str]							=      1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase    :						str							=      dp[i - 1][j]
																else:
																				_UpperCAmelCase    :						int							=      0
												else:
																_UpperCAmelCase    :						List[Any]							=      0

				return bool(dp[-1][-1]	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# inputing the strings
					# input_string = input("input a string :")
					# pattern = input("input a pattern :")

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = 'c*a*b'

					# using function to check whether given string matches the given pattern
					if match_pattern(input_string, pattern):
										print(F'''{input_string} matches the given pattern {pattern}''')
					else:
										print(F'''{input_string} does not match with the given pattern {pattern}''')


