
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	( lowercase			)       ->  Tuple:
		'''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     lowercase (		nn.Module       ):



								def __init__(			self    ,				A_    ,				A_	)  -> List[str]:
										"""simple docstring"""
										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 __UpperCamelCase			(			self    ,				A_    ,				*A_    ,				**A_	)  -> Dict:
										"""simple docstring"""
										return self.module(A_    ,				*A_    ,				**A_	) + self.adapter(A_	)


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     lowercase (		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	:	List[str]     					=    "bigscience/bloom-1b7"

		# Constant values
		__lowercase	:	List[str]     					=    2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

		__lowercase	:	List[Any]     					=    "Hello my name is"
		__lowercase	:	str     					=    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	:	Tuple     					=    10



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					AutoTokenizer.from_pretrained(self.model_name	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				del self.model_fpaa
				del self.model_abit

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



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				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 __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"""
				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=10	)

				self.assertIn(self.tokenizer.decode(output_sequences[0]    ,				skip_special_tokens=A_	)    ,				self.EXPECTED_OUTPUTS	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				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=10	)

				self.assertIn(self.tokenizer.decode(output_sequences[0]    ,				skip_special_tokens=A_	)    ,				self.EXPECTED_OUTPUTS	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				with self.assertRaises(A_	), tempfile.TemporaryDirectory() as tmpdirname:
						self.model_abit.save_pretrained(A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> 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
				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=10	)

				# 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 __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				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     lowercase (		unittest.TestCase       ):



		@classmethod
		def __UpperCamelCase			(			cls	)  -> str:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				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 __UpperCamelCase			(			self	)  -> str:
				"""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	)  -> Optional[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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				del self.pipe

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



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				super().setUp()



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				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=10	)
				self.assertIn(self.tokenizer.decode(output_parallel[0]    ,				skip_special_tokens=A_	)    ,				self.EXPECTED_OUTPUTS	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					'facebook/opt-350m'
				super().setUp()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				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=16	)
								UpperCamelCase          =					LoRALayer(module.k_proj    ,				rank=16	)
								UpperCamelCase          =					LoRALayer(module.v_proj    ,				rank=16	)

        # 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     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Dict     					=    "gpt2-xl"
		__lowercase	:	int     					=    3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7


from __future__ import annotations


class     lowercase :



		def __init__(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					text, pattern
				UpperCamelCase     ,  UpperCamelCase          =					len(A_	), len(A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if char == self.pattern[i]:
								return i
				return -1



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""

				for i in range(self.patLen - 1    ,				-1    ,				-1	):
						if self.pattern[i] != self.text[current_pos + i]:
								return current_pos + i
				return -1



		def __UpperCamelCase			(			self	)  -> list[int]:
				"""simple docstring"""
				# searches pattern in text and returns index positions
				UpperCamelCase          =					[]
				for i in range(self.textLen - self.patLen + 1	):
						UpperCamelCase          =					self.mismatch_in_text(A_	)
						if mismatch_index == -1:
								positions.append(A_	)
						else:
								UpperCamelCase          =					self.match_in_pattern(self.text[mismatch_index]	)
								UpperCamelCase          =					(
								    mismatch_index - match_index
								)  # shifting index lgtm [py/multiple-definition]
				return positions






_UpperCAmelCase :			Union[str, Any]              =	"ABAABA"
_UpperCAmelCase :			Any              =	"AB"
_UpperCAmelCase :			Dict              =	BoyerMooreSearch(text, pattern)
_UpperCAmelCase :			Optional[int]              =	bms.bad_character_heuristic()

if len(positions) == 0:
					print("No match found")
else:
					print("Pattern found in following positions: ")
					print(positions)
