

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      (						A    ):
	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 						__UpperCamelCase    (					nn.Module						):




					def __init__(		self	:						int		,	lowerCAmelCase	:						nn.Module		,	lowerCAmelCase	:						int							):


						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_       				=			module
						UpperCAmelCase_       				=			nn.Sequential(
						    nn.Linear(module.in_features		,	lowerCAmelCase		,	bias=lowerCAmelCase							)		,	nn.Linear(lowerCAmelCase		,	module.out_features		,	bias=lowerCAmelCase							)		,	)
						UpperCAmelCase_       				=			(2.0 / (5 * min(module.in_features		,	module.out_features							))) ** 0.5
						nn.init.normal_(self.adapter[0].weight		,	std=lowerCAmelCase							)
						nn.init.zeros_(self.adapter[1].weight							)
						self.adapter.to(module.weight.device							)




					def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[Any]		,	*lowerCAmelCase	:						List[str]		,	**lowerCAmelCase	:						int							):


						'''simple docstring'''







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

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						__UpperCamelCase    (					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
	SCREAMING_SNAKE_CASE__    				=     'bigscience/bloom-1b7'

	# Constant values
	SCREAMING_SNAKE_CASE__    				=     2.1_09_65_95_52_69_25_74

	SCREAMING_SNAKE_CASE__    				=     'Hello my name is'
	SCREAMING_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'						)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			AutoTokenizer.from_pretrained(self.model_name							)


class 						__UpperCamelCase    (					lowercase						):




	def       __A					(		self	:						int							):


		'''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=lowerCAmelCase		,	device_map="auto"							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		del self.model_fpaa
		del self.model_abit

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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_abit.config

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

		UpperCAmelCase_       				=			config.to_dict()
		UpperCAmelCase_       				=			config.to_diff_dict()

		UpperCAmelCase_       				=			config.to_json_string()




	def       __A					(		self	:						List[str]							):


		'''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       __A					(		self	:						List[Any]							):


		'''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(lowerCAmelCase		,	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       __A					(		self	:						str							):


		'''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=lowerCAmelCase							)		,	self.EXPECTED_OUTPUTS							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			BitsAndBytesConfig()
		UpperCAmelCase_       				=			True

		UpperCAmelCase_       				=			AutoModelForCausalLM.from_pretrained(
		    self.model_name		,	quantization_config=lowerCAmelCase		,	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=lowerCAmelCase							)		,	self.EXPECTED_OUTPUTS							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







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




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


		'''simple docstring'''







		UpperCAmelCase_       				=			BitsAndBytesConfig()

		with self.assertRaises(lowerCAmelCase							):
			UpperCAmelCase_       				=			AutoModelForCausalLM.from_pretrained(
			    self.model_name		,	quantization_config=lowerCAmelCase		,	load_in_abit=lowerCAmelCase		,	device_map="auto"		,	bnb_abit_quant_type="nf4"		,	)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







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

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

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

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

		with self.assertRaises(lowerCAmelCase							):
			# 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       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			AutoModelForSeqaSeqLM.from_pretrained("t5-small"		,	load_in_abit=lowerCAmelCase		,	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 						__UpperCamelCase    (					unittest.TestCase						):




	@classmethod
	def       __A					(		cls	:						Dict							):


		'''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       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Tuple							):


		'''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=lowerCAmelCase		,	device_map="auto"							)
		UpperCAmelCase_       				=			self.tokenizer(self.input_text		,	return_tensors="pt"							).to(0							)
		UpperCAmelCase_       				=			model.generate(**lowerCAmelCase							)

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




	def       __A					(		self	:						List[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=lowerCAmelCase		,	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(**lowerCAmelCase							)

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


class 						__UpperCamelCase    (					lowercase						):




	def       __A					(		self	:						Any							):


		'''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=lowerCAmelCase		,	device_map="auto"							)
		# Sequence classification model
		UpperCAmelCase_       				=			AutoModelForSequenceClassification.from_pretrained(
		    self.model_name		,	load_in_abit=lowerCAmelCase		,	device_map="auto"							)
		# CausalLM model
		UpperCAmelCase_       				=			AutoModelForCausalLM.from_pretrained(self.model_name		,	load_in_abit=lowerCAmelCase		,	device_map="auto"							)
		# Seq2seq model
		UpperCAmelCase_       				=			AutoModelForSeqaSeqLM.from_pretrained(
		    self.seq_to_seq_name		,	load_in_abit=lowerCAmelCase		,	device_map="auto"							)




	def       __A					(		self	:						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       __A					(		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 						__UpperCamelCase    (					lowercase						):




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		super().setUp()




	def       __A					(		self	:						int							):


		'''simple docstring'''







		del self.pipe

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




	def       __A					(		self	:						List[Any]							):


		'''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 						__UpperCamelCase    (					lowercase						):




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		super().setUp()




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			AutoModelForCausalLM.from_pretrained(
		    self.model_name		,	load_in_abit=lowerCAmelCase		,	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=lowerCAmelCase							)		,	self.EXPECTED_OUTPUTS							)


class 						__UpperCamelCase    (					lowercase						):




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"facebook/opt-350m"
		super().setUp()




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


		'''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=lowerCAmelCase							)

		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(lowerCAmelCase							)							):
				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(**lowerCAmelCase							)
			out.logits.norm().backward()

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


class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'gpt2-xl'
	SCREAMING_SNAKE_CASE__    				=     3.31_91_85_48_54_15_21_87


def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			generate_pascal_triangle(A    )
	for row_idx in range(A    ):
		# Print left spaces
		for _ in range(num_rows - row_idx - 1    ):
			print(end=" "    )
		# Print row values
		for col_idx in range(row_idx + 1    ):
			if col_idx != row_idx:
				print(triangle[row_idx][col_idx]       ,     end=" "    )
			else:
				print(triangle[row_idx][col_idx]       ,     end=""    )
		print()



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[]
	for current_row_idx in range(A    ):
		UpperCAmelCase_       				=			populate_current_row(A       ,     A    )
		triangle.append(A    )
	return triangle



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			[-1] * (current_row_idx + 1)
	# first and last elements of current row are equal to 1
	UpperCAmelCase_				,   UpperCAmelCase_       				=			1, 1
	for current_col_idx in range(1       ,     A    ):
		calculate_current_element(
		    A       ,     A       ,     A       ,     A    )
	return current_row



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     ):
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx - 1]
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx]
	UpperCAmelCase_       				=			above_to_left_elt + above_to_right_elt



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[[1]]

	for row_index in range(1       ,     A    ):
		UpperCAmelCase_       				=			[0] + result[-1] + [0]
		UpperCAmelCase_       				=			row_index + 1
		# Calculate the number of distinct elements in a row
		UpperCAmelCase_       				=			sum(divmod(A       ,     2    )    )
		UpperCAmelCase_       				=			[
		    temp_row[i - 1] + temp_row[i] for i in range(1       ,     distinct_elements + 1    )
		]
		UpperCAmelCase_       				=			row_first_half[: (row_index + 1) // 2]
		row_second_half.reverse()
		UpperCAmelCase_       				=			row_first_half + row_second_half
		result.append(A    )

	return result



def       __lowerCAmelCase      (						):
	from collections.abc import Callable
	from timeit import timeit

	def benchmark_a_function(A       ,     A    ) -> None:
		UpperCAmelCase_       				=			F"{func.__name__}({value})"
		UpperCAmelCase_       				=			timeit(F"__main__.{call}"       ,     setup="import __main__"    )
		# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
		print(F"{call:38} -- {timing:.4f} seconds"    )

	for value in range(15    ):  # (1, 7, 14):
		for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
			benchmark_a_function(A       ,     A    )
		print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()