




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
			SCREAMING_SNAKE_CASE           =					Image.open(requests.get(SCREAMING_SNAKE_CASE					,	stream=SCREAMING_SNAKE_CASE   ).raw   ).convert("""RGB"""   )

			return image
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]
			# fmt: off

			# vision encoder
			rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")   )
			rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")   )
			rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")   )
			rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")   )
			rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")   )
			rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")   )

			for i in range(config.vision_config.num_hidden_layers   ):
						rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")   )
						rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")   )

			# QFormer
			rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""")   )
			rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""")   )

			# fmt: on
			return rename_keys
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dct.pop(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					val
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			for i in range(config.vision_config.num_hidden_layers   ):
						# read in original q and v biases
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""   )
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""   )

						# next, set bias in the state dict
						SCREAMING_SNAKE_CASE           =					torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE					,	requires_grad=SCREAMING_SNAKE_CASE   ), v_bias)   )
						SCREAMING_SNAKE_CASE           =					qkv_bias
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					3_64 if """coco""" in model_name else 2_24
			SCREAMING_SNAKE_CASE           =					BlipaVisionConfig(image_size=SCREAMING_SNAKE_CASE   ).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "opt-2.7b" in model_name:
						SCREAMING_SNAKE_CASE           =					OPTConfig.from_pretrained("""facebook/opt-2.7b"""					,	eos_token_id=SCREAMING_SNAKE_CASE   ).to_dict()
			elif "opt-6.7b" in model_name:
						SCREAMING_SNAKE_CASE           =					OPTConfig.from_pretrained("""facebook/opt-6.7b"""					,	eos_token_id=SCREAMING_SNAKE_CASE   ).to_dict()
			elif "t5-xl" in model_name:
						SCREAMING_SNAKE_CASE           =					TaConfig.from_pretrained("""google/flan-t5-xl"""					,	dense_act_fn="""gelu"""					,	bos_token_id=1   ).to_dict()
			elif "t5-xxl" in model_name:
						SCREAMING_SNAKE_CASE           =					TaConfig.from_pretrained("""google/flan-t5-xxl"""					,	dense_act_fn="""gelu"""					,	bos_token_id=1   ).to_dict()

			SCREAMING_SNAKE_CASE           =					BlipaConfig(vision_config=SCREAMING_SNAKE_CASE					,	text_config=SCREAMING_SNAKE_CASE   )

			return config, image_size
@torch.no_grad()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					(
			    AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""   )
			    if """opt""" in model_name
			    else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""   )
			)
			SCREAMING_SNAKE_CASE           =					tokenizer("""\n"""					,	add_special_tokens=SCREAMING_SNAKE_CASE   ).input_ids[0]
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_blipa_config(SCREAMING_SNAKE_CASE					,	eos_token_id=SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE   ).eval()

			SCREAMING_SNAKE_CASE           =					{
			    """blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
			    """blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
			    """blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
			    """blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
			    """blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
			    """blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
			    """blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
			}

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model_name_to_original[model_name]

			# load original model
			print("""Loading original model..."""   )
			SCREAMING_SNAKE_CASE           =					"""cuda""" if torch.cuda.is_available() else """cpu"""
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					load_model_and_preprocess(
			    name=SCREAMING_SNAKE_CASE					,	model_type=SCREAMING_SNAKE_CASE					,	is_eval=SCREAMING_SNAKE_CASE					,	device=SCREAMING_SNAKE_CASE   )
			original_model.eval()
			print("""Done!"""   )

			# update state dict keys
			SCREAMING_SNAKE_CASE           =					original_model.state_dict()
			SCREAMING_SNAKE_CASE           =					create_rename_keys(SCREAMING_SNAKE_CASE   )
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						SCREAMING_SNAKE_CASE           =					state_dict.pop(SCREAMING_SNAKE_CASE   )
						if key.startswith("""Qformer.bert"""   ):
									SCREAMING_SNAKE_CASE           =					key.replace("""Qformer.bert"""					,	"""qformer"""   )
						if "attention.self" in key:
									SCREAMING_SNAKE_CASE           =					key.replace("""self"""					,	"""attention"""   )
						if "opt_proj" in key:
									SCREAMING_SNAKE_CASE           =					key.replace("""opt_proj"""					,	"""language_projection"""   )
						if "t5_proj" in key:
									SCREAMING_SNAKE_CASE           =					key.replace("""t5_proj"""					,	"""language_projection"""   )
						if key.startswith("""opt"""   ):
									SCREAMING_SNAKE_CASE           =					key.replace("""opt"""					,	"""language"""   )
						if key.startswith("""t5"""   ):
									SCREAMING_SNAKE_CASE           =					key.replace("""t5"""					,	"""language"""   )
						SCREAMING_SNAKE_CASE           =					val

			# read in qv biases
			read_in_q_v_bias(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					hf_model.load_state_dict(SCREAMING_SNAKE_CASE					,	strict=SCREAMING_SNAKE_CASE   )
			assert len(SCREAMING_SNAKE_CASE   ) == 0
			assert unexpected_keys == ["qformer.embeddings.position_ids"]

			SCREAMING_SNAKE_CASE           =					load_demo_image()
			SCREAMING_SNAKE_CASE           =					vis_processors["""eval"""](SCREAMING_SNAKE_CASE   ).unsqueeze(0   ).to(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					tokenizer(["""\n"""]					,	return_tensors="""pt"""   ).input_ids.to(SCREAMING_SNAKE_CASE   )

			# create processor
			SCREAMING_SNAKE_CASE           =					BlipImageProcessor(
			    size={"""height""": image_size, """width""": image_size}					,	image_mean=SCREAMING_SNAKE_CASE					,	image_std=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE					,	tokenizer=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					processor(images=SCREAMING_SNAKE_CASE					,	return_tensors="""pt"""   ).pixel_values.to(SCREAMING_SNAKE_CASE   )

			# make sure processor creates exact same pixel values
			assert torch.allclose(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			original_model.to(SCREAMING_SNAKE_CASE   )
			hf_model.to(SCREAMING_SNAKE_CASE   )
			with torch.no_grad():
						if "opt" in model_name:
									SCREAMING_SNAKE_CASE           =					original_model({"""image""": original_pixel_values, """text_input""": [""""""]}   ).logits
									SCREAMING_SNAKE_CASE           =					hf_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ).logits
						else:
									SCREAMING_SNAKE_CASE           =					original_model(
									    {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}   ).logits
									SCREAMING_SNAKE_CASE           =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,	-1_00   )
									SCREAMING_SNAKE_CASE           =					hf_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	labels=SCREAMING_SNAKE_CASE   ).logits

			assert original_logits.shape == logits.shape
			print("""First values of original logits:"""					,	original_logits[0, :3, :3]   )
			print("""First values of HF logits:"""					,	logits[0, :3, :3]   )

			# assert values
			if model_name == "blip2-flan-t5-xl":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]					,	device=SCREAMING_SNAKE_CASE   )
						assert torch.allclose(logits[0, :3, :3]					,	SCREAMING_SNAKE_CASE					,	atol=1E-4   )
			elif model_name == "blip2-flan-t5-xl-coco":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]					,	device=SCREAMING_SNAKE_CASE   )
			else:
						# cast to same type
						SCREAMING_SNAKE_CASE           =					logits.dtype
						assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE   )					,	SCREAMING_SNAKE_CASE					,	atol=1E-2   )
			print("""Looks ok!"""   )

			print("""Generating a caption..."""   )
			SCREAMING_SNAKE_CASE           =					""""""
			SCREAMING_SNAKE_CASE           =					tokenizer(SCREAMING_SNAKE_CASE					,	return_tensors="""pt"""   ).input_ids.to(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					original_model.generate({"""image""": original_pixel_values}   )
			SCREAMING_SNAKE_CASE           =					hf_model.generate(
			    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	do_sample=SCREAMING_SNAKE_CASE					,	num_beams=5					,	max_length=30					,	min_length=1					,	top_p=0.9					,	repetition_penalty=1.0					,	length_penalty=1.0					,	temperature=1					,	)
			print("""Original generation:"""					,	SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					input_ids.shape[1]
			SCREAMING_SNAKE_CASE           =					processor.batch_decode(outputs[:, prompt_length:]					,	skip_special_tokens=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					[text.strip() for text in output_text]
			print("""HF generation:"""					,	SCREAMING_SNAKE_CASE   )

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(SCREAMING_SNAKE_CASE   )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE   )

			if push_to_hub:
						processor.push_to_hub(f"""nielsr/{model_name}"""   )
						hf_model.push_to_hub(f"""nielsr/{model_name}"""   )


if __name__ == "__main__":
		__A						:							str							=	argparse.ArgumentParser()
		__A						:							Optional[Any]							=	[
		    """blip2-opt-2.7b""",
		    """blip2-opt-6.7b""",
		    """blip2-opt-2.7b-coco""",
		    """blip2-opt-6.7b-coco""",
		    """blip2-flan-t5-xl""",
		    """blip2-flan-t5-xl-coco""",
		    """blip2-flan-t5-xxl""",
		]
		parser.add_argument(
		    """--model_name""",
		    default="""blip2-opt-2.7b""",
		    choices=choices,
		    type=str,
		    help="""Path to hf config.json of model to convert""",
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument(
		    """--push_to_hub""",
		    action="""store_true""",
		    help="""Whether to push the model and processor to the hub after converting""",
		)

		__A						:							Union[str, Any]							=	parser.parse_args()

		convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












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_    (    SCREAMING_SNAKE_CASE   ):
			'''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 			UpperCAmelCase_      (   nn.Module       ):




									'''simple docstring'''



									def __init__(					self       :	Optional[int]       ,     a       :	nn.Module       ,     a       :	int			)			->					Optional[Any]:
												super().__init__()
												SCREAMING_SNAKE_CASE           =					module
												SCREAMING_SNAKE_CASE           =					nn.Sequential(
												    nn.Linear(module.in_features       ,     a       ,     bias=a			)       ,     nn.Linear(a       ,     module.out_features       ,     bias=a			)       ,     )
												SCREAMING_SNAKE_CASE           =					(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       :	Tuple       ,     a       :	str       ,     *a       :	List[Any]       ,     **a       :	List[Any]			)			->					List[Any]:
												return self.module(a       ,     *a       ,     **a			) + self.adapter(a			)




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




							'''simple docstring'''



							a__						  =     '''bigscience/bloom-1b7'''

							# Constant values
							a__						  =     2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

							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__						  =     1_0





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(self.model_name			)





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										super().setUp()

										# Models and tokenizer
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     torch_dtype=torch.floataa       ,     device_map="""auto"""			)
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										del self.model_fpaa
										del self.model_abit

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





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_abit.config

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

										SCREAMING_SNAKE_CASE           =					config.to_dict()
										SCREAMING_SNAKE_CASE           =					config.to_diff_dict()

										SCREAMING_SNAKE_CASE           =					config.to_json_string()





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										from bitsandbytes.nn import Paramsabit

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.get_memory_footprint()
										SCREAMING_SNAKE_CASE           =					self.model_abit.get_memory_footprint()

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





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[Any]:
										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       :	Dict			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					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       :	str			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()
										SCREAMING_SNAKE_CASE           =					True

										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(
										    self.model_name       ,     quantization_config=a       ,     device_map="""auto"""			)

										SCREAMING_SNAKE_CASE           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)
										SCREAMING_SNAKE_CASE           =					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       :	str			)			->					Optional[int]:
										with self.assertRaises(a			), tempfile.TemporaryDirectory() as tmpdirname:
													self.model_abit.save_pretrained(a			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					BitsAndBytesConfig()

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





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

										SCREAMING_SNAKE_CASE           =					self.model_fpaa.to(torch.floataa			)
										SCREAMING_SNAKE_CASE           =					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           =					self.model_fpaa.to("""cpu"""			)

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.half()

										# Check this does not throw an error
										SCREAMING_SNAKE_CASE           =					self.model_fpaa.float()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



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





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										gc.collect()
										torch.cuda.empty_cache()





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										from transformers import TaForConditionalGeneration

										SCREAMING_SNAKE_CASE           =					TaForConditionalGeneration._keep_in_fpaa_modules
										SCREAMING_SNAKE_CASE           =					None

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

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







							def    _UpperCAmelCase     (					self       :	int			)			->					int:
										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										SCREAMING_SNAKE_CASE           =					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           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			).to(0			)
										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

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





class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					List[str]:
										super().setUp()
										# model_name
										SCREAMING_SNAKE_CASE           =					"""bigscience/bloom-560m"""
										SCREAMING_SNAKE_CASE           =					"""t5-small"""

										# Different types of model

										SCREAMING_SNAKE_CASE           =					AutoModel.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Sequence classification model
										SCREAMING_SNAKE_CASE           =					AutoModelForSequenceClassification.from_pretrained(
										    self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# CausalLM model
										SCREAMING_SNAKE_CASE           =					AutoModelForCausalLM.from_pretrained(self.model_name       ,     load_in_abit=a       ,     device_map="""auto"""			)
										# Seq2seq model
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name       ,     load_in_abit=a       ,     device_map="""auto"""			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Tuple:
										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			)			->					List[Any]:
										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_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					str:
										super().setUp()





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Optional[Any]:
										del self.pipe

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







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					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           =					self.pipe(self.input_text			)
										self.assertIn(pipeline_output[0]["""generated_text"""]       ,     self.EXPECTED_OUTPUTS			)





@require_torch_multi_gpu
class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Dict			)			->					Optional[int]:
										super().setUp()







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					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           =					self.tokenizer(self.input_text       ,     return_tensors="""pt"""			)

										# Second real batch
										SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""facebook/opt-350m"""
										super().setUp()







							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										if version.parse(importlib.metadata.version("""bitsandbytes"""			)			) < version.parse("""0.37.0"""			):
													return

										# Step 1: freeze all parameters
										SCREAMING_SNAKE_CASE           =					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           =					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           =					param.data.to(torch.floataa			)

        # Step 2: add adapters
										for _, module in model.named_modules():
													if "OPTAttention" in repr(type(a			)			):
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.q_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.k_proj       ,     rank=16			)
																SCREAMING_SNAKE_CASE           =					LoRALayer(module.v_proj       ,     rank=16			)

        # Step 3: dummy batch
										SCREAMING_SNAKE_CASE           =					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           =					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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''gpt2-xl'''
							a__						  =     3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7






