





"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__     			=			logging.get_logger(__name__)


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if "resnet-50" in model_name:
				UpperCamelCase   =	ResNetConfig.from_pretrained("microsoft/resnet-50"    )
		elif "resnet-101" in model_name:
				UpperCamelCase   =	ResNetConfig.from_pretrained("microsoft/resnet-101"    )
		else:
				raise ValueError("Model name should include either resnet50 or resnet101"    )

		UpperCamelCase   =	DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE  ,	backbone_config=_SCREAMING_SNAKE_CASE    )

		# set label attributes
		UpperCamelCase   =	"panoptic" in model_name
		if is_panoptic:
				UpperCamelCase   =	250
		else:
				UpperCamelCase   =	91
				UpperCamelCase   =	"huggingface/label-files"
				UpperCamelCase   =	"coco-detection-id2label.json"
				UpperCamelCase   =	json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"    )  ,	"r"    )    )
				UpperCamelCase   =	{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
				UpperCamelCase   =	idalabel
				UpperCamelCase   =	{v: k for k, v in idalabel.items()}

		return config, is_panoptic


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]

		# stem
		# fmt: off
		rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")    )
		rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")    )
		rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")    )
		rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")    )
		rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")    )
		# stages
		for stage_idx in range(len(config.backbone_config.depths    )    ):
				for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
								    )    )
						# 3 convs
						for i in range(3    ):
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
								    )    )
								rename_keys.append(
								    (
								        F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
								        F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
								    )    )
    # fmt: on

		for i in range(config.encoder_layers    ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (
				        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
				        F"encoder.layers.{i}.self_attn.out_proj.weight",
				    )    )
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")    )
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")    )
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")    )
				rename_keys.append(
				    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")    )
				rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")    )
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
				        F"decoder.layers.{i}.self_attn.out_proj.weight",
				    )    )
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")    )
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
				        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
				    )    )
				rename_keys.append(
				    (
				        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
				        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
				    )    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")    )
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")    )
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")    )
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")    )
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")    )
				rename_keys.append(
				    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")    )
				rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")    )

		# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
		rename_keys.extend(
		    [
		        ("input_proj.weight", "input_projection.weight"),
		        ("input_proj.bias", "input_projection.bias"),
		        ("query_embed.weight", "query_position_embeddings.weight"),
		        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
		        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
		        ("class_embed.weight", "class_labels_classifier.weight"),
		        ("class_embed.bias", "class_labels_classifier.bias"),
		        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
		        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
		        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
		        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
		        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
		        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
		    ]    )

		return rename_keys


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	state_dict.pop(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	val


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


		UpperCamelCase   =	""
		if is_panoptic:
				UpperCamelCase   =	"detr."

		# first: transformer encoder
		for i in range(6    ):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				UpperCamelCase   =	state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
				UpperCamelCase   =	state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"    )
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase   =	in_proj_weight[:256, :]
				UpperCamelCase   =	in_proj_bias[:256]
				UpperCamelCase   =	in_proj_weight[256:512, :]
				UpperCamelCase   =	in_proj_bias[256:512]
				UpperCamelCase   =	in_proj_weight[-256:, :]
				UpperCamelCase   =	in_proj_bias[-256:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6    ):
				# read in weights + bias of input projection layer of self-attention
				UpperCamelCase   =	state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
				UpperCamelCase   =	state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"    )
				# next, add query, keys and values (in that order) to the state dict
				UpperCamelCase   =	in_proj_weight[:256, :]
				UpperCamelCase   =	in_proj_bias[:256]
				UpperCamelCase   =	in_proj_weight[256:512, :]
				UpperCamelCase   =	in_proj_bias[256:512]
				UpperCamelCase   =	in_proj_weight[-256:, :]
				UpperCamelCase   =	in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				UpperCamelCase   =	state_dict.pop(
				    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"    )
				UpperCamelCase   =	state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"    )
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				UpperCamelCase   =	in_proj_weight_cross_attn[:256, :]
				UpperCamelCase   =	in_proj_bias_cross_attn[:256]
				UpperCamelCase   =	in_proj_weight_cross_attn[256:512, :]
				UpperCamelCase   =	in_proj_bias_cross_attn[256:512]
				UpperCamelCase   =	in_proj_weight_cross_attn[-256:, :]
				UpperCamelCase   =	in_proj_bias_cross_attn[-256:]


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	"http://images.cocodataset.org/val2017/000000039769.jpg"
		UpperCamelCase   =	Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE    ).raw    )

		return im


@torch.no_grad()
def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	get_detr_config(_SCREAMING_SNAKE_CASE    )

		# load original model from torch hub
		UpperCamelCase   =	{
		    "detr-resnet-50": "detr_resnet50",
		    "detr-resnet-101": "detr_resnet101",
		}
		logger.info(F"Converting model {model_name}..."    )
		UpperCamelCase   =	torch.hub.load("facebookresearch/detr"  ,	model_name_to_original_name[model_name]  ,	pretrained=_SCREAMING_SNAKE_CASE    ).eval()
		UpperCamelCase   =	detr.state_dict()
		# rename keys
		for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE    ):
				if is_panoptic:
						UpperCamelCase   =	"detr." + src
				rename_key(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		# query, key and value matrices need special treatment
		read_in_q_k_v(_SCREAMING_SNAKE_CASE  ,	is_panoptic=_SCREAMING_SNAKE_CASE    )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		UpperCamelCase   =	"detr.model." if is_panoptic else "model."
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith("detr"    )
						    and not key.startswith("class_labels_classifier"    )
						    and not key.startswith("bbox_predictor"    )
						):
								UpperCamelCase   =	state_dict.pop(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								UpperCamelCase   =	state_dict.pop(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	val
						elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
								continue
						else:
								UpperCamelCase   =	state_dict.pop(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	val
				else:
						if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
								UpperCamelCase   =	state_dict.pop(_SCREAMING_SNAKE_CASE    )
								UpperCamelCase   =	val

    # finally, create HuggingFace model and load state dict
		UpperCamelCase   =	DetrForSegmentation(_SCREAMING_SNAKE_CASE    ) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE    )
		model.load_state_dict(_SCREAMING_SNAKE_CASE    )
		model.eval()

		# verify our conversion on an image
		UpperCamelCase   =	"coco_panoptic" if is_panoptic else "coco_detection"
		UpperCamelCase   =	DetrImageProcessor(format=_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	processor(images=prepare_img()  ,	return_tensors="pt"    )
		UpperCamelCase   =	encoding["pixel_values"]

		UpperCamelCase   =	detr(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	model(_SCREAMING_SNAKE_CASE    )

		assert torch.allclose(outputs.logits  ,	original_outputs["pred_logits"]  ,	atol=1e-3    )
		assert torch.allclose(outputs.pred_boxes  ,	original_outputs["pred_boxes"]  ,	atol=1e-3    )
		if is_panoptic:
				assert torch.allclose(outputs.pred_masks  ,	original_outputs["pred_masks"]  ,	atol=1e-4    )
		print("Looks ok!"    )

		if pytorch_dump_folder_path is not None:
				# Save model and image processor
				logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."    )
				Path(_SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
				model.save_pretrained(_SCREAMING_SNAKE_CASE    )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

		if push_to_hub:
				# Upload model and image processor to the hub
				logger.info("Uploading PyTorch model and image processor to the hub..."    )
				model.push_to_hub(F"nielsr/{model_name}"    )
				processor.push_to_hub(F"nielsr/{model_name}"    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''',
						    default='''detr-resnet-50''',
						    type=str,
						    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
						    help='''Name of the DETR model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
						)
						parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
						lowerCAmelCase__     			=			parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""





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__					(					_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        _lowerCamelCase      (       nn.Module ):


												def __init__(self   ,							__a   ,							__a		) ->   Tuple:
														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     snake_case_   (self   ,							__a   ,							*__a   ,							**__a		) ->   int:
														return self.module(__a   ,							*__a   ,							**__a		) + self.adapter(__a		)





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class        _lowerCamelCase      (       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
						UpperCAmelCase_ =			"bigscience/bloom-1b7"

						# Constant values
						UpperCAmelCase_ =			2.109659552692574

						UpperCAmelCase_ =			"Hello my name is"
						UpperCAmelCase_ =			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" )
						UpperCAmelCase_ =			10


						def     snake_case_   (self		) ->   str:
								# Models and tokenizer
								UpperCamelCase   =	AutoTokenizer.from_pretrained(self.model_name		)





class        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Any:
								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     snake_case_   (self		) ->   str:
								del self.model_fpaa
								del self.model_abit

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


						def     snake_case_   (self		) ->   Optional[Any]:
								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     snake_case_   (self		) ->   Optional[int]:
								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     snake_case_   (self		) ->   Dict:
								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     snake_case_   (self		) ->   Tuple:
								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     snake_case_   (self		) ->   Optional[int]:
								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     snake_case_   (self		) ->   Dict:
								with self.assertRaises(__a		), tempfile.TemporaryDirectory() as tmpdirname:
										self.model_abit.save_pretrained(__a		)


						def     snake_case_   (self		) ->   int:
								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     snake_case_   (self		) ->   Any:
								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     snake_case_   (self		) ->   str:
								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        _lowerCamelCase      (       unittest.TestCase ):


						@classmethod
						def     snake_case_   (cls		) ->   Tuple:
								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     snake_case_   (self		) ->   Any:
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self		) ->   Optional[Any]:
								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     snake_case_   (self		) ->   str:
								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        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Optional[Any]:
								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     snake_case_   (self		) ->   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     snake_case_   (self		) ->   Optional[int]:
								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        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   int:
								super().setUp()


						def     snake_case_   (self		) ->   Any:
								del self.pipe

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




						def     snake_case_   (self		) ->   List[Any]:
								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        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   List[Any]:
								super().setUp()




						def     snake_case_   (self		) ->   Optional[int]:
								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        _lowerCamelCase      (       _lowercase ):


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	"facebook/opt-350m"
								super().setUp()




						def     snake_case_   (self		) ->   List[Any]:
								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        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"gpt2-xl"
						UpperCAmelCase_ =			3.3191854854152187






