
import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


SCREAMING_SNAKE_CASE :		Dict       			=							logging.getLogger(__name__)

SCREAMING_SNAKE_CASE :		int       			=							50  # max width of layer names
SCREAMING_SNAKE_CASE :		int       			=							70  # max width of quantizer names



def 							lowerCamelCase_	( __UpperCamelCase  ):
				A_													=	parser.add_argument_group('''quant_trainer arguments'''  )
				group.add_argument('''--wprec'''			,    type=_lowercase			,    default=8			,    help='''weight precision'''  )
				group.add_argument('''--aprec'''			,    type=_lowercase			,    default=8			,    help='''activation precision'''  )
				group.add_argument('''--quant-per-tensor'''			,    action='''store_true'''			,    help='''per tensor weight scaling'''  )
				group.add_argument('''--quant-disable'''			,    action='''store_true'''			,    help='''disable all quantizers'''  )
				group.add_argument('''--quant-disable-embeddings'''			,    action='''store_true'''			,    help='''disable all embeddings quantizers'''  )
				group.add_argument('''--quant-disable-keyword'''			,    type=_lowercase			,    nargs='''+'''			,    help='''disable quantizers by keyword'''  )
				group.add_argument('''--quant-disable-layer-module'''			,    type=_lowercase			,    help='''disable quantizers by keyword under layer.'''  )
				group.add_argument('''--quant-enable-layer-module'''			,    type=_lowercase			,    help='''enable quantizers by keyword under layer'''  )
				group.add_argument('''--calibrator'''			,    default='''max'''			,    help='''which quantization range calibrator to use'''  )
				group.add_argument('''--percentile'''			,    default=_lowercase			,    type=_lowercase			,    help='''percentile for PercentileCalibrator'''  )
				group.add_argument('''--fuse-qkv'''			,    action='''store_true'''			,    help='''use the same scale factor for qkv'''  )
				group.add_argument('''--clip-gelu'''			,    metavar='''N'''			,    type=_lowercase			,    help='''clip gelu output maximum value to N'''  )
				group.add_argument(
				    '''--recalibrate-weights'''			,    action='''store_true'''			,    help=(
				        '''recalibrate weight amaxes by taking the max of the weights.'''
				        ''' amaxes will be computed with the current quantization granularity (axis).'''
				    )			,    )



def 							lowerCamelCase_	( __UpperCamelCase  ):


				if args.calibrator == "max":
								A_													=	'''max'''
				elif args.calibrator == "percentile":
								if args.percentile is None:
												raise ValueError('''Specify --percentile when using percentile calibrator'''  )
								A_													=	'''histogram'''
				elif args.calibrator == "mse":
								A_													=	'''histogram'''
				else:
								raise ValueError(F"Invalid calibrator {args.calibrator}"  )

				A_													=	QuantDescriptor(num_bits=args.aprec			,    calib_method=_lowercase  )
				A_													=	QuantDescriptor(num_bits=args.wprec			,    axis=(None if args.quant_per_tensor else (0,))  )
				quant_nn.QuantLinear.set_default_quant_desc_input(_lowercase  )
				quant_nn.QuantLinear.set_default_quant_desc_weight(_lowercase  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase=False			,    __UpperCamelCase=False  ):
				logger.info('''Configuring Model for Quantization'''  )
				logger.info(F"using quantization package {pytorch_quantization.__file__}"  )

				if not calib:
								if args.quant_disable_embeddings:
												set_quantizer_by_name(_lowercase			,    ['''embeddings''']			,    which='''weight'''			,    _disabled=_lowercase  )

								if args.quant_disable:
												set_quantizer_by_name(_lowercase			,    ['''''']			,    _disabled=_lowercase  )

								if args.quant_disable_keyword:
												set_quantizer_by_name(_lowercase			,    args.quant_disable_keyword			,    _disabled=_lowercase  )

								if args.quant_disable_layer_module:
												set_quantizer_by_name(_lowercase			,    [r'''layer.\d+.''' + args.quant_disable_layer_module]			,    _disabled=_lowercase  )

								if args.quant_enable_layer_module:
												set_quantizer_by_name(_lowercase			,    [r'''layer.\d+.''' + args.quant_enable_layer_module]			,    _disabled=_lowercase  )

								if args.recalibrate_weights:
												recalibrate_weights(_lowercase  )

								if args.fuse_qkv:
												fuse_qkv(_lowercase			,    _lowercase  )

				if args.clip_gelu:
								clip_gelu(_lowercase			,    args.clip_gelu  )

				# if args.local_rank in [-1, 0] and not calib:
				print_quant_summary(_lowercase  )



def 							lowerCamelCase_	( __UpperCamelCase  ):
				logger.info('''Enabling Calibration'''  )
				for name, module in model.named_modules():
								if name.endswith('''_quantizer'''  ):
												if module._calibrator is not None:
																module.disable_quant()
																module.enable_calib()
												else:
																module.disable()
												logger.info(F"{name:80}: {module}"  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):
				logger.info('''Loading calibrated amax'''  )
				for name, module in model.named_modules():
								if name.endswith('''_quantizer'''  ):
												if module._calibrator is not None:
																if isinstance(module._calibrator			,    calib.MaxCalibrator  ):
																				module.load_calib_amax()
																else:
																				module.load_calib_amax('''percentile'''			,    percentile=args.percentile  )
																module.enable_quant()
																module.disable_calib()
												else:
																module.enable()
				model.cuda()
				print_quant_summary(_lowercase  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):


				def fusea(__UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase  ):
								for mod in [qq, qk, qv]:
												if not hasattr(_lowercase			,    '''_amax'''  ):
																print('''          WARNING: NO AMAX BUFFER'''  )
																return
								A_													=	qq._amax.detach().item()
								A_													=	qk._amax.detach().item()
								A_													=	qv._amax.detach().item()

								A_													=	max(_lowercase			,    _lowercase			,    _lowercase  )
								qq._amax.fill_(_lowercase  )
								qk._amax.fill_(_lowercase  )
								qv._amax.fill_(_lowercase  )
								logger.info(F"          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"  )

				for name, mod in model.named_modules():
								if name.endswith('''.attention.self'''  ):
												logger.info(F"FUSE_QKV: {name:{name_width}}"  )
												fusea(mod.matmul_q_input_quantizer			,    mod.matmul_k_input_quantizer			,    mod.matmul_v_input_quantizer  )
												if args.quant_per_tensor:
																fusea(mod.query._weight_quantizer			,    mod.key._weight_quantizer			,    mod.value._weight_quantizer  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase  ):


				for name, mod in model.named_modules():
								if name.endswith('''.output.dense'''  ) and not name.endswith('''attention.output.dense'''  ):
												A_													=	mod._input_quantizer._amax.data.detach().item()
												mod._input_quantizer._amax.data.detach().clamp_(max=_lowercase  )
												A_													=	mod._input_quantizer._amax.data.detach().item()
												logger.info(F"CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"  )



def 							lowerCamelCase_	( __UpperCamelCase  ):


				for name, mod in model.named_modules():
								if hasattr(_lowercase			,    '''_weight_quantizer'''  ) and mod._weight_quantizer.axis is not None:
												A_													=	mod.weight.shape[0]
												A_													=	mod._weight_quantizer._amax.detach()
												A_													=	torch.ones(_lowercase			,    dtype=amax.dtype			,    device=amax.device  ) * amax
												print(F"expanding {name} {amax} -> {mod._weight_quantizer._amax}"  )



def 							lowerCamelCase_	( __UpperCamelCase  ):


				for name, mod in model.named_modules():
								if hasattr(_lowercase			,    '''_weight_quantizer'''  ):
												if not hasattr(mod.weight_quantizer			,    '''_amax'''  ):
																print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'''  )
																continue

												# determine which axes to reduce across
												# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
												A_													=	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis  )
												A_													=	set(range(len(mod.weight.size()  )  )  ) - axis_set
												A_													=	pytorch_quantization.utils.reduce_amax(mod.weight			,    axis=_lowercase			,    keepdims=_lowercase  ).detach()
												logger.info(F"RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"  )
												A_													=	amax



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase=25			,    __UpperCamelCase=1_80			,    __UpperCamelCase=None  ):


				if ignore is None:
								A_													=	[]
				elif not isinstance(_lowercase			,    _lowercase  ):
								A_													=	[ignore]

				A_													=	0
				for name, mod in model.named_modules():
								if not hasattr(_lowercase			,    '''weight'''  ):
												continue
								A_													=	max(_lowercase			,    len(_lowercase  )  )

				for name, mod in model.named_modules():
								A_													=	getattr(_lowercase			,    '''_input_quantizer'''			,    _lowercase  )
								A_													=	getattr(_lowercase			,    '''_weight_quantizer'''			,    _lowercase  )
								if not hasattr(_lowercase			,    '''weight'''  ):
												continue
								if type(_lowercase  ) in ignore:
												continue
								if [True for s in ignore if type(_lowercase  ) is str and s in name]:
												continue
								A_													=	F"Act:{input_q.extra_repr()}"
								A_													=	F"Wgt:{weight_q.extra_repr()}"
								A_													=	F"{name:{name_width}} {act_str} {wgt_str}"
								if len(_lowercase  ) <= line_width:
												logger.info(_lowercase  )
								else:
												logger.info(F"{name:{name_width}} {act_str}"  )
												logger.info(F"{'  ':{name_width}} {wgt_str}"  )



def 							lowerCamelCase_	( __UpperCamelCase  ):
				A_													=	0
				for name, mod in model.named_modules():
								if isinstance(_lowercase			,    pytorch_quantization.nn.TensorQuantizer  ):
												print(F"{name:80} {mod}"  )
												count += 1
				print(F"{count} TensorQuantizers found in model"  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase  ):
				A_													=	getattr(_lowercase			,    _lowercase			,    _lowercase  )
				if quantizer_mod is not None:
								assert hasattr(_lowercase			,    _lowercase  )
								setattr(_lowercase			,    _lowercase			,    _lowercase  )
				else:
								logger.warning(F"{name} has no {quantizer}"  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase="both"			,    **__UpperCamelCase  ):
				A_													=	F"Warning: changing {which} quantizers of {name:{qname_width}}"
				for k, v in kwargs.items():
								s += F" {k}={v}"
								if which in ["input", "both"]:
												set_quantizer(_lowercase			,    _lowercase			,    '''_input_quantizer'''			,    _lowercase			,    _lowercase  )
								if which in ["weight", "both"]:
												set_quantizer(_lowercase			,    _lowercase			,    '''_weight_quantizer'''			,    _lowercase			,    _lowercase  )
				logger.info(_lowercase  )



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    **__UpperCamelCase  ):


				for name, mod in model.named_modules():
								if hasattr(_lowercase			,    '''_input_quantizer'''  ) or hasattr(_lowercase			,    '''_weight_quantizer'''  ):
												for n in names:
																if re.search(_lowercase			,    _lowercase  ):
																				set_quantizers(_lowercase			,    _lowercase			,    **_lowercase  )
								elif name.endswith('''_quantizer'''  ):
												for n in names:
																if re.search(_lowercase			,    _lowercase  ):
																				A_													=	F"Warning: changing {name:{name_width}}"
																				for k, v in kwargs.items():
																								s += F" {k}={v}"
																								setattr(_lowercase			,    _lowercase			,    _lowercase  )
																				logger.info(_lowercase  )



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     KandinskyInpaintPipeline
       _lowercase											=     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
       _lowercase											=     [
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
           """mask_image""",
       ]
       _lowercase											=     [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       _lowercase											=     False







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            return self.time_input_dim







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return self.time_input_dim * 4







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return 100







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
            return tokenizer







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			MCLIPConfig(
                numDims=self.cross_attention_dim,transformerDimensions=self.text_embedder_hidden_size,hidden_size=self.text_embedder_hidden_size,intermediate_size=37,num_attention_heads=4,num_hidden_layers=5,vocab_size=1005,)

            __UpperCamelCase =			MultilingualCLIP(A_					)
            __UpperCamelCase =			text_encoder.eval()

            return text_encoder







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)

            __UpperCamelCase =			{
                'in_channels': 9,
                # Out channels is double in channels because predicts mean and variance
                'out_channels': 8,
                'addition_embed_type': 'text_image',
                'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
                'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
                'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
                'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
                'layers_per_block': 1,
                'encoder_hid_dim': self.text_embedder_hidden_size,
                'encoder_hid_dim_type': 'text_image_proj',
                'cross_attention_dim': self.cross_attention_dim,
                'attention_head_dim': 4,
                'resnet_time_scale_shift': 'scale_shift',
                'class_embed_type': None,
            }

            __UpperCamelCase =			UNetaDConditionModel(**A_					)
            return model







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 12,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			VQModel(**self.dummy_movq_kwargs					)
            return model







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.dummy_text_encoder
            __UpperCamelCase =			self.dummy_tokenizer
            __UpperCamelCase =			self.dummy_unet
            __UpperCamelCase =			self.dummy_movq

            __UpperCamelCase =			DDIMScheduler(
                num_train_timesteps=1000,beta_schedule='linear',beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,clip_sample=A_,set_alpha_to_one=A_,steps_offset=1,prediction_type='epsilon',thresholding=A_,)

            __UpperCamelCase =			{
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components







       def 		snake_case_	(			self: Tuple,A_: Optional[int],A_: Dict=0					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1					)					).to(A_					)
            # create init_image
            __UpperCamelCase =			floats_tensor((1, 3, 64, 64),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			image.cpu().permute(0,2,3,1					)[0]
            __UpperCamelCase =			Image.fromarray(np.uinta(A_					)					).convert('RGB'					).resize((256, 256)					)
            # create mask
            __UpperCamelCase =			np.ones((64, 64),dtype=np.floataa					)
            __UpperCamelCase =			0

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'horse',
                'image': init_image,
                'mask_image': mask,
                'image_embeds': image_embeds,
                'negative_image_embeds': negative_image_embeds,
                'generator': generator,
                'height': 64,
                'width': 64,
                'num_inference_steps': 2,
                'guidance_scale': 4.0,
                'output_type': 'np',
            }
            return inputs







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'

            __UpperCamelCase =			self.get_dummy_components()

            __UpperCamelCase =			self.pipeline_class(**A_					)
            __UpperCamelCase =			pipe.to(A_					)

            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			pipe(**self.get_dummy_inputs(A_					)					)
            __UpperCamelCase =			output.images

            __UpperCamelCase =			pipe(
                **self.get_dummy_inputs(A_					),return_dict=A_,)[0]

            __UpperCamelCase =			image[0, -3:, -3:, -1]
            __UpperCamelCase =			image_from_tuple[0, -3:, -3:, -1]

            print(F'''image.shape {image.shape}'''					)

            assert image.shape == (1, 64, 64, 3)

            __UpperCamelCase =			np.array(
                [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]					)

            assert (
                np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
            ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
            ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



@slow
@require_torch_gpu
class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

            __UpperCamelCase =			load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
            __UpperCamelCase =			np.ones((768, 768),dtype=np.floataa					)
            __UpperCamelCase =			0

            __UpperCamelCase =			'a hat'

            __UpperCamelCase =			KandinskyPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-prior',torch_dtype=torch.floataa					)
            pipe_prior.to(A_					)

            __UpperCamelCase =			KandinskyInpaintPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-inpaint',torch_dtype=torch.floataa					)
            __UpperCamelCase =			pipeline.to(A_					)
            pipeline.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			torch.Generator(device='cpu'					).manual_seed(0					)
            __UpperCamelCase,    __UpperCamelCase =			pipe_prior(
                A_,generator=A_,num_inference_steps=5,negative_prompt='',).to_tuple()

            __UpperCamelCase =			pipeline(
                A_,image=A_,mask_image=A_,image_embeds=A_,negative_image_embeds=A_,generator=A_,num_inference_steps=100,height=768,width=768,output_type='np',)

            __UpperCamelCase =			output.images[0]

            assert image.shape == (768, 768, 3)

            assert_mean_pixel_difference(A_,A_					)


