
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


_UpperCAmelCase :			int              =	logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				*A_    ,				A_=None    ,				A_=None    ,				A_=None    ,				**A_	)  -> int:
				"""simple docstring"""
				super().__init__(*A_    ,				**A_	)
				UpperCamelCase          =					eval_examples
				UpperCamelCase          =					post_process_function
				UpperCamelCase          =					quant_trainer_args
				UpperCamelCase          =					128  # default number of calibration samples



		def __UpperCamelCase			(			self    ,				A_=None	)  -> str:
				"""simple docstring"""
				if calib_dataset is None and self.calib_dataset is None:
						raise ValueError('Trainer: calibration requires an calib_dataset.'	)
				UpperCamelCase          =					calib_dataset if calib_dataset is not None else self.calib_dataset

				UpperCamelCase          =					self._remove_unused_columns(A_    ,				description='Calibration'	)

				return DataLoader(
				    A_    ,				batch_size=self.args.eval_batch_size    ,				collate_fn=self.data_collator    ,				drop_last=self.args.dataloader_drop_last    ,				num_workers=self.args.dataloader_num_workers    ,				pin_memory=self.args.dataloader_pin_memory    ,				shuffle=A_    ,				)



		def __UpperCamelCase			(			self    ,				A_=None	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.train_dataset if calib_dataset is None else calib_dataset
				UpperCamelCase          =					self.get_calib_dataloader(A_	)

				UpperCamelCase          =					self.model
				quant_trainer.configure_model(A_    ,				self.quant_trainer_args    ,				calib=A_	)
				model.eval()
				quant_trainer.enable_calibration(A_	)

				logger.info('***** Running calibration *****'	)
				logger.info(F'''  Num examples = {self.calib_num}'''	)
				logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''	)

				for step, inputs in enumerate(A_	):
						# Prediction step
						UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					self.prediction_step(A_    ,				A_    ,				prediction_loss_only=A_	)
						if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
								break

				quant_trainer.finish_calibration(A_    ,				self.quant_trainer_args	)
				UpperCamelCase          =					model



		def __UpperCamelCase			(			self    ,				A_=None    ,				A_=None    ,				A_=None    ,				A_ = "eval"	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.eval_dataset if eval_dataset is None else eval_dataset
				UpperCamelCase          =					self.get_eval_dataloader(A_	)
				UpperCamelCase          =					self.eval_examples if eval_examples is None else eval_examples

				# Temporarily disable metric computation, we will do it in the loop here.
				UpperCamelCase          =					self.compute_metrics
				UpperCamelCase          =					None
				UpperCamelCase          =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
				try:
						UpperCamelCase          =					eval_loop(
						    A_    ,				description='Evaluation'    ,				prediction_loss_only=True if compute_metrics is None else None    ,				ignore_keys=A_    ,				)
				finally:
						UpperCamelCase          =					compute_metrics

				if self.post_process_function is not None and self.compute_metrics is not None:
						UpperCamelCase          =					self.post_process_function(A_    ,				A_    ,				output.predictions	)
						UpperCamelCase          =					self.compute_metrics(A_	)

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()	):
								if not key.startswith(F'''{metric_key_prefix}_'''	):
										UpperCamelCase          =					metrics.pop(A_	)

						self.log(A_	)
				else:
						UpperCamelCase          =					{}

				if self.args.tpu_metrics_debug or self.args.debug:
						# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
						xm.master_print(met.metrics_report()	)

				UpperCamelCase          =					self.callback_handler.on_evaluate(self.args    ,				self.state    ,				self.control    ,				A_	)
				return metrics



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_=None    ,				A_ = "test"	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_test_dataloader(A_	)

				# Temporarily disable metric computation, we will do it in the loop here.
				UpperCamelCase          =					self.compute_metrics
				UpperCamelCase          =					None
				UpperCamelCase          =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
				try:
						UpperCamelCase          =					eval_loop(
						    A_    ,				description='Prediction'    ,				prediction_loss_only=True if compute_metrics is None else None    ,				ignore_keys=A_    ,				)
				finally:
						UpperCamelCase          =					compute_metrics

				if self.post_process_function is None or self.compute_metrics is None:
						return output

				UpperCamelCase          =					self.post_process_function(A_    ,				A_    ,				output.predictions    ,				'predict'	)
				UpperCamelCase          =					self.compute_metrics(A_	)

				# Prefix all keys with metric_key_prefix + '_'
				for key in list(metrics.keys()	):
						if not key.startswith(F'''{metric_key_prefix}_'''	):
								UpperCamelCase          =					metrics.pop(A_	)

				return PredictionOutput(predictions=predictions.predictions    ,				label_ids=predictions.label_ids    ,				metrics=A_	)



		def __UpperCamelCase			(			self    ,				A_="./"	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.eval_dataset
				UpperCamelCase          =					self.get_eval_dataloader(A_	)

				UpperCamelCase          =					next(iter(A_	)	)

				# saving device - to make it consistent
				UpperCamelCase          =					torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

				# convert to tuple
				UpperCamelCase          =					tuple(v.to(A_	) for k, v in batch.items()	)

				logger.info('Converting model to be onnx compatible'	)
				from pytorch_quantization.nn import TensorQuantizer

				UpperCamelCase          =					True

				UpperCamelCase          =					self.model.to(A_	)

				model.eval()
				model.float()

				UpperCamelCase          =					model.module if hasattr(A_    ,				'module'	) else model
				quant_trainer.configure_model(A_    ,				self.quant_trainer_args	)

				UpperCamelCase          =					os.path.join(A_    ,				'model.onnx'	)
				logger.info(F'''exporting model to {output_model_file}'''	)

				UpperCamelCase          =					{0: 'batch_size', 1: 'seq_len'}

				torch.onnx.export(
				    A_    ,				A_    ,				A_    ,				export_params=A_    ,				opset_version=13    ,				do_constant_folding=A_    ,				input_names=['input_ids', 'attention_mask', 'token_type_ids']    ,				output_names=['output_start_logits', 'output_end_logits']    ,				dynamic_axes={
				        'input_ids': axes,
				        'attention_mask': axes,
				        'token_type_ids': axes,
				        'output_start_logits': axes,
				        'output_end_logits': axes,
				    }    ,				verbose=A_    ,				)
				logger.info('onnx export finished'	)

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    KandinskyVaaControlnetImgaImgPipeline
		__lowercase	:	Optional[Any]     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Any     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Union[str, Any]     					=    [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__lowercase	:	Optional[int]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				torch.manual_seed(0	)

				UpperCamelCase          =					{
				    'in_channels': 8,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'image_hint',
				    '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': '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 __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					VQModel(**self.dummy_movq_kwargs	)
				return model



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_unet
				UpperCamelCase          =					self.dummy_movq

				UpperCamelCase          =					{
				    'num_train_timesteps': 1_000,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				UpperCamelCase          =					DDIMScheduler(**A_	)

				UpperCamelCase          =					{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				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 hint
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)

				if str(A_	).startswith('mps'	):
						UpperCamelCase          =					torch.manual_seed(A_	)
				else:
						UpperCamelCase          =					torch.Generator(device=A_	).manual_seed(A_	)
				UpperCamelCase          =					{
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase			(			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]

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

				UpperCamelCase          =					np.array(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]	)
				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()}'''






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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
				UpperCamelCase          =					init_image.resize((512, 512)	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/hint_image_cat.png'	)
				UpperCamelCase          =					torch.from_numpy(np.array(A_	)	).float() / 255.0
				UpperCamelCase          =					hint.permute(2    ,				0    ,				1	).unsqueeze(0	)

				UpperCamelCase          =					'A robot, 4k photo'

				UpperCamelCase          =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'    ,				torch_dtype=torch.floataa	)
				pipe_prior.to(A_	)

				UpperCamelCase          =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,				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_    ,				image=A_    ,				strength=0.85    ,				generator=A_    ,				negative_prompt=''    ,				).to_tuple()

				UpperCamelCase          =					pipeline(
				    image=A_    ,				image_embeds=A_    ,				negative_image_embeds=A_    ,				hint=A_    ,				generator=A_    ,				num_inference_steps=100    ,				height=512    ,				width=512    ,				strength=0.5    ,				output_type='np'    ,				)

				UpperCamelCase          =					output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(A_    ,				A_	)
