
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
						from fairscale.optim import OSS


SCREAMING_SNAKE_CASE :		Tuple       			=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :		Optional[int]       			=							{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}






class        __lowercase		(       _a							):


					def __init__(						self							,							a__=None							,							a__=None							,							*a__							,							**a__    )		->				Tuple:





									'''simple docstring'''
									super().__init__(*A_							,							**A_    )

									if config is None:
													assert isinstance(self.model							,							A_    ), (
													    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
													    F" {self.model.__class__}"
													)
													A_													=	self.model.config
									else:
													A_													=	config

									A_													=	data_args
									A_													=	self.config.tgt_vocab_size if isinstance(self.config							,							A_    ) else self.config.vocab_size

									if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
													assert self.config.pad_token_id is not None, (
													    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
													    " calculation or doing label smoothing."
													)

									if self.config.pad_token_id is None and self.config.eos_token_id is not None:
													logger.warning(
													    F"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
													    ''' padding..'''    )

									if self.args.label_smoothing == 0:
													A_													=	torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id    )
									else:
													# dynamically import label_smoothed_nll_loss
													from utils import label_smoothed_nll_loss

													A_													=	label_smoothed_nll_loss


					def 							lowerCAmelCase_     (						self							,							a__    )		->				Tuple:





									'''simple docstring'''




									if self.optimizer is None:
													A_													=	['''bias''', '''LayerNorm.weight''']
													A_													=	[
													    {
													        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay    )],
													        '''weight_decay''': self.args.weight_decay,
													    },
													    {
													        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay    )],
													        '''weight_decay''': 0.0,
													    },
													]
													A_													=	Adafactor if self.args.adafactor else AdamW
													if self.args.adafactor:
																	A_													=	Adafactor
																	A_													=	{'''scale_parameter''': False, '''relative_step''': False}
													else:
																	A_													=	AdamW
																	A_													=	{
																	    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																	    '''eps''': self.args.adam_epsilon,
																	}
													A_													=	self.args.learning_rate
													if self.sharded_ddp:
																	A_													=	OSS(
																	    params=A_							,							optim=A_							,							**A_							,							)
													else:
																	A_													=	optimizer_cls(A_							,							**A_    )

									if self.lr_scheduler is None:
													A_													=	self._get_lr_scheduler(A_    )
									else:  # ignoring --lr_scheduler
													logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''    )


					def 							lowerCAmelCase_     (						self							,							a__    )		->				Dict:





									'''simple docstring'''
									A_													=	arg_to_scheduler[self.args.lr_scheduler]
									if self.args.lr_scheduler == "constant":
													A_													=	schedule_func(self.optimizer    )
									elif self.args.lr_scheduler == "constant_w_warmup":
													A_													=	schedule_func(self.optimizer							,							num_warmup_steps=self.args.warmup_steps    )
									else:
													A_													=	schedule_func(
													    self.optimizer							,							num_warmup_steps=self.args.warmup_steps							,							num_training_steps=A_    )
									return scheduler


					def 							lowerCAmelCase_     (						self    )		->				int:





									'''simple docstring'''




									if isinstance(self.train_dataset							,							torch.utils.data.IterableDataset    ):
													return None
									elif is_torch_tpu_available():
													return get_tpu_sampler(self.train_dataset    )
									else:
													if self.args.sortish_sampler:
																	self.train_dataset.make_sortish_sampler(
																	    self.args.per_device_train_batch_size							,							distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)							,							)

													return (
													    RandomSampler(self.train_dataset    )
													    if self.args.local_rank == -1
													    else DistributedSampler(self.train_dataset    )
													)


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__    )		->				Optional[int]:





									'''simple docstring'''




									if self.args.label_smoothing == 0:
													if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																	# force training to ignore pad token
																	A_													=	model(**A_							,							use_cache=A_    )[0]
																	A_													=	self.loss_fn(logits.view(-1							,							logits.shape[-1]    )							,							labels.view(-1    )    )
													else:
																	# compute usual loss via models
																	A_						,       A_													=	model(**A_							,							labels=A_							,							use_cache=A_    )[:2]
									else:
													# compute label smoothed loss
													A_													=	model(**A_							,							use_cache=A_    )[0]
													A_													=	torch.nn.functional.log_softmax(A_							,							dim=-1    )
													A_						,       A_													=	self.loss_fn(A_							,							A_							,							self.args.label_smoothing							,							ignore_index=self.config.pad_token_id    )
									return loss, logits


					def 							lowerCAmelCase_     (						self							,							a__							,							a__    )		->				Tuple:





									'''simple docstring'''
									A_													=	inputs.pop('''labels'''    )
									A_						,       A_													=	self._compute_loss(A_							,							A_							,							A_    )
									return loss


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__							,							a__ = None							,							)		->				Dict:





									'''simple docstring'''
									A_													=	self._prepare_inputs(A_    )

									A_													=	{
									    '''max_length''': self.data_args.val_max_target_length
									    if self.data_args is not None
									    else self.config.max_length,
									    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
									}

									if self.args.predict_with_generate and not self.args.prediction_loss_only:
													A_													=	self.model.generate(
													    inputs['''input_ids''']							,							attention_mask=inputs['''attention_mask''']							,							**A_							,							)
													# in case the batch is shorter than max length, the output should be padded
													if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																	A_													=	self._pad_tensors_to_max_len(A_							,							gen_kwargs['''max_length''']    )

									A_													=	inputs.pop('''labels'''    )
									with torch.no_grad():
													# compute loss on predict data
													A_						,       A_													=	self._compute_loss(A_							,							A_							,							A_    )

									A_													=	loss.mean().detach()
									if self.args.prediction_loss_only:
													return (loss, None, None)

									A_													=	generated_tokens if self.args.predict_with_generate else logits

									if labels.shape[-1] < gen_kwargs["max_length"]:
													A_													=	self._pad_tensors_to_max_len(A_							,							gen_kwargs['''max_length''']    )

									return (loss, logits, labels)


					def 							lowerCAmelCase_     (						self							,							a__							,							a__    )		->				Union[str, Any]:





									'''simple docstring'''
									A_													=	self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

									if pad_token_id is None:
													raise ValueError(
													    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
													    F" padded to `max_length`={max_length}"    )

									A_													=	pad_token_id * torch.ones(
									    (tensor.shape[0], max_length)							,							dtype=tensor.dtype							,							device=tensor.device    )
									A_													=	tensor
									return padded_tensor



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_					)


