





"""simple docstring"""





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_UpperCamelCase					      =   logging.get_logger(__name__)  # pylint: disable=invalid-name



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[List, PIL.Image.Image, torch.Tensor] ):


							'''simple docstring'''







							warnings.warn(
							    '''The preprocess method is deprecated and will be removed in a future version. Please'''
							    ''' use VaeImageProcessor.preprocess instead'''     ,    SCREAMING_SNAKE_CASE     ,    )
							if isinstance(SCREAMING_SNAKE_CASE     ,    torch.Tensor ):
														return image
							elif isinstance(SCREAMING_SNAKE_CASE     ,    PIL.Image.Image ):
														__lowerCamelCase   :   Optional[int]       =[image]

							if isinstance(image[0]     ,    PIL.Image.Image ):
														__lowerCamelCase     ,    __lowerCamelCase   :   str       =image[0].size
														__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														__lowerCamelCase   :   Union[str, Any]       =[np.array(i.resize((w, h)     ,    resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image]
														__lowerCamelCase   :   Any       =np.concatenate(SCREAMING_SNAKE_CASE     ,    axis=0 )
														__lowerCamelCase   :   str       =np.array(SCREAMING_SNAKE_CASE ).astype(np.floataa ) / 255.0
														__lowerCamelCase   :   List[str]       =image.transpose(0     ,    3     ,    1     ,    2 )
														__lowerCamelCase   :   str       =2.0 * image - 1.0
														__lowerCamelCase   :   Dict       =torch.from_numpy(SCREAMING_SNAKE_CASE )
							elif isinstance(image[0]     ,    torch.Tensor ):
														__lowerCamelCase   :   Optional[Any]       =torch.cat(SCREAMING_SNAKE_CASE     ,    dim=0 )
							return image



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[List, PIL.Image.Image, torch.Tensor] ):


							'''simple docstring'''







							if isinstance(SCREAMING_SNAKE_CASE     ,    torch.Tensor ):
														return mask
							elif isinstance(SCREAMING_SNAKE_CASE     ,    PIL.Image.Image ):
														__lowerCamelCase   :   str       =[mask]

							if isinstance(mask[0]     ,    PIL.Image.Image ):
														__lowerCamelCase     ,    __lowerCamelCase   :   str       =mask[0].size
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														__lowerCamelCase   :   Dict       =[np.array(m.convert('''L''' ).resize((w, h)     ,    resample=PIL_INTERPOLATION['''nearest'''] ) )[None, :] for m in mask]
														__lowerCamelCase   :   Any       =np.concatenate(SCREAMING_SNAKE_CASE     ,    axis=0 )
														__lowerCamelCase   :   Optional[int]       =mask.astype(np.floataa ) / 255.0
														__lowerCamelCase   :   List[str]       =0
														__lowerCamelCase   :   Tuple       =1
														__lowerCamelCase   :   int       =torch.from_numpy(SCREAMING_SNAKE_CASE )
							elif isinstance(mask[0]     ,    torch.Tensor ):
														__lowerCamelCase   :   int       =torch.cat(SCREAMING_SNAKE_CASE     ,    dim=0 )
							return mask
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							UNetaDModel
				__snake_case :							RePaintScheduler




				def __init__(  self	:Any    ,     __lowercase	:Tuple    ,     __lowercase	:Optional[int]      ):
											super().__init__()
											self.register_modules(unet=__lowercase    ,     scheduler=__lowercase      )




				@torch.no_grad()
				def __call__(  self	:str    ,     __lowercase	:Union[torch.Tensor, PIL.Image.Image]    ,     __lowercase	:Union[torch.Tensor, PIL.Image.Image]    ,     __lowercase	:int = 250    ,     __lowercase	:float = 0.0    ,     __lowercase	:int = 10    ,     __lowercase	:int = 10    ,     __lowercase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     __lowercase	:Optional[str] = "pil"    ,     __lowercase	:bool = True    ,     ):
											__lowerCamelCase   :   Tuple       =image

											__lowerCamelCase   :   List[Any]       =_preprocess_image(__lowercase      )
											__lowerCamelCase   :   Tuple       =original_image.to(device=self.device    ,     dtype=self.unet.dtype      )
											__lowerCamelCase   :   Dict       =_preprocess_mask(__lowercase      )
											__lowerCamelCase   :   Union[str, Any]       =mask_image.to(device=self.device    ,     dtype=self.unet.dtype      )

											__lowerCamelCase   :   Any       =original_image.shape[0]

											# sample gaussian noise to begin the loop
											if isinstance(__lowercase    ,     __lowercase      ) and len(__lowercase      ) != batch_size:
																		raise ValueError(
																		    f'You have passed a list of generators of length {len(__lowercase      )}, but requested an effective batch'
																		    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

											__lowerCamelCase   :   Any       =original_image.shape
											__lowerCamelCase   :   Optional[int]       =randn_tensor(__lowercase    ,     generator=__lowercase    ,     device=self.device    ,     dtype=self.unet.dtype      )

											# set step values
											self.scheduler.set_timesteps(__lowercase    ,     __lowercase    ,     __lowercase    ,     self.device      )
											__lowerCamelCase   :   str       =eta

											__lowerCamelCase   :   str       =self.scheduler.timesteps[0] + 1
											__lowerCamelCase   :   str       =generator[0] if isinstance(__lowercase    ,     __lowercase      ) else generator
											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
																		if t < t_last:
																									# predict the noise residual
																									__lowerCamelCase   :   str       =self.unet(__lowercase    ,     __lowercase      ).sample
																									# compute previous image: x_t -> x_t-1
																									__lowerCamelCase   :   Union[str, Any]       =self.scheduler.step(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      ).prev_sample

																		else:
																									# compute the reverse: x_t-1 -> x_t
																									__lowerCamelCase   :   List[Any]       =self.scheduler.undo_step(__lowercase    ,     __lowercase    ,     __lowercase      )
																		__lowerCamelCase   :   Optional[int]       =t

											__lowerCamelCase   :   Optional[int]       =(image / 2 + 0.5).clamp(0    ,     1      )
											__lowerCamelCase   :   Tuple       =image.cpu().permute(0    ,     2    ,     3    ,     1      ).numpy()
											if output_type == "pil":
																		__lowerCamelCase   :   Any       =self.numpy_to_pil(__lowercase      )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__lowercase      )











"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Tuple									=		"""marian"""
				__snake_case :							Any									=		["""past_key_values"""]
				__snake_case :							Optional[Any]									=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self	:List[Any]    ,     __lowercase	:Any=5_8101    ,     __lowercase	:Tuple=None    ,     __lowercase	:Union[str, Any]=1024    ,     __lowercase	:Dict=12    ,     __lowercase	:int=4096    ,     __lowercase	:int=16    ,     __lowercase	:List[Any]=12    ,     __lowercase	:Dict=4096    ,     __lowercase	:Dict=16    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:List[Any]=True    ,     __lowercase	:int=True    ,     __lowercase	:Tuple="gelu"    ,     __lowercase	:str=1024    ,     __lowercase	:Optional[int]=0.1    ,     __lowercase	:List[str]=0.0    ,     __lowercase	:Union[str, Any]=0.0    ,     __lowercase	:Dict=0.02    ,     __lowercase	:Tuple=5_8100    ,     __lowercase	:Optional[Any]=False    ,     __lowercase	:int=5_8100    ,     __lowercase	:Any=0    ,     __lowercase	:str=0    ,     __lowercase	:str=True    ,     **__lowercase	:Any    ,     ):
											__lowerCamelCase   :   List[Any]       =vocab_size
											__lowerCamelCase   :   Optional[int]       =decoder_vocab_size or vocab_size
											__lowerCamelCase   :   Tuple       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =d_model
											__lowerCamelCase   :   Any       =encoder_ffn_dim
											__lowerCamelCase   :   str       =encoder_layers
											__lowerCamelCase   :   List[str]       =encoder_attention_heads
											__lowerCamelCase   :   str       =decoder_ffn_dim
											__lowerCamelCase   :   Tuple       =decoder_layers
											__lowerCamelCase   :   Any       =decoder_attention_heads
											__lowerCamelCase   :   List[Any]       =dropout
											__lowerCamelCase   :   Any       =attention_dropout
											__lowerCamelCase   :   Union[str, Any]       =activation_dropout
											__lowerCamelCase   :   Optional[int]       =activation_function
											__lowerCamelCase   :   Dict       =init_std
											__lowerCamelCase   :   List[Any]       =encoder_layerdrop
											__lowerCamelCase   :   Optional[Any]       =decoder_layerdrop
											__lowerCamelCase   :   Any       =use_cache
											__lowerCamelCase   :   Any       =encoder_layers
											__lowerCamelCase   :   Optional[int]       =scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCamelCase   :   int       =share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=__lowercase    ,     eos_token_id=__lowercase    ,     is_encoder_decoder=__lowercase    ,     decoder_start_token_id=__lowercase    ,     forced_eos_token_id=__lowercase    ,     **__lowercase    ,     )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def        __lowercase	(  self	:int      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )

																		if self.use_past:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch'''}
																									__lowerCamelCase   :   Any       ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																		else:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}
																									__lowerCamelCase   :   List[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}

																		if self.use_past:
																									self.fill_with_past_key_values_(__lowercase    ,     direction='''inputs'''      )
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		__lowerCamelCase   :   Any       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   List[str]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase   :   List[str]       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		    ]      )

											return common_inputs




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        __lowercase	(  self	:Optional[int]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =super().outputs
											else:
																		__lowerCamelCase   :   List[str]       =super(__lowercase    ,     self      ).outputs
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   int       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Any       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											return common_outputs




				def        __lowercase	(  self	:str    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											# Generate decoder inputs
											__lowerCamelCase   :   Optional[Any]       =seq_length if not self.use_past else 1
											__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											__lowerCamelCase   :   str       =dict(**__lowercase    ,     **__lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		__lowerCamelCase   :   Optional[Any]       =common_inputs['''decoder_input_ids'''].shape[1]
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =self.num_attention_heads
																		__lowerCamelCase   :   Any       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		__lowerCamelCase   :   Dict       =decoder_seq_length + 3
																		__lowerCamelCase   :   Tuple       =(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase    ,     __lowercase      )]    ,     dim=1      )

																		__lowerCamelCase   :   Any       =[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																		__lowerCamelCase   :   List[Any]       =min(__lowercase    ,     __lowercase      )
																		__lowerCamelCase   :   int       =max(__lowercase    ,     __lowercase      ) - min_num_layers
																		__lowerCamelCase   :   Any       ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																		for _ in range(__lowercase      ):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									    )      )
																		# TODO: test this.
																		__lowerCamelCase   :   Dict       =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																		for _ in range(__lowercase    ,     __lowercase      ):
																									common_inputs["past_key_values"].append((torch.zeros(__lowercase      ), torch.zeros(__lowercase      ))      )
											return common_inputs




				def        __lowercase	(  self	:Dict    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		__lowerCamelCase   :   str       =seqlen + 2
																		__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =self.num_layers
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =self.num_attention_heads
																		__lowerCamelCase   :   List[str]       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		__lowerCamelCase   :   Any       =common_inputs['''attention_mask'''].dtype
																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''attention_mask'''], torch.ones(__lowercase    ,     __lowercase    ,     dtype=__lowercase      )]    ,     dim=1      )
																		__lowerCamelCase   :   List[str]       =[
																		    (torch.zeros(__lowercase      ), torch.zeros(__lowercase      )) for _ in range(__lowercase      )
																		]
											return common_inputs




				def        __lowercase	(  self	:int    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											__lowerCamelCase   :   Union[str, Any]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_batch    ,     num_token_to_add=0      )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											__lowerCamelCase   :   Optional[int]       =tokenizer.num_special_tokens_to_add(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_sequence    ,     num_token_to_add=__lowercase      )

											# Generate dummy inputs according to compute batch and sequence
											__lowerCamelCase   :   Any       =[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
											__lowerCamelCase   :   Union[str, Any]       =dict(tokenizer(__lowercase    ,     return_tensors=__lowercase      )      )
											return common_inputs




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   int       =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											else:
																		__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_causal_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											return common_inputs




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:Dict    ,     __lowercase	:List[str]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   str       =super()._flatten_past_key_values_(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											else:
																		__lowerCamelCase   :   Optional[Any]       =super(__lowercase    ,     self      )._flatten_past_key_values_(
																		    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				@property
				def        __lowercase	(  self	:List[str]      ):
											return 1e-4




