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


_lowercase:	str       			=		logging.get_logger(__name__)

_lowercase:	Optional[Any]       			=		{
    '''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        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="marian"
			UpperCamelCase__    							=["past_key_values"]
			UpperCamelCase__    							={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





			def __init__(       self	:	Optional[Any]  ,  lowercase__	:	int=5_81_01  ,  lowercase__	:	Optional[int]=None  ,  lowercase__	:	List[str]=10_24  ,  lowercase__	:	Union[str, Any]=12  ,  lowercase__	:	List[str]=40_96  ,  lowercase__	:	Optional[int]=16  ,  lowercase__	:	int=12  ,  lowercase__	:	str=40_96  ,  lowercase__	:	Optional[int]=16  ,  lowercase__	:	Optional[Any]=0.0  ,  lowercase__	:	Optional[Any]=0.0  ,  lowercase__	:	List[str]=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	List[str]="gelu"  ,  lowercase__	:	Optional[int]=10_24  ,  lowercase__	:	Dict=0.1  ,  lowercase__	:	Optional[int]=0.0  ,  lowercase__	:	Dict=0.0  ,  lowercase__	:	Optional[int]=0.0_2  ,  lowercase__	:	Any=5_81_00  ,  lowercase__	:	Union[str, Any]=False  ,  lowercase__	:	Any=5_81_00  ,  lowercase__	:	Union[str, Any]=0  ,  lowercase__	:	Tuple=0  ,  lowercase__	:	Optional[Any]=True  ,  **lowercase__	:	List[Any]  ,  ):
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							decoder_vocab_size or vocab_size
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							d_model
										_lowerCAmelCase							=							encoder_ffn_dim
										_lowerCAmelCase							=							encoder_layers
										_lowerCAmelCase							=							encoder_attention_heads
										_lowerCAmelCase							=							decoder_ffn_dim
										_lowerCAmelCase							=							decoder_layers
										_lowerCAmelCase							=							decoder_attention_heads
										_lowerCAmelCase							=							dropout
										_lowerCAmelCase							=							attention_dropout
										_lowerCAmelCase							=							activation_dropout
										_lowerCAmelCase							=							activation_function
										_lowerCAmelCase							=							init_std
										_lowerCAmelCase							=							encoder_layerdrop
										_lowerCAmelCase							=							decoder_layerdrop
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							encoder_layers
										_lowerCAmelCase							=							scale_embedding  # scale factor will be sqrt(d_model) if True
										_lowerCAmelCase							=							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        lowerCamelCase__				(   UpperCAmelCase				):





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

																	if self.use_past:
																								_lowerCAmelCase							=							{0: 'batch'}
																								_lowerCAmelCase							=							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
																	else:
																								_lowerCAmelCase							=							{0: 'batch', 1: 'decoder_sequence'}
																								_lowerCAmelCase							=							{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							=							OrderedDict(
																	    [
																	        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																	        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																	    ]						)
																	if self.use_past:
																								_lowerCAmelCase			,		_lowerCAmelCase							=							self.num_layers
																								for i in range(lowercase__						):
																															_lowerCAmelCase							=							{0: 'batch', 2: 'past_sequence + sequence'}
																															_lowerCAmelCase							=							{0: 'batch', 2: 'past_sequence + sequence'}
										else:
																	_lowerCAmelCase							=							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   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										if self.task in ["default", "seq2seq-lm"]:
																	_lowerCAmelCase							=							super().outputs
										else:
																	_lowerCAmelCase							=							super(lowercase__  ,  self						).outputs
																	if self.use_past:
																								_lowerCAmelCase			,		_lowerCAmelCase							=							self.num_layers
																								for i in range(lowercase__						):
																															_lowerCAmelCase							=							{0: 'batch', 2: 'past_sequence + sequence'}
																															_lowerCAmelCase							=							{0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	PreTrainedTokenizer  ,  lowercase__	:	int = -1  ,  lowercase__	:	int = -1  ,  lowercase__	:	bool = False  ,  lowercase__	:	Optional[TensorType] = None  ,  ):
										_lowerCAmelCase							=							self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)

										# Generate decoder inputs
										_lowerCAmelCase							=							seq_length if not self.use_past else 1
										_lowerCAmelCase							=							self._generate_dummy_inputs_for_encoder_and_decoder(
										    lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
										_lowerCAmelCase							=							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							=							common_inputs['input_ids'].shape
																	_lowerCAmelCase							=							common_inputs['decoder_input_ids'].shape[1]
																	_lowerCAmelCase			,		_lowerCAmelCase							=							self.num_attention_heads
																	_lowerCAmelCase							=							(
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	_lowerCAmelCase							=							decoder_seq_length + 3
																	_lowerCAmelCase							=							(
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	_lowerCAmelCase							=							torch.cat(
																	    [common_inputs['decoder_attention_mask'], torch.ones(lowercase__  ,  lowercase__						)]  ,  dim=1						)

																	_lowerCAmelCase							=							[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	_lowerCAmelCase			,		_lowerCAmelCase							=							self.num_layers
																	_lowerCAmelCase							=							min(lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							max(lowercase__  ,  lowercase__						) - min_num_layers
																	_lowerCAmelCase							=							'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							=							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   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	PreTrainedTokenizer  ,  lowercase__	:	int = -1  ,  lowercase__	:	int = -1  ,  lowercase__	:	bool = False  ,  lowercase__	:	Optional[TensorType] = None  ,  ):
										_lowerCAmelCase							=							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							=							common_inputs['input_ids'].shape
																	# Not using the same length for past_key_values
																	_lowerCAmelCase							=							seqlen + 2
																	_lowerCAmelCase			,		_lowerCAmelCase							=							self.num_layers
																	_lowerCAmelCase			,		_lowerCAmelCase							=							self.num_attention_heads
																	_lowerCAmelCase							=							(
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)

																	_lowerCAmelCase							=							common_inputs['attention_mask'].dtype
																	_lowerCAmelCase							=							torch.cat(
																	    [common_inputs['attention_mask'], torch.ones(lowercase__  ,  lowercase__  ,  dtype=lowercase__						)]  ,  dim=1						)
																	_lowerCAmelCase							=							[
																	    (torch.zeros(lowercase__						), torch.zeros(lowercase__						)) for _ in range(lowercase__						)
																	]
										return common_inputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  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							=							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							=							tokenizer.num_special_tokens_to_add(lowercase__						)
										_lowerCAmelCase							=							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							=							[' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
										_lowerCAmelCase							=							dict(tokenizer(lowercase__  ,  return_tensors=lowercase__						)						)
										return common_inputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]  ,  lowercase__	:	PreTrainedTokenizer  ,  lowercase__	:	int = -1  ,  lowercase__	:	int = -1  ,  lowercase__	:	bool = False  ,  lowercase__	:	Optional[TensorType] = None  ,  ):
										if self.task in ["default", "seq2seq-lm"]:
																	_lowerCAmelCase							=							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																	    lowercase__  ,  batch_size=lowercase__  ,  seq_length=lowercase__  ,  is_pair=lowercase__  ,  framework=lowercase__						)

										else:
																	_lowerCAmelCase							=							self._generate_dummy_inputs_for_causal_lm(
																	    lowercase__  ,  batch_size=lowercase__  ,  seq_length=lowercase__  ,  is_pair=lowercase__  ,  framework=lowercase__						)

										return common_inputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Any  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	str						):
										if self.task in ["default", "seq2seq-lm"]:
																	_lowerCAmelCase							=							super()._flatten_past_key_values_(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)
										else:
																	_lowerCAmelCase							=							super(lowercase__  ,  self						)._flatten_past_key_values_(
																	    lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										return 1e-4







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


_lowercase:	List[str]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name






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

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

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






def _lowerCamelCase  (		snake_case  ):
							if isinstance(snake_case     , torch.Tensor  ):
														return mask
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[mask]

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





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





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  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							=							image

										_lowerCAmelCase							=							_preprocess_image(lowercase__						)
										_lowerCAmelCase							=							original_image.to(device=self.device  ,  dtype=self.unet.dtype						)
										_lowerCAmelCase							=							_preprocess_mask(lowercase__						)
										_lowerCAmelCase							=							mask_image.to(device=self.device  ,  dtype=self.unet.dtype						)

										_lowerCAmelCase							=							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							=							original_image.shape
										_lowerCAmelCase							=							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							=							eta

										_lowerCAmelCase							=							self.scheduler.timesteps[0] + 1
										_lowerCAmelCase							=							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							=							self.unet(lowercase__  ,  lowercase__						).sample
																								# compute previous image: x_t -> x_t-1
																								_lowerCAmelCase							=							self.scheduler.step(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						).prev_sample

																	else:
																								# compute the reverse: x_t-1 -> x_t
																								_lowerCAmelCase							=							self.scheduler.undo_step(lowercase__  ,  lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							t

										_lowerCAmelCase							=							(image / 2 + 0.5).clamp(0  ,  1						)
										_lowerCAmelCase							=							image.cpu().permute(0  ,  2  ,  3  ,  1						).numpy()
										if output_type == "pil":
																	_lowerCAmelCase							=							self.numpy_to_pil(lowercase__						)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






