'''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


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :int  				=    {
    """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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						"marian"
    snake_case__							:   Union[str, Any]							=						["past_key_values"]
    snake_case__							:   str							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(       self    ,							lowercase__=58_101    ,							lowercase__=None    ,							lowercase__=1_024    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__="gelu"    ,							lowercase__=1_024    ,							lowercase__=0.1    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=58_100    ,							lowercase__=False    ,							lowercase__=58_100    ,							lowercase__=0    ,							lowercase__=0    ,							lowercase__=True    ,							**lowercase__    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       decoder_vocab_size or vocab_size
         SCREAMING_SNAKE_CASE					:			int    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       d_model
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       decoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       decoder_layers
         SCREAMING_SNAKE_CASE					:			str    	=       decoder_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dropout
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_dropout
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       activation_dropout
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       activation_function
         SCREAMING_SNAKE_CASE					:			Tuple    	=       init_std
         SCREAMING_SNAKE_CASE					:			int    	=       encoder_layerdrop
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       decoder_layerdrop
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_cache
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			int    	=       scale_embedding  # scale factor will be sqrt(d_model) if True
         SCREAMING_SNAKE_CASE					:			str    	=       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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







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

              if self.use_past:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch'}
                   SCREAMING_SNAKE_CASE					:			Any    	=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   SCREAMING_SNAKE_CASE					:			str    	=       {0: 'batch', 1: 'decoder_sequence'}
                   SCREAMING_SNAKE_CASE					:			int    	=       {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.
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         else:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       super().outputs
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       super(lowercase__    ,							self ).outputs
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			int    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )

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

              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(lowercase__    ,							lowercase__ )]    ,							dim=1 )

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

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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         # 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
         SCREAMING_SNAKE_CASE					:			List[str]    	=       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
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer.num_special_tokens_to_add(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       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
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       dict(tokenizer(lowercase__    ,							return_tensors=lowercase__ ) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

         else:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self._generate_dummy_inputs_for_causal_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       super()._flatten_past_key_values_(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         else:
              SCREAMING_SNAKE_CASE					:			Any    	=       super(lowercase__    ,							self )._flatten_past_key_values_(
                  lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4




'''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


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





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

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

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


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      torch.Tensor		):
          return mask
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [mask]

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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   UNetaDModel
    snake_case__							:   RePaintScheduler






    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__ )






    @torch.no_grad()
    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 250    ,							lowercase__ = 0.0    ,							lowercase__ = 10    ,							lowercase__ = 10    ,							lowercase__ = None    ,							lowercase__ = "pil"    ,							lowercase__ = True    ,							)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image

         SCREAMING_SNAKE_CASE					:			List[str]    	=       _preprocess_image(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       original_image.to(device=self.device    ,							dtype=self.unet.dtype )
         SCREAMING_SNAKE_CASE					:			Any    	=       _preprocess_mask(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       mask_image.to(device=self.device    ,							dtype=self.unet.dtype )

         SCREAMING_SNAKE_CASE					:			int    	=       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.""" )

         SCREAMING_SNAKE_CASE					:			Dict    	=       original_image.shape
         SCREAMING_SNAKE_CASE					:			Any    	=       randn_tensor(lowercase__    ,							generator=lowercase__    ,							device=self.device    ,							dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(lowercase__    ,							lowercase__    ,							lowercase__    ,							self.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       eta

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler.timesteps[0] + 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.unet(lowercase__    ,							lowercase__ ).sample
                   # compute previous image: x_t -> x_t-1
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler.undo_step(lowercase__    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       t

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (image / 2 + 0.5).clamp(0    ,							1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 ).numpy()
         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )



