


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


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



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Any:
    warnings.warn(
        'The preprocess method is deprecated and will be removed in a future version. Please'
        ' use VaeImageProcessor.preprocess instead'							, lowerCamelCase_							, )
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return image
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							int      		=		[image]

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

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



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						str:
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return mask
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							Dict      		=		[mask]

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






class _lowerCamelCase(						_a   ):
       lowercase_ :	UNetaDModel
       lowercase_ :	RePaintScheduler
       def __init__(					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           super().__init__()
           self.register_modules(unet=lowerCamelCase,      scheduler=lowerCamelCase)




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 2_50,      lowerCamelCase = 0.0,      lowerCamelCase = 10,      lowerCamelCase = 10,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      )       ->   Union[ImagePipelineOutput, Tuple]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		image

           _lowercase       :							List[str]      		=		_preprocess_image(lowerCamelCase)
           _lowercase       :							List[Any]      		=		original_image.to(device=self.device,      dtype=self.unet.dtype)
           _lowercase       :							int      		=		_preprocess_mask(lowerCamelCase)
           _lowercase       :							Dict      		=		mask_image.to(device=self.device,      dtype=self.unet.dtype)

           _lowercase       :							Optional[int]      		=		original_image.shape[0]

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

           _lowercase       :							int      		=		original_image.shape
           _lowercase       :							Dict      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=self.unet.dtype)

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

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

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

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

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor







@require_torch
@require_vision
class   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  ViTImageProcessor if is_vision_available() else None
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					(3, 32, 128)
       __lowerCAmelCase					:						List[str]					=					tempfile.mkdtemp()

       # fmt: off
       __lowerCAmelCase					:						List[str]					=					['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       __lowerCAmelCase					:						Optional[int]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)

       __lowerCAmelCase					:						Tuple					=					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''vocab_file''']					)
       with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             fp.write(json.dumps(A_					) + '''\n'''					)

       __lowerCAmelCase					:						Union[str, Any]					=					{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __lowerCAmelCase					:						Optional[Any]					=					os.path.join(self.tmpdirname      ,							A_					)
       with open(self.image_processor_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             json.dump(A_      ,							A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return ViTImageProcessor.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)

       __lowerCAmelCase					:						str					=					Image.fromarray(np.moveaxis(A_      ,							0      ,							-1					)					)

       return image_input
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_tokenizer()
       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Union[str, Any]					=					MgpstrProcessor.from_pretrained(self.tmpdirname      ,							use_fast=A_					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_tokenizer()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						List[Any]					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						int					=					self.get_image_processor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						int					=					MgpstrProcessor.from_pretrained(
           self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=A_      ,							padding_value=1.0					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_image_processor()
       __lowerCAmelCase					:						Optional[Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Optional[int]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						Optional[Any]					=					image_processor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Tuple					=					processor(images=A_      ,							return_tensors='''np'''					)

       for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()      ,							input_processor[key].sum()      ,							delta=1e-2					)
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Optional[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Any					=					'''test'''

       __lowerCAmelCase					:						Dict					=					processor(text=A_					)

       __lowerCAmelCase					:						str					=					tokenizer(A_					)
       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						str					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					'''test'''
       __lowerCAmelCase					:						int					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						int					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							['''pixel_values''', '''labels''']					)

       # test if it raises when no input is passed
       with pytest.raises(A_					):
             processor()
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()
       __lowerCAmelCase					:						int					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[str]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       __lowerCAmelCase					:						Optional[int]					=					processor.char_decode(A_					)
       __lowerCAmelCase					:						Tuple					=					tokenizer.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					[seq.replace(''' '''      ,							''''''					) for seq in decoded_tok]

       self.assertListEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						Optional[Any]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						List[Any]					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							processor.model_input_names					)





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.get_image_processor()
       __lowerCAmelCase					:						List[str]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.randn(1      ,							27      ,							38					)
       __lowerCAmelCase					:						Optional[int]					=					torch.randn(1      ,							27      ,							5_0257					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.randn(1      ,							27      ,							3_0522					)

       __lowerCAmelCase					:						List[str]					=					processor.batch_decode([char_input, bpe_input, wp_input]					)

       self.assertListEqual(list(results.keys()					)      ,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']					)


