



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  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		snake_case_			=ViTImageProcessor if is_vision_available() else None



		@property
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  (3, 32, 1_28)
						lowerCAmelCase__  :       Any				=  tempfile.mkdtemp()

						# fmt: off
						lowerCAmelCase__  :       Optional[Any]				=  ['''[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__  :       Any				=  dict(zip(__lowerCamelCase	,range(len(__lowerCamelCase				)				)				)				)

						lowerCAmelCase__  :       str				=  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(__lowerCamelCase				) + '''\n'''				)

						lowerCAmelCase__  :       Any				=  {
						    '''do_normalize''': False,
						    '''do_resize''': True,
						    '''image_processor_type''': '''ViTImageProcessor''',
						    '''resample''': 3,
						    '''size''': {'''height''': 32, '''width''': 1_28},
						}
						lowerCAmelCase__  :       Any				=  os.path.join(self.tmpdirname	,__lowerCamelCase				)
						with open(self.image_processor_file	,'''w'''	,encoding='''utf-8'''				) as fp:
										json.dump(__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						return MgpstrTokenizer.from_pretrained(self.tmpdirname	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,**__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						return ViTImageProcessor.from_pretrained(self.tmpdirname	,**__lowerCamelCase				)



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

						"""simple docstring"""






						shutil.rmtree(self.tmpdirname				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  np.random.randint(2_55	,size=(3, 30, 4_00)	,dtype=np.uinta				)

						lowerCAmelCase__  :       Optional[Any]				=  Image.fromarray(np.moveaxis(__lowerCamelCase	,0	,-1				)				)

						return image_input



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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

						lowerCAmelCase__  :       int				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)
						processor.save_pretrained(self.tmpdirname				)
						lowerCAmelCase__  :       List[str]				=  MgpstrProcessor.from_pretrained(self.tmpdirname	,use_fast=__lowerCamelCase				)

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

						self.assertEqual(processor.image_processor.to_json_string()	,image_processor.to_json_string()				)
						self.assertIsInstance(processor.image_processor	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_tokenizer()
						lowerCAmelCase__  :       Optional[int]				=  self.get_image_processor()

						lowerCAmelCase__  :       Optional[Any]				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)
						processor.save_pretrained(self.tmpdirname				)

						lowerCAmelCase__  :       Optional[int]				=  self.get_tokenizer(bos_token='''(BOS)'''	,eos_token='''(EOS)'''				)
						lowerCAmelCase__  :       Dict				=  self.get_image_processor(do_normalize=__lowerCamelCase	,padding_value=1.0				)

						lowerCAmelCase__  :       Dict				=  MgpstrProcessor.from_pretrained(
						    self.tmpdirname	,bos_token='''(BOS)'''	,eos_token='''(EOS)'''	,do_normalize=__lowerCamelCase	,padding_value=1.0				)

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

						self.assertEqual(processor.image_processor.to_json_string()	,image_processor_add_kwargs.to_json_string()				)
						self.assertIsInstance(processor.image_processor	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






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

						lowerCAmelCase__  :       Union[str, Any]				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)

						lowerCAmelCase__  :       str				=  self.prepare_image_inputs()

						lowerCAmelCase__  :       Any				=  image_processor(__lowerCamelCase	,return_tensors='''np'''				)
						lowerCAmelCase__  :       List[str]				=  processor(images=__lowerCamelCase	,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 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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

						lowerCAmelCase__  :       str				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  '''test'''

						lowerCAmelCase__  :       Union[str, Any]				=  processor(text=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[int]				=  tokenizer(__lowerCamelCase				)
						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	,encoded_processor[key]				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






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

						lowerCAmelCase__  :       List[Any]				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)

						lowerCAmelCase__  :       str				=  '''test'''
						lowerCAmelCase__  :       Tuple				=  self.prepare_image_inputs()

						lowerCAmelCase__  :       Optional[int]				=  processor(text=__lowerCamelCase	,images=__lowerCamelCase				)

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

						# test if it raises when no input is passed
						with pytest.raises(__lowerCamelCase				):
										processor()



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_image_processor()
						lowerCAmelCase__  :       str				=  self.get_tokenizer()

						lowerCAmelCase__  :       int				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__  :       str				=  processor.char_decode(__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  tokenizer.batch_decode(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  [seq.replace(''' '''	,''''''				) for seq in decoded_tok]

						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)



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

						"""simple docstring"""






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

						lowerCAmelCase__  :       List[str]				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)

						lowerCAmelCase__  :       List[str]				=  None
						lowerCAmelCase__  :       Dict				=  self.prepare_image_inputs()

						lowerCAmelCase__  :       str				=  processor(text=__lowerCamelCase	,images=__lowerCamelCase				)

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



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






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

						lowerCAmelCase__  :       Tuple				=  MgpstrProcessor(tokenizer=__lowerCamelCase	,image_processor=__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  torch.randn(1	,27	,38				)
						lowerCAmelCase__  :       Optional[Any]				=  torch.randn(1	,27	,5_02_57				)
						lowerCAmelCase__  :       List[str]				=  torch.randn(1	,27	,3_05_22				)

						lowerCAmelCase__  :       Tuple				=  processor.batch_decode([char_input, bpe_input, wp_input]				)

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






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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


__snake_case       :	List[Any]            =logging.get_logger(__name__)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=["""pixel_values"""]



		def __init__(self	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = PILImageResampling.BILINEAR	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = True	,__lowerCamelCase = 1 / 2_55	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  size if size is not None else {'''shortest_edge''': 2_56}
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''				)
						lowerCAmelCase__  :       Dict				=  do_resize
						lowerCAmelCase__  :       str				=  size
						lowerCAmelCase__  :       Tuple				=  resample
						lowerCAmelCase__  :       List[Any]				=  do_center_crop
						lowerCAmelCase__  :       Dict				=  crop_size
						lowerCAmelCase__  :       Optional[int]				=  do_rescale
						lowerCAmelCase__  :       List[str]				=  rescale_factor
						lowerCAmelCase__  :       List[Any]				=  do_normalize
						lowerCAmelCase__  :       Optional[Any]				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__  :       str				=  image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = PILImageResampling.BICUBIC	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
						lowerCAmelCase__  :       str				=  get_resize_output_image_size(__lowerCamelCase	,size=size['''shortest_edge''']	,default_to_square=__lowerCamelCase				)
						return resize(__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase				)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
						return center_crop(__lowerCamelCase	,size=(size['''height'''], size['''width'''])	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase				)  ->    np.ndarray:

						"""simple docstring"""






						return rescale(__lowerCamelCase	,scale=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						return normalize(__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = ChannelDimension.FIRST	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__  :       Optional[int]				=  size if size is not None else self.size
						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  resample if resample is not None else self.resample
						lowerCAmelCase__  :       Union[str, Any]				=  do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__  :       Any				=  crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__  :       List[str]				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''				)
						lowerCAmelCase__  :       List[str]				=  do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__  :       str				=  rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__  :       int				=  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__  :       Optional[Any]				=  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__  :       List[Any]				=  image_std if image_std is not None else self.image_std

						lowerCAmelCase__  :       Any				=  make_list_of_images(__lowerCamelCase				)

						if not valid_images(__lowerCamelCase				):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

						if do_resize and size is None:
										raise ValueError('''Size must be specified if do_resize is True.'''				)

						if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

						if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

						# All transformations expect numpy arrays.
						lowerCAmelCase__  :       Union[str, Any]				=  [to_numpy_array(__lowerCamelCase				) for image in images]

						if do_resize:
										lowerCAmelCase__  :       str				=  [self.resize(image=__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase				) for image in images]

						if do_center_crop:
										lowerCAmelCase__  :       List[Any]				=  [self.center_crop(image=__lowerCamelCase	,size=__lowerCamelCase				) for image in images]

						if do_rescale:
										lowerCAmelCase__  :       Any				=  [self.rescale(image=__lowerCamelCase	,scale=__lowerCamelCase				) for image in images]

						if do_normalize:
										lowerCAmelCase__  :       Tuple				=  [self.normalize(image=__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       int				=  [to_channel_dimension_format(__lowerCamelCase	,__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       int				=  {'''pixel_values''': images}
						return BatchFeature(data=__lowerCamelCase	,tensor_type=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(__lowerCamelCase				) != len(__lowerCamelCase				):
														raise ValueError(
														    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

										if is_torch_tensor(__lowerCamelCase				):
														lowerCAmelCase__  :       str				=  target_sizes.numpy()

										lowerCAmelCase__  :       Optional[Any]				=  []

										for idx in range(len(__lowerCamelCase				)				):
														lowerCAmelCase__  :       Optional[int]				=  torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0				)	,size=target_sizes[idx]	,mode='''bilinear'''	,align_corners=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[Any]				=  resized_logits[0].argmax(dim=0				)
														semantic_segmentation.append(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       Optional[Any]				=  logits.argmax(dim=1				)
										lowerCAmelCase__  :       int				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

						return semantic_segmentation

