
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


A     :	Dict    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    ['''pixel_values''']




				def __init__(     self		,     _snake_case = True		,     _snake_case = None		,     _snake_case = PILImageResampling.BILINEAR		,     _snake_case = True		,     _snake_case = None		,     _snake_case = True		,     _snake_case = 1 / 255		,     _snake_case = True		,     _snake_case = None		,     _snake_case = None		,     **_snake_case		,     )    ->      None:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    size if size is not None else {'''shortest_edge''': 256}
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								__a										=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								__a										=    get_size_dict(_snake_case		,     param_name='''crop_size'''				)
								__a										=    do_resize
								__a										=    size
								__a										=    resample
								__a										=    do_center_crop
								__a										=    crop_size
								__a										=    do_rescale
								__a										=    rescale_factor
								__a										=    do_normalize
								__a										=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__a										=    image_std if image_std is not None else IMAGENET_STANDARD_STD




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


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




								__a										=    get_size_dict(_snake_case				)
								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(_snake_case		,     size=(size['''height'''], size['''width'''])		,     data_format=_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case				)    ->      np.ndarray:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = None		,     **_snake_case		,     )    ->      np.ndarray:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = None		,     _snake_case = ChannelDimension.FIRST		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								__a										=    do_resize if do_resize is not None else self.do_resize
								__a										=    size if size is not None else self.size
								__a										=    get_size_dict(_snake_case		,     default_to_square=_snake_case				)
								__a										=    resample if resample is not None else self.resample
								__a										=    do_center_crop if do_center_crop is not None else self.do_center_crop
								__a										=    crop_size if crop_size is not None else self.crop_size
								__a										=    get_size_dict(_snake_case		,     param_name='''crop_size'''				)
								__a										=    do_rescale if do_rescale is not None else self.do_rescale
								__a										=    rescale_factor if rescale_factor is not None else self.rescale_factor
								__a										=    do_normalize if do_normalize is not None else self.do_normalize
								__a										=    image_mean if image_mean is not None else self.image_mean
								__a										=    image_std if image_std is not None else self.image_std

								__a										=    make_list_of_images(_snake_case				)

								if not valid_images(_snake_case				):
												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.
								__a										=    [to_numpy_array(_snake_case				) for image in images]

								if do_resize:
												__a										=    [self.resize(image=_snake_case		,     size=_snake_case		,     resample=_snake_case				) for image in images]

								if do_center_crop:
												__a										=    [self.center_crop(image=_snake_case		,     size=_snake_case				) for image in images]

								if do_rescale:
												__a										=    [self.rescale(image=_snake_case		,     scale=_snake_case				) for image in images]

								if do_normalize:
												__a										=    [self.normalize(image=_snake_case		,     mean=_snake_case		,     std=_snake_case				) for image in images]

								__a										=    [to_channel_dimension_format(_snake_case		,     _snake_case				) for image in images]

								__a										=    {'''pixel_values''': images}
								return BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    outputs.logits

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

												if is_torch_tensor(_snake_case				):
																__a										=    target_sizes.numpy()

												__a										=    []

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

								return semantic_segmentation
'''simple docstring'''




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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       ViTImageProcessor if is_vision_available() else None



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (3, 32, 128)
					lowerCAmelCase   					=  tempfile.mkdtemp()

					# fmt: off
					lowerCAmelCase   					=  ["""[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   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))

					lowerCAmelCase   					=  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   					=  {
					    """do_normalize""": False,
					    """do_resize""": True,
					    """image_processor_type""": """ViTImageProcessor""",
					    """resample""": 3,
					    """size""": {"""height""": 32, """width""": 128},
					}
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    __lowerCAmelCase)
					with open(self.image_processor_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							json.dump(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)

					lowerCAmelCase   					=  Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1))

					return image_input



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)
					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)""")
					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer(__lowerCAmelCase)
					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					lowerCAmelCase   					=  processor.char_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  tokenizer.batch_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  [seq.replace(""" """       ,    """""") for seq in decoded_tok]

					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

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







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    38)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    50257)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    30522)

					lowerCAmelCase   					=  processor.batch_decode([char_input, bpe_input, wp_input])

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




