

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			tempfile.mkdtemp()

							A__        =			[
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							A__        =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]       )
							with open(self.vocab_file      ,      "w"      ,      encoding="utf-8"       ) as vocab_writer:
											vocab_writer.write("".join([x + "\n" for x in vocab_tokens]       )       )

							A__        =			{
							    "do_resize": True,
							    "size": 20,
							    "do_center_crop": True,
							    "crop_size": 18,
							    "do_normalize": True,
							    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
							    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
							}
							A__        =			os.path.join(self.tmpdirname      ,      lowercase       )
							with open(self.image_processor_file      ,      "w"      ,      encoding="utf-8"       ) as fp:
											json.dump(lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						Dict:
							'''simple docstring'''






							return BertTokenizer.from_pretrained(self.tmpdirname      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							return BertTokenizerFast.from_pretrained(self.tmpdirname      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      **lowercase       )  ->						Any:
							'''simple docstring'''






							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,      **lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta       )]
							A__        =			[Image.fromarray(np.moveaxis(lowercase      ,      0      ,      -1       )       ) for x in image_inputs]
							return image_inputs


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.get_tokenizer()
							A__        =			self.get_rust_tokenizer()
							A__        =			self.get_image_processor()

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )
							processor_slow.save_pretrained(self.tmpdirname       )
							A__        =			AlignProcessor.from_pretrained(self.tmpdirname      ,      use_fast=lowercase       )

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )
							processor_fast.save_pretrained(self.tmpdirname       )
							A__        =			AlignProcessor.from_pretrained(self.tmpdirname       )

							self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()       )
							self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()       )
							self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()       )
							self.assertIsInstance(processor_slow.tokenizer      ,      lowercase       )
							self.assertIsInstance(processor_fast.tokenizer      ,      lowercase       )

							self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()       )
							self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()       )
							self.assertIsInstance(processor_slow.image_processor      ,      lowercase       )
							self.assertIsInstance(processor_fast.image_processor      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			AlignProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()       )
							processor.save_pretrained(self.tmpdirname       )

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

							A__        =			AlignProcessor.from_pretrained(
							    self.tmpdirname      ,      bos_token="(BOS)"      ,      eos_token="(EOS)"      ,      do_normalize=lowercase      ,      padding_value=1.0       )

							self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.tokenizer      ,      lowercase       )

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


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.get_image_processor()
							A__        =			self.get_tokenizer()

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )

							A__        =			self.prepare_image_inputs()

							A__        =			image_processor(lowercase      ,      return_tensors="np"       )
							A__        =			processor(images=lowercase      ,      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       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.get_image_processor()
							A__        =			self.get_tokenizer()

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )

							A__        =			"lower newer"

							A__        =			processor(text=lowercase       )

							A__        =			tokenizer(lowercase      ,      padding="max_length"      ,      max_length=64       )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			self.get_image_processor()
							A__        =			self.get_tokenizer()

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )

							A__        =			"lower newer"
							A__        =			self.prepare_image_inputs()

							A__        =			processor(text=lowercase      ,      images=lowercase       )

							self.assertListEqual(list(inputs.keys()       )      ,      ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]       )

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


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			self.get_image_processor()
							A__        =			self.get_tokenizer()

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )

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

							A__        =			processor.batch_decode(lowercase       )
							A__        =			tokenizer.batch_decode(lowercase       )

							self.assertListEqual(lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.get_image_processor()
							A__        =			self.get_tokenizer()

							A__        =			AlignProcessor(tokenizer=lowercase      ,      image_processor=lowercase       )

							A__        =			"lower newer"
							A__        =			self.prepare_image_inputs()

							A__        =			processor(text=lowercase      ,      images=lowercase       )

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




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import PoolFormerImageProcessor

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=0.9      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      )  ->						str:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 30}
							A__        =			crop_size if crop_size is not None else {"height": 30, "width": 30}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize_and_center_crop
							A__        =			size
							A__        =			crop_pct
							A__        =			crop_size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       PoolFormerImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			PoolFormerImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize_and_center_crop"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )
							self.assertTrue(hasattr(lowercase      ,      "crop_pct"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 30, "width": 30}       )

							A__        =			self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      Image.Image       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values

							self.assertEqual(
							    encoded_images.shape      ,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      numpify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      np.ndarray       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      torchify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      torch.Tensor       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )      ,      )

