



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							):
       UpperCAmelCase__				:					Dict        =  ViTImageProcessor if is_vision_available() else None
       @property
       def 					snake_case_       (			self       )     ->		Dict:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	(3, 32, 128)
          UpperCamelCase    :  Any							=	tempfile.mkdtemp()

          # fmt: off
          UpperCamelCase    :  Union[str, 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
          UpperCamelCase    :  Optional[Any]							=	dict(zip(SCREAMING_SNAKE_CASE_,			range(len(SCREAMING_SNAKE_CASE_       )       )       )       )

          UpperCamelCase    :  Optional[int]							=	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(SCREAMING_SNAKE_CASE_       ) + '\n'       )

          UpperCamelCase    :  List[str]							=	{
              'do_normalize': False,
              'do_resize': True,
              'image_processor_type': 'ViTImageProcessor',
              'resample': 3,
              'size': {'height': 32, 'width': 128},
          }
          UpperCamelCase    :  int							=	os.path.join(self.tmpdirname,			SCREAMING_SNAKE_CASE_       )
          with open(self.image_processor_file,			'w',			encoding='utf-8'       ) as fp:
             json.dump(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
          return MgpstrTokenizer.from_pretrained(self.tmpdirname,			**SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          return ViTImageProcessor.from_pretrained(self.tmpdirname,			**SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		str:
          shutil.rmtree(self.tmpdirname       )
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Union[str, Any]							=	np.random.randint(255,			size=(3, 30, 400),			dtype=np.uinta       )

          UpperCamelCase    :  List[str]							=	Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,			0,			-1       )       )

          return image_input
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Tuple							=	self.get_tokenizer()
          UpperCamelCase    :  Dict							=	self.get_image_processor()

          UpperCamelCase    :  Tuple							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )
          processor.save_pretrained(self.tmpdirname       )
          UpperCamelCase    :  Dict							=	MgpstrProcessor.from_pretrained(self.tmpdirname,			use_fast=SCREAMING_SNAKE_CASE_       )

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

          self.assertEqual(processor.image_processor.to_json_string(),			image_processor.to_json_string()       )
          self.assertIsInstance(processor.image_processor,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Union[str, Any]							=	self.get_tokenizer()
          UpperCamelCase    :  List[str]							=	self.get_image_processor()

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

          UpperCamelCase    :  int							=	self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)'       )
          UpperCamelCase    :  Dict							=	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,			padding_value=1.0       )

          UpperCamelCase    :  List[str]							=	MgpstrProcessor.from_pretrained(
              self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=SCREAMING_SNAKE_CASE_,			padding_value=1.0       )

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

          self.assertEqual(processor.image_processor.to_json_string(),			image_processor_add_kwargs.to_json_string()       )
          self.assertIsInstance(processor.image_processor,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Tuple							=	self.get_image_processor()
          UpperCamelCase    :  Tuple							=	self.get_tokenizer()

          UpperCamelCase    :  Any							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  str							=	self.prepare_image_inputs()

          UpperCamelCase    :  Any							=	image_processor(SCREAMING_SNAKE_CASE_,			return_tensors='np'       )
          UpperCamelCase    :  str							=	processor(images=SCREAMING_SNAKE_CASE_,			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 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Dict							=	self.get_image_processor()
          UpperCamelCase    :  List[str]							=	self.get_tokenizer()

          UpperCamelCase    :  Tuple							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	'test'

          UpperCamelCase    :  Any							=	processor(text=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	tokenizer(SCREAMING_SNAKE_CASE_       )
          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key],			encoded_processor[key]       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  List[str]							=	self.get_image_processor()
          UpperCamelCase    :  Dict							=	self.get_tokenizer()

          UpperCamelCase    :  Tuple							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  str							=	'test'
          UpperCamelCase    :  Optional[Any]							=	self.prepare_image_inputs()

          UpperCamelCase    :  Tuple							=	processor(text=SCREAMING_SNAKE_CASE_,			images=SCREAMING_SNAKE_CASE_       )

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

          # test if it raises when no input is passed
          with pytest.raises(SCREAMING_SNAKE_CASE_       ):
             processor()
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Tuple							=	self.get_image_processor()
          UpperCamelCase    :  Union[str, Any]							=	self.get_tokenizer()

          UpperCamelCase    :  Optional[int]							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase    :  str							=	processor.char_decode(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[int]							=	[seq.replace(' ',			''       ) for seq in decoded_tok]

          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Union[str, Any]							=	self.get_image_processor()
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer()

          UpperCamelCase    :  Any							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	None
          UpperCamelCase    :  Optional[int]							=	self.prepare_image_inputs()

          UpperCamelCase    :  List[Any]							=	processor(text=SCREAMING_SNAKE_CASE_,			images=SCREAMING_SNAKE_CASE_       )

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






       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Union[str, Any]							=	self.get_image_processor()
          UpperCamelCase    :  List[Any]							=	self.get_tokenizer()

          UpperCamelCase    :  Tuple							=	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE_,			image_processor=SCREAMING_SNAKE_CASE_       )

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

          UpperCamelCase    :  Any							=	processor.batch_decode([char_input, bpe_input, wp_input]       )

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








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor



class 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=18,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=400,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			)     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  List[Any]							=	batch_size
          UpperCamelCase    :  int							=	num_channels
          UpperCamelCase    :  Any							=	image_size
          UpperCamelCase    :  Optional[int]							=	min_resolution
          UpperCamelCase    :  Optional[Any]							=	max_resolution
          UpperCamelCase    :  Union[str, Any]							=	do_resize
          UpperCamelCase    :  List[Any]							=	size
          UpperCamelCase    :  int							=	do_normalize






       def 					snake_case_       (			self       )     ->		Tuple:
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]       ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  ImageGPTImageProcessor if is_vision_available() else None
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  str							=	ImageGPTImageProcessingTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'clusters'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Tuple							=	self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size,			{'height': 18, 'width': 18}       )

          UpperCamelCase    :  Dict							=	self.image_processing_class.from_dict(self.image_processor_dict,			size=42       )
          self.assertEqual(image_processor.size,			{'height': 42, 'width': 42}       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.image_processing_class(**self.image_processor_dict       )
          UpperCamelCase    :  int							=	json.loads(image_processor.to_json_string()       )
          for key, value in self.image_processor_dict.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			obj[key]       )       )
             else:
                self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  List[str]							=	os.path.join(SCREAMING_SNAKE_CASE_,			'image_processor.json'       )
             image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Tuple							=	self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  List[Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  str							=	self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  Union[str, Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )






       @unittest.skip('ImageGPT requires clusters at initialization'       )
       def 					snake_case_       (			self       )     ->		str:
          pass






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	load_dataset('hf-internal-testing/fixtures_image_utils'       ,     split='test'	)

   UpperCamelCase    :  int							=	Image.open(dataset[4]['file']	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(dataset[5]['file']	)

   UpperCamelCase    :  str							=	[imagea, imagea]

   return images



@require_vision
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

          UpperCamelCase    :  List[str]							=	prepare_images()

          # test non-batched
          UpperCamelCase    :  int							=	image_processing(images[0],			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(1, 1024)       )

          UpperCamelCase    :  Union[str, Any]							=	[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist(),			SCREAMING_SNAKE_CASE_       )

          # test batched
          UpperCamelCase    :  Tuple							=	image_processing(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(2, 1024)       )

          UpperCamelCase    :  Optional[Any]							=	[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist(),			SCREAMING_SNAKE_CASE_       )



