
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   :					Union[str, Any]       ,					_A   :					Tuple       ,					_A   :					Optional[int]=7       ,					_A   :					str=3       ,					_A   :					List[Any]=1_8       ,					_A   :					Tuple=3_0       ,					_A   :					List[str]=4_0_0       ,					_A   :					List[str]=True       ,					_A   :					Dict=None       ,					_A   :					Optional[int]=True       ,					)		->		Optional[int]:
         snake_case				  =      size if size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_normalize





     def 	UpperCAmelCase(self   :					Tuple     )		->		Dict:
         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__       :							str     =	ImageGPTImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					str     )		->		Optional[Any]:
         snake_case				  =      ImageGPTImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		List[str]:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Dict     )		->		str:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"clusters"     )     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_normalize"     )     )





     def 	UpperCAmelCase(self   :					List[str]     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2     )
         self.assertEqual(image_processor.size       ,					{"height": 4_2, "width": 4_2}     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[str]:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         snake_case				  =      json.loads(image_processor.to_json_string()     )
         for key, value in self.image_processor_dict.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(_A       ,					obj[key]     )     )
             else:
                 self.assertEqual(obj[key]       ,					_A     )





     def 	UpperCAmelCase(self   :					str     )		->		Any:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )

         with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case				  =      os.path.join(_A       ,					"image_processor.json"     )
             image_processor_first.to_json_file(_A     )
             snake_case				  =      self.image_processing_class.from_json_file(_A     ).to_dict()

         snake_case				  =      image_processor_first.to_dict()
         for key, value in image_processor_first.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(_A       ,					image_processor_second[key]     )     )
             else:
                 self.assertEqual(image_processor_first[key]       ,					_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[int]:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )

         with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(_A     )
             snake_case				  =      self.image_processing_class.from_pretrained(_A     ).to_dict()

         snake_case				  =      image_processor_first.to_dict()
         for key, value in image_processor_first.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(_A       ,					image_processor_second[key]     )     )
             else:
                 self.assertEqual(image_processor_first[key]       ,					_A     )





     @unittest.skip("ImageGPT requires clusters at initialization"     )
     def 	UpperCAmelCase(self   :					List[Any]     )		->		Any:
         pass



def 		lowercase_    (     )							->							List[str]:
    """simple docstring"""

    snake_case				  =      load_dataset("hf-internal-testing/fixtures_image_utils"				,	split="test"	)

    snake_case				  =      Image.open(dataset[4]["file"]	)
    snake_case				  =      Image.open(dataset[5]["file"]	)

    snake_case				  =      [imagea, imagea]

    return images







@require_vision
@require_torch
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         snake_case				  =      ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"     )

         snake_case				  =      prepare_images()

         # test non-batched
         snake_case				  =      image_processing(images[0]       ,					return_tensors="pt"     )

         self.assertIsInstance(encoding.input_ids       ,					torch.LongTensor     )
         self.assertEqual(encoding.input_ids.shape       ,					(1, 1_0_2_4)     )

         snake_case				  =      [3_0_6, 1_9_1, 1_9_1]
         self.assertEqual(encoding.input_ids[0, :3].tolist()       ,					_A     )

         # test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     )

         self.assertIsInstance(encoding.input_ids       ,					torch.LongTensor     )
         self.assertEqual(encoding.input_ids.shape       ,					(2, 1_0_2_4)     )

         snake_case				  =      [3_0_3, 1_3, 1_3]
         self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,					_A     )



import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	PhobertTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case				  =      ["T@@", "i", "I", "R@@", "r", "e@@"]
         snake_case				  =      dict(zip(_A       ,					range(len(_A     )     )     )     )
         snake_case				  =      ["#version: 0.2", "l à</w>"]
         snake_case				  =      {"unk_token": "<unk>"}

         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["merges_file"]     )

         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             for token in vocab_tokens:
                 fp.write(f'{token} {vocab_tokens[token]}\n'     )
         with open(self.merges_file       ,					"w"       ,					encoding="utf-8"     ) as fp:
             fp.write("\n".join(_A     )     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         kwargs.update(self.special_tokens_map     )
         return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[Any]     )		->		str:
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         snake_case				  =      PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map     )
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         snake_case				  =      tokenizer.tokenize(_A     )
         print(_A     )
         self.assertListEqual(_A       ,					_A     )

         snake_case				  =      tokens + [tokenizer.unk_token]

         snake_case				  =      [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )       ,					_A     )

