



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor

@require_vision
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							Tuple				)->Any:
      __SCREAMING_SNAKE_CASE       :    int       =	tempfile.mkdtemp()

      # fmt: off
      __SCREAMING_SNAKE_CASE       :    int       =	["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
      # fmt: on
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	dict(zip(UpperCamelCase     ,  range(len(UpperCamelCase				)				)				)				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	{"unk_token": "<unk>"}

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["vocab_file"]				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	os.path.join(self.tmpdirname     ,  VOCAB_FILES_NAMES["merges_file"]				)
      with open(self.vocab_file     ,  "w"     ,  encoding="utf-8"				) as fp:
       fp.write(json.dumps(UpperCamelCase				) + "\n"				)
      with open(self.merges_file     ,  "w"     ,  encoding="utf-8"				) as fp:
       fp.write("\n".join(UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{
          "do_resize": True,
          "size": 2_0,
          "do_center_crop": True,
          "crop_size": 1_8,
          "do_normalize": True,
          "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
          "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
      }
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	os.path.join(self.tmpdirname     ,  UpperCamelCase				)
      with open(self.image_processor_file     ,  "w"     ,  encoding="utf-8"				) as fp:
       json.dump(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]     ,  **UpperCamelCase :							Union[str, Any]				)->Optional[Any]:
      return CLIPTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]     ,  **UpperCamelCase :							Tuple				)->Dict:
      return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Any     ,  **UpperCamelCase :							Any				)->Tuple:
      return ViTImageProcessor.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
      shutil.rmtree(self.tmpdirname				)





     def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[np.random.randint(2_5_5     ,  size=(3, 3_0, 4_0_0)     ,  dtype=np.uinta				)]

      __SCREAMING_SNAKE_CASE       :    str       =	[Image.fromarray(np.moveaxis(UpperCamelCase     ,  0     ,  -1				)				) for x in image_inputs]

      return image_inputs





     def 	__snake_case    (       self :							str				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_tokenizer()
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_rust_tokenizer()
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_image_processor()

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)
      processor_slow.save_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Any       =	CLIPSegProcessor.from_pretrained(self.tmpdirname     ,  use_fast=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)
      processor_fast.save_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	CLIPSegProcessor.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     ,  UpperCamelCase				)
      self.assertIsInstance(processor_fast.tokenizer     ,  UpperCamelCase				)

      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     ,  UpperCamelCase				)
      self.assertIsInstance(processor_fast.image_processor     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							int				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Dict       =	CLIPSegProcessor(tokenizer=self.get_tokenizer()     ,  image_processor=self.get_image_processor()				)
      processor.save_pretrained(self.tmpdirname				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_tokenizer(bos_token="(BOS)"     ,  eos_token="(EOS)"				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_image_processor(do_normalize=UpperCamelCase     ,  padding_value=1.0				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	CLIPSegProcessor.from_pretrained(
          self.tmpdirname     ,  bos_token="(BOS)"     ,  eos_token="(EOS)"     ,  do_normalize=UpperCamelCase     ,  padding_value=1.0				)

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

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





     def 	__snake_case    (       self :							Tuple				)->Any:
      __SCREAMING_SNAKE_CASE       :    str       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processor(UpperCamelCase     ,  return_tensors="np"				)
      __SCREAMING_SNAKE_CASE       :    Any       =	processor(images=UpperCamelCase     ,  return_tensors="np"				)

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()     ,  input_processor[key].sum()     ,  delta=1E-2				)





     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    List[str]       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	"lower newer"

      __SCREAMING_SNAKE_CASE       :    Any       =	processor(text=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenizer(UpperCamelCase				)

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





     def 	__snake_case    (       self :							Dict				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    int       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"lower newer"
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.prepare_image_inputs()

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

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

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





     def 	__snake_case    (       self :							Optional[Any]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    str       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Any       =	self.prepare_image_inputs()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	processor(images=UpperCamelCase     ,  visual_prompt=UpperCamelCase				)

      self.assertListEqual(list(inputs.keys()				)     ,  ["pixel_values", "conditional_pixel_values"]				)

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






     def 	__snake_case    (       self :							List[str]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.get_image_processor()
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_tokenizer()

      __SCREAMING_SNAKE_CASE       :    Dict       =	CLIPSegProcessor(tokenizer=UpperCamelCase     ,  image_processor=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	processor.batch_decode(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	tokenizer.batch_decode(UpperCamelCase				)

      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)











from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Optional[NestedDataStructureLike[PathLike]] = None     ,  UpperCamelCase :							Optional[NamedSplit] = None     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	path_or_paths
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split if split or isinstance(UpperCamelCase     ,  UpperCamelCase				) else "train"
      __SCREAMING_SNAKE_CASE       :    Dict       =	features
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Tuple       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_proc
      __SCREAMING_SNAKE_CASE       :    int       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							Optional[int]				)->Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
      pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	features
      __SCREAMING_SNAKE_CASE       :    str       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Any       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    int       =	num_proc
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							str				)->Union[Dataset, IterableDataset]:
      pass






