




'''simple docstring'''





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







      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			List[str]:
       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 			_UpperCamelCase      (				snake_case__	,       unittest.TestCase		):
      UpperCAmelCase_        =		ImageGPTImageProcessor if is_vision_available() else None
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       UpperCAmelCase__							 =						ImageGPTImageProcessingTester(self      )
      @property
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			int:
       return self.image_processor_tester.prepare_image_processor_dict()
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Any:
       UpperCAmelCase__							 =						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   :Optional[Any]      )     ->			Tuple:
       UpperCAmelCase__							 =						self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size ,      {"height": 18, "width": 18}      )

       UpperCAmelCase__							 =						self.image_processing_class.from_dict(self.image_processor_dict ,      size=42      )
       self.assertEqual(image_processor.size ,      {"height": 42, "width": 42}      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			int:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       UpperCAmelCase__							 =						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   :Optional[int]      )     ->			List[str]:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )

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

       UpperCAmelCase__							 =						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]      )     ->			int:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )

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

       UpperCAmelCase__							 =						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   :Union[str, Any]      )     ->			Optional[Any]:
       pass



def 						lowerCAmelCase		(		):




 """simple docstring"""







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

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

 UpperCAmelCase__							 =						[imagea, imagea]

 return images


@require_vision
@require_torch
class 			_UpperCamelCase      (				unittest.TestCase		):
      @slow
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Any:
       UpperCAmelCase__							 =						ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"      )

       UpperCAmelCase__							 =						prepare_images()

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

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

       UpperCAmelCase__							 =						[306, 191, 191]
       self.assertEqual(encoding.input_ids[0, :3].tolist() ,      _A      )

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

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

       UpperCAmelCase__							 =						[303, 13, 13]
       self.assertEqual(encoding.input_ids[1, -3:].tolist() ,      _A      )











import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_lowerCAmelCase						:   str		=   logging.get_logger(__name__)

_lowerCAmelCase						:   List[Any]		=   {"tokenizer_file": "tokenizer.json"}

_lowerCAmelCase						:   Tuple		=   {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      UpperCAmelCase_        =		None
      def __init__(			self   :List[Any] ,      lowerCamelCase   :Union[str, Any]=None ,      lowerCamelCase   :Optional[Any]=None ,      lowerCamelCase   :List[Any]=None ,      lowerCamelCase   :Union[str, Any]="<unk>" ,      lowerCamelCase   :Union[str, Any]="<s>" ,      lowerCamelCase   :Union[str, Any]="</s>" ,      lowerCamelCase   :str="<pad>" ,      lowerCamelCase   :Any=False ,      lowerCamelCase   :Optional[Any]=False ,      **lowerCamelCase   :Optional[Any] ,      )     ->			Any:
       super().__init__(
           lowerCamelCase ,      lowerCamelCase ,      tokenizer_file=lowerCamelCase ,      unk_token=lowerCamelCase ,      bos_token=lowerCamelCase ,      eos_token=lowerCamelCase ,      pad_token=lowerCamelCase ,      add_prefix_space=lowerCamelCase ,      clean_up_tokenization_spaces=lowerCamelCase ,      **lowerCamelCase ,      )
       UpperCAmelCase__							 =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
        UpperCAmelCase__							 =						getattr(lowerCamelCase ,      pre_tok_state.pop("type"      )      )
        UpperCAmelCase__							 =						add_prefix_space
        UpperCAmelCase__							 =						pre_tok_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						add_prefix_space
      def 	UpperCAmelCase_						(			self   :int ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :int      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						kwargs.get("is_split_into_words" ,      lowerCamelCase      )
       if not (self.add_prefix_space or not is_split_into_words):
        raise Exception(
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
            " pretokenized inputs."      )

       return super()._batch_encode_plus(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Any ,      *lowerCamelCase   :List[Any] ,      **lowerCamelCase   :Union[str, Any]      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						kwargs.get("is_split_into_words" ,      lowerCamelCase      )

       if not (self.add_prefix_space or not is_split_into_words):
        raise Exception(
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
            " pretokenized inputs."      )

       return super()._encode_plus(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       UpperCAmelCase__							 =						self._tokenizer.model.save(lowerCamelCase ,      name=lowerCamelCase      )
       return tuple(lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :"Conversation"      )     ->			List[int]:
       UpperCAmelCase__							 =						[]
       for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(lowerCamelCase ,      add_special_tokens=lowerCamelCase      ) + [self.eos_token_id]      )

       if len(lowerCamelCase      ) > self.model_max_length:
        UpperCAmelCase__							 =						input_ids[-self.model_max_length :]
       return input_ids





