'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import DeformableDetrImageProcessor





class 							lowercase						(						unittest.TestCase ):


   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	List[str]=7   ,		__lowerCAmelCase				:	Optional[Any]=3   ,		__lowerCAmelCase				:	List[str]=30   ,		__lowerCAmelCase				:	Optional[int]=400   ,		__lowerCAmelCase				:	str=True   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		__lowerCAmelCase				:	Dict=True   ,		__lowerCAmelCase				:	List[str]=[0.5, 0.5, 0.5]   ,		__lowerCAmelCase				:	Tuple=[0.5, 0.5, 0.5]   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	int=1 / 255   ,		__lowerCAmelCase				:	List[Any]=True   ,		)      ->					Any:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         lowercase_     =      size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      num_channels
         lowercase_     =      min_resolution
         lowercase_     =      max_resolution
         lowercase_     =      do_resize
         lowercase_     =      size
         lowercase_     =      do_normalize
         lowercase_     =      image_mean
         lowercase_     =      image_std
         lowercase_     =      do_rescale
         lowercase_     =      rescale_factor
         lowercase_     =      do_pad


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[str]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Union[str, Any]=False)      ->					Union[str, Any]:
         if not batched:
               lowercase_     =      image_inputs[0]
               if isinstance(__lowerCAmelCase   ,		Image.Image):
                     lowercase_ ,							lowercase_     =      image.size
               else:
                     lowercase_ ,							lowercase_     =      image.shape[1], image.shape[2]
               if w < h:
                     lowercase_     =      int(self.size["shortest_edge"] * h / w)
                     lowercase_     =      self.size["shortest_edge"]
               elif w > h:
                     lowercase_     =      self.size["shortest_edge"]
                     lowercase_     =      int(self.size["shortest_edge"] * w / h)
               else:
                     lowercase_     =      self.size["shortest_edge"]
                     lowercase_     =      self.size["shortest_edge"]

         else:
               lowercase_     =      []
               for image in image_inputs:
                     lowercase_ ,							lowercase_     =      self.get_expected_values([image])
                     expected_values.append((expected_height, expected_width))
               lowercase_     =      max(__lowerCAmelCase   ,		key=lambda __lowerCAmelCase: item[0])[0]
               lowercase_     =      max(__lowerCAmelCase   ,		key=lambda __lowerCAmelCase: item[1])[1]

         return expected_height, expected_width





@require_torch
@require_vision
class 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=DeformableDetrImageProcessor if is_vision_available() else None


   def __UpperCAmelCase       (   self				:	Any)      ->					Tuple:
         lowercase_     =      DeformableDetrImageProcessingTester(self)


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[str]:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Union[str, Any]:
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"image_std"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_rescale"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"do_pad"))
         self.assertTrue(hasattr(__lowerCAmelCase   ,		"size"))


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size   ,		{"shortest_edge": 18, "longest_edge": 1333})
         self.assertEqual(image_processor.do_pad   ,		__lowerCAmelCase)

         lowercase_     =      self.image_processing_class.from_dict(
             self.image_processor_dict   ,		size=42   ,		max_size=84   ,		pad_and_return_pixel_mask=__lowerCAmelCase)
         self.assertEqual(image_processor.size   ,		{"shortest_edge": 42, "longest_edge": 84})
         self.assertEqual(image_processor.do_pad   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int)      ->					Union[str, Any]:
         pass


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		Image.Image)

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

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase)

         self.assertEqual(
             encoded_images.shape   ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,		)

         # Test batched
         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

         lowercase_     =      image_processing(__lowerCAmelCase   ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape   ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,		)


   def __UpperCAmelCase       (   self				:	List[str])      ->					Union[str, Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase   ,		numpify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		np.ndarray)

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

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase)

         self.assertEqual(
             encoded_images.shape   ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,		)

         # Test batched
         lowercase_     =      image_processing(__lowerCAmelCase   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

         self.assertEqual(
             encoded_images.shape   ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,		)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Optional[Any]:
         # Initialize image_processing
         lowercase_     =      self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     =      prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=__lowerCAmelCase   ,		torchify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase   ,		torch.Tensor)

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

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase)

         self.assertEqual(
             encoded_images.shape   ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,		)

         # Test batched
         lowercase_     =      image_processing(__lowerCAmelCase   ,		return_tensors="pt").pixel_values

         lowercase_ ,							lowercase_     =      self.image_processor_tester.get_expected_values(__lowerCAmelCase   ,		batched=__lowerCAmelCase)

         self.assertEqual(
             encoded_images.shape   ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,		)


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Optional[Any]:
         # prepare image and target
         lowercase_     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"   ,		"r") as f:
               lowercase_     =      json.loads(f.read())

         lowercase_     =      {"image_id": 3_9769, "annotations": target}

         # encode them
         lowercase_     =      DeformableDetrImageProcessor()
         lowercase_     =      image_processing(images=__lowerCAmelCase   ,		annotations=__lowerCAmelCase   ,		return_tensors="pt")

         # verify pixel values
         lowercase_     =      torch.Size([1, 3, 800, 1066])
         self.assertEqual(encoding["pixel_values"].shape   ,		__lowerCAmelCase)

         lowercase_     =      torch.tensor([0.2796, 0.3138, 0.3481])
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,		__lowerCAmelCase   ,		atol=1e-4))

         # verify area
         lowercase_     =      torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,		__lowerCAmelCase))
         # verify boxes
         lowercase_     =      torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape   ,		__lowerCAmelCase)
         lowercase_     =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,		__lowerCAmelCase   ,		atol=1e-3))
         # verify image_id
         lowercase_     =      torch.tensor([3_9769])
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,		__lowerCAmelCase))
         # verify is_crowd
         lowercase_     =      torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,		__lowerCAmelCase))
         # verify class_labels
         lowercase_     =      torch.tensor([75, 75, 63, 65, 17, 17])
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,		__lowerCAmelCase))
         # verify orig_size
         lowercase_     =      torch.tensor([480, 640])
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,		__lowerCAmelCase))
         # verify size
         lowercase_     =      torch.tensor([800, 1066])
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,		__lowerCAmelCase))


   @slow
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         # prepare image, target and masks_path
         lowercase_     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"   ,		"r") as f:
               lowercase_     =      json.loads(f.read())

         lowercase_     =      {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

         lowercase_     =      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

         # encode them
         lowercase_     =      DeformableDetrImageProcessor(format="coco_panoptic")
         lowercase_     =      image_processing(images=__lowerCAmelCase   ,		annotations=__lowerCAmelCase   ,		masks_path=__lowerCAmelCase   ,		return_tensors="pt")

         # verify pixel values
         lowercase_     =      torch.Size([1, 3, 800, 1066])
         self.assertEqual(encoding["pixel_values"].shape   ,		__lowerCAmelCase)

         lowercase_     =      torch.tensor([0.2796, 0.3138, 0.3481])
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]   ,		__lowerCAmelCase   ,		atol=1e-4))

         # verify area
         lowercase_     =      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]   ,		__lowerCAmelCase))
         # verify boxes
         lowercase_     =      torch.Size([6, 4])
         self.assertEqual(encoding["labels"][0]["boxes"].shape   ,		__lowerCAmelCase)
         lowercase_     =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]   ,		__lowerCAmelCase   ,		atol=1e-3))
         # verify image_id
         lowercase_     =      torch.tensor([3_9769])
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]   ,		__lowerCAmelCase))
         # verify is_crowd
         lowercase_     =      torch.tensor([0, 0, 0, 0, 0, 0])
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]   ,		__lowerCAmelCase))
         # verify class_labels
         lowercase_     =      torch.tensor([17, 17, 63, 75, 75, 93])
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]   ,		__lowerCAmelCase))
         # verify masks
         lowercase_     =      82_2873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()   ,		__lowerCAmelCase)
         # verify orig_size
         lowercase_     =      torch.tensor([480, 640])
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]   ,		__lowerCAmelCase))
         # verify size
         lowercase_     =      torch.tensor([800, 1066])
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]   ,		__lowerCAmelCase))







'''simple docstring'''

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
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             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				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             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				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         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:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






