



'''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 DetrImageProcessor


class 				snake_case (unittest.TestCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=7     ,UpperCAmelCase_=3     ,UpperCAmelCase_=30     ,UpperCAmelCase_=400     ,UpperCAmelCase_=True     ,UpperCAmelCase_=None     ,UpperCAmelCase_=True     ,UpperCAmelCase_=1 / 255     ,UpperCAmelCase_=True     ,UpperCAmelCase_=[0.5, 0.5, 0.5]     ,UpperCAmelCase_=[0.5, 0.5, 0.5]     ,UpperCAmelCase_=True     ,)  ->							str:
           # 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": 1_333}
           lowercase__		       =						parent
           lowercase__		       =						batch_size
           lowercase__		       =						num_channels
           lowercase__		       =						min_resolution
           lowercase__		       =						max_resolution
           lowercase__		       =						do_resize
           lowercase__		       =						size
           lowercase__		       =						do_rescale
           lowercase__		       =						rescale_factor
           lowercase__		       =						do_normalize
           lowercase__		       =						image_mean
           lowercase__		       =						image_std
           lowercase__		       =						do_pad



       def 							_a      (				self      )  ->							Tuple:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_pad": self.do_pad,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False      )  ->							Tuple:
           if not batched:
               lowercase__		       =						image_inputs[0]
               if isinstance(UpperCAmelCase_     ,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(UpperCAmelCase_     ,key=lambda UpperCAmelCase_      : item[0]      )[0]
               lowercase__		       =						max(UpperCAmelCase_     ,key=lambda UpperCAmelCase_      : item[1]      )[1]

           return expected_height, expected_width





@require_torch
@require_vision
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Any						   =					DetrImageProcessor if is_vision_available() else None



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						DetrImageProcessingTester(self      )



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



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"image_mean"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"image_std"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"do_normalize"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"do_rescale"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"rescale_factor"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"do_resize"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"size"      )      )
           self.assertTrue(hasattr(UpperCAmelCase_     ,"do_pad"      )      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size     ,{"shortest_edge": 18, "longest_edge": 1_333}      )
           self.assertEqual(image_processor.do_pad     ,UpperCAmelCase_      )

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



       def 							_a      (				self      )  ->							Optional[Any]:
           pass



       def 							_a      (				self      )  ->							int:
           # 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=UpperCAmelCase_      )
           for image in image_inputs:
               self.assertIsInstance(UpperCAmelCase_     ,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(UpperCAmelCase_      )

           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(UpperCAmelCase_     ,batched=UpperCAmelCase_      )

           lowercase__		       =						image_processing(UpperCAmelCase_     ,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 							_a      (				self      )  ->							List[str]:
           # 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=UpperCAmelCase_     ,numpify=UpperCAmelCase_      )
           for image in image_inputs:
               self.assertIsInstance(UpperCAmelCase_     ,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(UpperCAmelCase_      )

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

           # Test batched
           lowercase__		       =						image_processing(UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(UpperCAmelCase_     ,batched=UpperCAmelCase_      )

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



       def 							_a      (				self      )  ->							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=UpperCAmelCase_     ,torchify=UpperCAmelCase_      )
           for image in image_inputs:
               self.assertIsInstance(UpperCAmelCase_     ,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(UpperCAmelCase_      )

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

           # Test batched
           lowercase__		       =						image_processing(UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(UpperCAmelCase_     ,batched=UpperCAmelCase_      )

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



       @slow
       def 							_a      (				self      )  ->							Union[str, 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": 39_769, "annotations": target}

           # encode them
           lowercase__		       =						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"      )
           lowercase__		       =						image_processing(images=UpperCAmelCase_     ,annotations=UpperCAmelCase_     ,return_tensors="pt"      )

           # verify pixel values
           lowercase__		       =						torch.Size([1, 3, 800, 1_066]      )
           self.assertEqual(encoding["pixel_values"].shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           # verify area
           lowercase__		       =						torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,UpperCAmelCase_      )      )
           # verify boxes
           lowercase__		       =						torch.Size([6, 4]      )
           self.assertEqual(encoding["labels"][0]["boxes"].shape     ,UpperCAmelCase_      )
           lowercase__		       =						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,UpperCAmelCase_     ,atol=1E-3      )      )
           # verify image_id
           lowercase__		       =						torch.tensor([39_769]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,UpperCAmelCase_      )      )
           # verify is_crowd
           lowercase__		       =						torch.tensor([0, 0, 0, 0, 0, 0]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,UpperCAmelCase_      )      )
           # verify class_labels
           lowercase__		       =						torch.tensor([75, 75, 63, 65, 17, 17]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,UpperCAmelCase_      )      )
           # verify orig_size
           lowercase__		       =						torch.tensor([480, 640]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,UpperCAmelCase_      )      )
           # verify size
           lowercase__		       =						torch.tensor([800, 1_066]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,UpperCAmelCase_      )      )







       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           # 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": 39_769, "segments_info": target}

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

           # encode them
           lowercase__		       =						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"      )
           lowercase__		       =						image_processing(images=UpperCAmelCase_     ,annotations=UpperCAmelCase_     ,masks_path=UpperCAmelCase_     ,return_tensors="pt"      )

           # verify pixel values
           lowercase__		       =						torch.Size([1, 3, 800, 1_066]      )
           self.assertEqual(encoding["pixel_values"].shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           # verify area
           lowercase__		       =						torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,UpperCAmelCase_      )      )
           # verify boxes
           lowercase__		       =						torch.Size([6, 4]      )
           self.assertEqual(encoding["labels"][0]["boxes"].shape     ,UpperCAmelCase_      )
           lowercase__		       =						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,UpperCAmelCase_     ,atol=1E-3      )      )
           # verify image_id
           lowercase__		       =						torch.tensor([39_769]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,UpperCAmelCase_      )      )
           # verify is_crowd
           lowercase__		       =						torch.tensor([0, 0, 0, 0, 0, 0]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,UpperCAmelCase_      )      )
           # verify class_labels
           lowercase__		       =						torch.tensor([17, 17, 63, 75, 75, 93]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,UpperCAmelCase_      )      )
           # verify masks
           lowercase__		       =						822_873
           self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,UpperCAmelCase_      )
           # verify orig_size
           lowercase__		       =						torch.tensor([480, 640]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,UpperCAmelCase_      )      )
           # verify size
           lowercase__		       =						torch.tensor([800, 1_066]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,UpperCAmelCase_      )      )









'''simple docstring'''





from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/blenderbot_small-90M": 512,
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Any						   =					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :str						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :List[Any]						   =					BlenderbotSmallTokenizer



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_=False     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							Optional[Any]:
           super().__init__(
               ByteLevelBPETokenizer(
                   vocab=UpperCAmelCase_     ,merges=UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_     ,trim_offsets=UpperCAmelCase_     ,)     ,bos_token=UpperCAmelCase_     ,eos_token=UpperCAmelCase_     ,unk_token=UpperCAmelCase_     ,**UpperCAmelCase_     ,)
           lowercase__		       =						add_prefix_space



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							Optional[int]:
           lowercase__		       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							List[int]:
           lowercase__		       =						[self.sep_token_id]
           lowercase__		       =						[self.cls_token_id]

           if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




