





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 DetaImageProcessor





class      lowercase__    (							unittest.TestCase  ):







  def __init__(	self    ,						_lowercase    ,						_lowercase=7    ,						_lowercase=3    ,						_lowercase=30    ,						_lowercase=400    ,						_lowercase=True    ,						_lowercase=None    ,						_lowercase=True    ,						_lowercase=[0.5, 0.5, 0.5]    ,						_lowercase=[0.5, 0.5, 0.5]    ,						_lowercase=True    ,						_lowercase=1 / 255    ,						_lowercase=True    ,						):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowerCAmelCase_					:       Optional[Any]    =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
       lowerCAmelCase_					:       Dict    =		parent
       lowerCAmelCase_					:       Optional[Any]    =		batch_size
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       Union[str, Any]    =		min_resolution
       lowerCAmelCase_					:       List[str]    =		max_resolution
       lowerCAmelCase_					:       Optional[Any]    =		do_resize
       lowerCAmelCase_					:       Optional[Any]    =		size
       lowerCAmelCase_					:       Optional[int]    =		do_normalize
       lowerCAmelCase_					:       str    =		image_mean
       lowerCAmelCase_					:       Optional[int]    =		image_std
       lowerCAmelCase_					:       Optional[int]    =		do_rescale
       lowerCAmelCase_					:       Optional[int]    =		rescale_factor
       lowerCAmelCase_					:       List[str]    =		do_pad







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

       else:
            lowerCAmelCase_					:       Tuple    =		[]
            for image in image_inputs:
                 lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		self.get_expected_values([image]						)
                 expected_values.append((expected_height, expected_width)						)
            lowerCAmelCase_					:       int    =		max(_lowercase    ,						key=lambda _lowercase						: item[0]						)[0]
            lowerCAmelCase_					:       List[Any]    =		max(_lowercase    ,						key=lambda _lowercase						: item[1]						)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class      lowercase__    (							__A     ,					unittest.TestCase  ):
  __UpperCamelCase	     =  DetaImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		DetaImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[int]    =		self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowercase    ,						"""image_mean"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""image_std"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_normalize"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_resize"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_rescale"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""do_pad"""						)						)
       self.assertTrue(hasattr(_lowercase    ,						"""size"""						)						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		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    ,						_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       pass







  def 	UpperCAmelCase__		(	self						):
       # Initialize image_processing
       lowerCAmelCase_					:       Dict    =		self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowerCAmelCase_					:       Dict    =		prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=_lowercase						)
       for image in image_inputs:
            self.assertIsInstance(_lowercase    ,						Image.Image						)

       # Test not batched input
       lowerCAmelCase_					:       Optional[Any]    =		image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_   ,  lowerCAmelCase_					:       List[Any]    =		self.image_processor_tester.get_expected_values(_lowercase						)

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

       # Test batched
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Any    =		self.image_processor_tester.get_expected_values(_lowercase    ,						batched=_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		image_processing(_lowercase    ,						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						):
       # Initialize image_processing
       lowerCAmelCase_					:       Any    =		self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowerCAmelCase_					:       Dict    =		prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=_lowercase    ,						numpify=_lowercase						)
       for image in image_inputs:
            self.assertIsInstance(_lowercase    ,						np.ndarray						)

       # Test not batched input
       lowerCAmelCase_					:       List[Any]    =		image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		self.image_processor_tester.get_expected_values(_lowercase						)

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

       # Test batched
       lowerCAmelCase_					:       List[Any]    =		image_processing(_lowercase    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		self.image_processor_tester.get_expected_values(_lowercase    ,						batched=_lowercase						)

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







  def 	UpperCAmelCase__		(	self						):
       # Initialize image_processing
       lowerCAmelCase_					:       int    =		self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowerCAmelCase_					:       Optional[int]    =		prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=_lowercase    ,						torchify=_lowercase						)
       for image in image_inputs:
            self.assertIsInstance(_lowercase    ,						torch.Tensor						)

       # Test not batched input
       lowerCAmelCase_					:       Union[str, Any]    =		image_processing(image_inputs[0]    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_   ,  lowerCAmelCase_					:       Union[str, Any]    =		self.image_processor_tester.get_expected_values(_lowercase						)

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

       # Test batched
       lowerCAmelCase_					:       Optional[Any]    =		image_processing(_lowercase    ,						return_tensors="""pt"""						).pixel_values

       lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[int]    =		self.image_processor_tester.get_expected_values(_lowercase    ,						batched=_lowercase						)

       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						):
       # prepare image and target
       lowerCAmelCase_					:       str    =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,						"""r"""						) as f:
            lowerCAmelCase_					:       str    =		json.loads(f.read()						)

       lowerCAmelCase_					:       Tuple    =		{"""image_id""": 39_769, """annotations""": target}

       # encode them
       lowerCAmelCase_					:       Optional[Any]    =		DetaImageProcessor()
       lowerCAmelCase_					:       List[Any]    =		image_processing(images=_lowercase    ,						annotations=_lowercase    ,						return_tensors="""pt"""						)

       # verify pixel values
       lowerCAmelCase_					:       Optional[Any]    =		torch.Size([1, 3, 800, 1_066]						)
       self.assertEqual(encoding["""pixel_values"""].shape    ,						_lowercase						)

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

       # verify area
       lowerCAmelCase_					:       Any    =		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"""]    ,						_lowercase						)						)
       # verify boxes
       lowerCAmelCase_					:       Dict    =		torch.Size([6, 4]						)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,						_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,						_lowercase    ,						atol=1e-3						)						)
       # verify image_id
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([39_769]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,						_lowercase						)						)
       # verify is_crowd
       lowerCAmelCase_					:       Optional[Any]    =		torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,						_lowercase						)						)
       # verify class_labels
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([75, 75, 63, 65, 17, 17]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,						_lowercase						)						)
       # verify orig_size
       lowerCAmelCase_					:       Tuple    =		torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,						_lowercase						)						)
       # verify size
       lowerCAmelCase_					:       int    =		torch.tensor([800, 1_066]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,						_lowercase						)						)







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

       lowerCAmelCase_					:       Any    =		{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

       lowerCAmelCase_					:       Optional[Any]    =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

       # encode them
       lowerCAmelCase_					:       Optional[Any]    =		DetaImageProcessor(format="""coco_panoptic"""						)
       lowerCAmelCase_					:       List[str]    =		image_processing(images=_lowercase    ,						annotations=_lowercase    ,						masks_path=_lowercase    ,						return_tensors="""pt"""						)

       # verify pixel values
       lowerCAmelCase_					:       Union[str, Any]    =		torch.Size([1, 3, 800, 1_066]						)
       self.assertEqual(encoding["""pixel_values"""].shape    ,						_lowercase						)

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

       # verify area
       lowerCAmelCase_					:       List[str]    =		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"""]    ,						_lowercase						)						)
       # verify boxes
       lowerCAmelCase_					:       List[Any]    =		torch.Size([6, 4]						)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,						_lowercase						)
       lowerCAmelCase_					:       Tuple    =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,						_lowercase    ,						atol=1e-3						)						)
       # verify image_id
       lowerCAmelCase_					:       List[str]    =		torch.tensor([39_769]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,						_lowercase						)						)
       # verify is_crowd
       lowerCAmelCase_					:       Union[str, Any]    =		torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,						_lowercase						)						)
       # verify class_labels
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor([17, 17, 63, 75, 75, 93]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,						_lowercase						)						)
       # verify masks
       lowerCAmelCase_					:       Dict    =		822_873
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,						_lowercase						)
       # verify orig_size
       lowerCAmelCase_					:       List[Any]    =		torch.tensor([480, 640]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,						_lowercase						)						)
       # verify size
       lowerCAmelCase_					:       List[Any]    =		torch.tensor([800, 1_066]						)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,						_lowercase						)						)







from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


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

UpperCAmelCase_					:   Any 							=						{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """perceiver"""







  def __init__(	self    ,						_lowercase=256    ,						_lowercase=1_280    ,						_lowercase=768    ,						_lowercase=1    ,						_lowercase=26    ,						_lowercase=8    ,						_lowercase=8    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase="kv"    ,						_lowercase=1    ,						_lowercase=1    ,						_lowercase="gelu"    ,						_lowercase=0.1    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=True    ,						_lowercase=262    ,						_lowercase=2_048    ,						_lowercase=56    ,						_lowercase=[368, 496]    ,						_lowercase=16    ,						_lowercase=1_920    ,						_lowercase=16    ,						_lowercase=[1, 16, 224, 224]    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)

       lowerCAmelCase_					:       Optional[int]    =		num_latents
       lowerCAmelCase_					:       List[str]    =		d_latents
       lowerCAmelCase_					:       int    =		d_model
       lowerCAmelCase_					:       Dict    =		num_blocks
       lowerCAmelCase_					:       Union[str, Any]    =		num_self_attends_per_block
       lowerCAmelCase_					:       List[str]    =		num_self_attention_heads
       lowerCAmelCase_					:       List[str]    =		num_cross_attention_heads
       lowerCAmelCase_					:       List[Any]    =		qk_channels
       lowerCAmelCase_					:       Optional[Any]    =		v_channels
       lowerCAmelCase_					:       Optional[Any]    =		cross_attention_shape_for_attention
       lowerCAmelCase_					:       Optional[int]    =		self_attention_widening_factor
       lowerCAmelCase_					:       int    =		cross_attention_widening_factor
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       int    =		attention_probs_dropout_prob
       lowerCAmelCase_					:       int    =		initializer_range
       lowerCAmelCase_					:       Optional[Any]    =		layer_norm_eps
       lowerCAmelCase_					:       Optional[Any]    =		use_query_residual
       # masked language modeling attributes
       lowerCAmelCase_					:       Tuple    =		vocab_size
       lowerCAmelCase_					:       List[Any]    =		max_position_embeddings
       # image classification attributes
       lowerCAmelCase_					:       List[Any]    =		image_size
       # flow attributes
       lowerCAmelCase_					:       Dict    =		train_size
       # multimodal autoencoding attributes
       lowerCAmelCase_					:       Optional[Any]    =		num_frames
       lowerCAmelCase_					:       int    =		audio_samples_per_frame
       lowerCAmelCase_					:       Any    =		samples_per_patch
       lowerCAmelCase_					:       Any    =		output_shape





class      lowercase__    (							__A  ):







  @property
  def 	UpperCAmelCase__		(	self						):
       if self.task == "multiple-choice":
            lowerCAmelCase_					:       Optional[Any]    =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_					:       Optional[int]    =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""inputs""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return 1e-4







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 3    ,						_lowercase = 40    ,						_lowercase = 40    ,						):
       # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

       if isinstance(_lowercase    ,						_lowercase						):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_					:       Union[str, Any]    =		compute_effective_axis_dimension(
                _lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch    ,						num_token_to_add=0						)
            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            lowerCAmelCase_					:       int    =		preprocessor.num_special_tokens_to_add(_lowercase						)
            lowerCAmelCase_					:       int    =		compute_effective_axis_dimension(
                _lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_sequence    ,						num_token_to_add=_lowercase						)
            # Generate dummy inputs according to compute batch and sequence
            lowerCAmelCase_					:       Optional[int]    =		[""" """.join(["""a"""]						) * seq_length] * batch_size
            lowerCAmelCase_					:       Optional[Any]    =		dict(preprocessor(_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Optional[Any]    =		inputs.pop("""input_ids"""						)
            return inputs
       elif isinstance(_lowercase    ,						_lowercase						) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_					:       Any    =		compute_effective_axis_dimension(_lowercase    ,						fixed_dimension=OnnxConfig.default_fixed_batch						)
            lowerCAmelCase_					:       str    =		self._generate_dummy_images(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       List[str]    =		dict(preprocessor(images=_lowercase    ,						return_tensors=_lowercase						)						)
            lowerCAmelCase_					:       Tuple    =		inputs.pop("""pixel_values"""						)
            return inputs
       else:
            raise ValueError(
                """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""						)
