
import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
          from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
          from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
     from PIL import Image





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase="shi-labs/oneformer_demo"				):
    with open(hf_hub_download(__lowerCAmelCase     ,		__lowerCAmelCase     ,		repo_type="dataset"				)     ,		"r"				) as f:
        snake_case__    =					json.load(__lowerCAmelCase				)
    snake_case__    =					{}
    snake_case__    =					[]
    snake_case__    =					[]
    for key, info in class_info.items():
        snake_case__    =					info["name"]
        class_names.append(info["name"]				)
        if info["isthing"]:
            thing_ids.append(int(__lowerCAmelCase				)				)
    snake_case__    =					thing_ids
    snake_case__    =					class_names
    return metadata




class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase=7		,						lowerCamelCase=3		,						lowerCamelCase=30		,						lowerCamelCase=4_00		,						lowerCamelCase=None		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=10		,						lowerCamelCase=False		,						lowerCamelCase=2_55		,						lowerCamelCase="shi-labs/oneformer_demo"		,						lowerCamelCase="ade20k_panoptic.json"		,						lowerCamelCase=10		,						):
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					num_channels
          snake_case__    =					min_resolution
          snake_case__    =					max_resolution
          snake_case__    =					do_resize
          snake_case__    =					{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
          snake_case__    =					do_normalize
          snake_case__    =					image_mean
          snake_case__    =					image_std
          snake_case__    =					class_info_file
          snake_case__    =					prepare_metadata(lowerCamelCase		,						lowerCamelCase			)
          snake_case__    =					num_text
          snake_case__    =					repo_path

          # for the post_process_functions
          snake_case__    =					2
          snake_case__    =					10
          snake_case__    =					10
          snake_case__    =					3
          snake_case__    =					4
          snake_case__    =					num_labels
          snake_case__    =					do_reduce_labels
          snake_case__    =					ignore_index
      def A_      (      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,
              "num_labels": self.num_labels,
              "do_reduce_labels": self.do_reduce_labels,
              "ignore_index": self.ignore_index,
              "class_info_file": self.class_info_file,
              "metadata": self.metadata,
              "num_text": self.num_text,
          }
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=False			):
          if not batched:
              snake_case__    =					image_inputs[0]
              if isinstance(lowerCamelCase		,						Image.Image			):
                  snake_case__    ,       snake_case__    =					image.size
              else:
                  snake_case__    ,       snake_case__    =					image.shape[1], image.shape[2]
              if w < h:
                  snake_case__    =					int(self.size["shortest_edge"] * h / w			)
                  snake_case__    =					self.size["shortest_edge"]
              elif w > h:
                  snake_case__    =					self.size["shortest_edge"]
                  snake_case__    =					int(self.size["shortest_edge"] * w / h			)
              else:
                  snake_case__    =					self.size["shortest_edge"]
                  snake_case__    =					self.size["shortest_edge"]

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

          return expected_height, expected_width



      def A_      (      self			):
          return OneFormerForUniversalSegmentationOutput(
              # +1 for null class
              class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)			)		,						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)			)		,						)





@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Tuple     						=  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      _A    :		Dict     						=  image_processing_class
      def A_      (      self			):
          snake_case__    =					OneFormerImageProcessorTester(self			)
      @property
      def A_      (      self			):
          return self.image_processing_tester.prepare_image_processor_dict()
      def A_      (      self			):
          snake_case__    =					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		,						"size"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"ignore_index"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"class_info_file"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"num_text"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"repo_path"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"metadata"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_reduce_labels"			)			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          # Initialize image_processor
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          snake_case__    =					prepare_image_inputs(self.image_processing_tester		,						equal_resolution=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						Image.Image			)

          # Test not batched input
          snake_case__    =					image_processor(image_inputs[0]		,						["semantic"]		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processing_tester.get_expected_values(lowerCamelCase			)

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

          # Test batched
          snake_case__    ,       snake_case__    =					self.image_processing_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          snake_case__    =					image_processor(
              lowerCamelCase		,						["semantic"] * len(lowerCamelCase			)		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,						)
      def A_      (      self			):
          # Initialize image_processor
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          snake_case__    =					prepare_image_inputs(self.image_processing_tester		,						equal_resolution=lowerCamelCase		,						numpify=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						np.ndarray			)

          # Test not batched input
          snake_case__    =					image_processor(image_inputs[0]		,						["semantic"]		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processing_tester.get_expected_values(lowerCamelCase			)

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

          # Test batched
          snake_case__    ,       snake_case__    =					self.image_processing_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          snake_case__    =					image_processor(
              lowerCamelCase		,						["semantic"] * len(lowerCamelCase			)		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,						)
      def A_      (      self			):
          # Initialize image_processor
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          snake_case__    =					prepare_image_inputs(self.image_processing_tester		,						equal_resolution=lowerCamelCase		,						torchify=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						torch.Tensor			)

          # Test not batched input
          snake_case__    =					image_processor(image_inputs[0]		,						["semantic"]		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processing_tester.get_expected_values(lowerCamelCase			)

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

          # Test batched
          snake_case__    ,       snake_case__    =					self.image_processing_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          snake_case__    =					image_processor(
              lowerCamelCase		,						["semantic"] * len(lowerCamelCase			)		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,						)
      def A_      (      self		,						lowerCamelCase=False		,						lowerCamelCase=False		,						lowerCamelCase="np"			):
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # prepare image and target
          snake_case__    =					self.image_processing_tester.num_labels
          snake_case__    =					None
          snake_case__    =					None
          snake_case__    =					prepare_image_inputs(self.image_processing_tester		,						equal_resolution=lowerCamelCase			)
          if with_segmentation_maps:
              snake_case__    =					num_labels
              if is_instance_map:
                  snake_case__    =					list(range(lowerCamelCase			)			) * 2
                  snake_case__    =					dict(enumerate(lowerCamelCase			)			)
              snake_case__    =					[
                  np.random.randint(0		,						high * 2		,						(img.size[1], img.size[0])			).astype(np.uinta			) for img in image_inputs
              ]
              if segmentation_type == "pil":
                  snake_case__    =					[Image.fromarray(lowerCamelCase			) for annotation in annotations]

          snake_case__    =					image_processor(
              lowerCamelCase		,						["semantic"] * len(lowerCamelCase			)		,						lowerCamelCase		,						return_tensors="pt"		,						instance_id_to_semantic_id=lowerCamelCase		,						pad_and_return_pixel_mask=lowerCamelCase		,						)

          return inputs
      def A_      (      self			):
          pass
      def A_      (      self			):
          def common(lowerCamelCase=False		,						lowerCamelCase=None			):
              snake_case__    =					self.comm_get_image_processor_inputs(
                  with_segmentation_maps=lowerCamelCase		,						is_instance_map=lowerCamelCase		,						segmentation_type=lowerCamelCase			)

              snake_case__    =					inputs["mask_labels"]
              snake_case__    =					inputs["class_labels"]
              snake_case__    =					inputs["pixel_values"]
              snake_case__    =					inputs["text_inputs"]

              # check the batch_size
              for mask_label, class_label, text_input in zip(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
                  self.assertEqual(mask_label.shape[0]		,						class_label.shape[0]			)
                  # this ensure padding has happened
                  self.assertEqual(mask_label.shape[1:]		,						pixel_values.shape[2:]			)
                  self.assertEqual(len(lowerCamelCase			)		,						self.image_processing_tester.num_text			)

          common()
          common(is_instance_map=lowerCamelCase			)
          common(is_instance_map=lowerCamelCase		,						segmentation_type="pil"			)
          common(is_instance_map=lowerCamelCase		,						segmentation_type="pil"			)
      def A_      (      self			):
          snake_case__    =					np.zeros((20, 50)			)
          snake_case__    =					1
          snake_case__    =					1
          snake_case__    =					1

          snake_case__    =					binary_mask_to_rle(lowerCamelCase			)
          self.assertEqual(len(lowerCamelCase			)		,						4			)
          self.assertEqual(rle[0]		,						21			)
          self.assertEqual(rle[1]		,						45			)
      def A_      (      self			):
          snake_case__    =					self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		,						max_seq_length=77		,						task_seq_length=77		,						class_info_file="ade20k_panoptic.json"		,						num_text=self.image_processing_tester.num_text		,						repo_path="shi-labs/oneformer_demo"		,						)
          snake_case__    =					self.image_processing_tester.get_fake_oneformer_outputs()

          snake_case__    =					fature_extractor.post_process_semantic_segmentation(lowerCamelCase			)

          self.assertEqual(len(lowerCamelCase			)		,						self.image_processing_tester.batch_size			)
          self.assertEqual(
              segmentation[0].shape		,						(
                  self.image_processing_tester.height,
                  self.image_processing_tester.width,
              )		,						)

          snake_case__    =					[(1, 4) for i in range(self.image_processing_tester.batch_size			)]
          snake_case__    =					fature_extractor.post_process_semantic_segmentation(lowerCamelCase		,						target_sizes=lowerCamelCase			)

          self.assertEqual(segmentation[0].shape		,						target_sizes[0]			)
      def A_      (      self			):
          snake_case__    =					self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		,						max_seq_length=77		,						task_seq_length=77		,						class_info_file="ade20k_panoptic.json"		,						num_text=self.image_processing_tester.num_text		,						repo_path="shi-labs/oneformer_demo"		,						)
          snake_case__    =					self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case__    =					image_processor.post_process_instance_segmentation(lowerCamelCase		,						threshold=0			)

          self.assertTrue(len(lowerCamelCase			) == self.image_processing_tester.batch_size			)
          for el in segmentation:
              self.assertTrue("segmentation" in el			)
              self.assertTrue("segments_info" in el			)
              self.assertEqual(type(el["segments_info"]			)		,						lowerCamelCase			)
              self.assertEqual(
                  el["segmentation"].shape		,						(self.image_processing_tester.height, self.image_processing_tester.width)			)



      def A_      (      self			):
          snake_case__    =					self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes		,						max_seq_length=77		,						task_seq_length=77		,						class_info_file="ade20k_panoptic.json"		,						num_text=self.image_processing_tester.num_text		,						repo_path="shi-labs/oneformer_demo"		,						)
          snake_case__    =					self.image_processing_tester.get_fake_oneformer_outputs()
          snake_case__    =					image_processor.post_process_panoptic_segmentation(lowerCamelCase		,						threshold=0			)

          self.assertTrue(len(lowerCamelCase			) == self.image_processing_tester.batch_size			)
          for el in segmentation:
              self.assertTrue("segmentation" in el			)
              self.assertTrue("segments_info" in el			)
              self.assertEqual(type(el["segments_info"]			)		,						lowerCamelCase			)
              self.assertEqual(
                  el["segmentation"].shape		,						(self.image_processing_tester.height, self.image_processing_tester.width)			)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



