





"""simple docstring"""






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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"							)		->       List[Any]:






       """simple docstring"""




       with open(hf_hub_download(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,repo_type='dataset'							),'r'							) as f:
              _UpperCAmelCase     =     json.load(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     {}
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     []
       for key, info in class_info.items():
              _UpperCAmelCase     =     info['name']
              class_names.append(info['name']							)
              if info["isthing"]:
                     thing_ids.append(int(SCREAMING_SNAKE_CASE							)							)
       _UpperCAmelCase     =     thing_ids
       _UpperCAmelCase     =     class_names
       return metadata



class 	lowerCAmelCase					(      unittest.TestCase							):






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

              # for the post_process_functions
              _UpperCAmelCase     =     2
              _UpperCAmelCase     =     10
              _UpperCAmelCase     =     10
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     4
              _UpperCAmelCase     =     num_labels
              _UpperCAmelCase     =     do_reduce_labels
              _UpperCAmelCase     =     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    ,  a__    ,  a__=False     ):
              if not batched:
                     _UpperCAmelCase     =     image_inputs[0]
                     if isinstance(a__    ,  Image.Image     ):
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     image.size
                     else:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     image.shape[1], image.shape[2]
                     if w < h:
                            _UpperCAmelCase     =     int(self.size['shortest_edge'] * h / w     )
                            _UpperCAmelCase     =     self.size['shortest_edge']
                     elif w > h:
                            _UpperCAmelCase     =     self.size['shortest_edge']
                            _UpperCAmelCase     =     int(self.size['shortest_edge'] * w / h     )
                     else:
                            _UpperCAmelCase     =     self.size['shortest_edge']
                            _UpperCAmelCase     =     self.size['shortest_edge']

              else:
                     _UpperCAmelCase     =     []
                     for image in image_inputs:
                            _UpperCAmelCase    ,							_UpperCAmelCase     =     self.get_expected_values([image]     )
                            expected_values.append((expected_height, expected_width)     )
                     _UpperCAmelCase     =     max(a__    ,  key=lambda a__     : item[0]     )[0]
                     _UpperCAmelCase     =     max(a__    ,  key=lambda a__     : 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 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
       # only for test_image_processing_common.test_image_proc_to_json_string
       lowerCAmelCase__						 =  image_processing_class






       def 							__A (   self     ):
              _UpperCAmelCase     =     OneFormerImageProcessorTester(self     )






       @property
       def 							__A (   self     ):
              return self.image_processing_tester.prepare_image_processor_dict()






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              self.assertTrue(hasattr(a__    ,  'image_mean'     )     )
              self.assertTrue(hasattr(a__    ,  'image_std'     )     )
              self.assertTrue(hasattr(a__    ,  'do_normalize'     )     )
              self.assertTrue(hasattr(a__    ,  'do_resize'     )     )
              self.assertTrue(hasattr(a__    ,  'size'     )     )
              self.assertTrue(hasattr(a__    ,  'ignore_index'     )     )
              self.assertTrue(hasattr(a__    ,  'class_info_file'     )     )
              self.assertTrue(hasattr(a__    ,  'num_text'     )     )
              self.assertTrue(hasattr(a__    ,  'repo_path'     )     )
              self.assertTrue(hasattr(a__    ,  'metadata'     )     )
              self.assertTrue(hasattr(a__    ,  'do_reduce_labels'     )     )






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              # Initialize image_processor
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PIL images
              _UpperCAmelCase     =     prepare_image_inputs(self.image_processing_tester    ,  equal_resolution=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  Image.Image     )

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

              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.image_processing_tester.get_expected_values(a__     )

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

              # Test batched
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.image_processing_tester.get_expected_values(a__    ,  batched=a__     )

              _UpperCAmelCase     =     image_processor(
                  a__    ,  ['semantic'] * len(a__     )    ,  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
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random numpy tensors
              _UpperCAmelCase     =     prepare_image_inputs(self.image_processing_tester    ,  equal_resolution=a__    ,  numpify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  np.ndarray     )

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

              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.image_processing_tester.get_expected_values(a__     )

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

              # Test batched
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.image_processing_tester.get_expected_values(a__    ,  batched=a__     )

              _UpperCAmelCase     =     image_processor(
                  a__    ,  ['semantic'] * len(a__     )    ,  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
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # create random PyTorch tensors
              _UpperCAmelCase     =     prepare_image_inputs(self.image_processing_tester    ,  equal_resolution=a__    ,  torchify=a__     )
              for image in image_inputs:
                     self.assertIsInstance(a__    ,  torch.Tensor     )

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

              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.image_processing_tester.get_expected_values(a__     )

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

              # Test batched
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.image_processing_tester.get_expected_values(a__    ,  batched=a__     )

              _UpperCAmelCase     =     image_processor(
                  a__    ,  ['semantic'] * len(a__     )    ,  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    ,  a__=False    ,  a__=False    ,  a__="np"     ):
              _UpperCAmelCase     =     self.image_processing_class(**self.image_processor_dict     )
              # prepare image and target
              _UpperCAmelCase     =     self.image_processing_tester.num_labels
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     prepare_image_inputs(self.image_processing_tester    ,  equal_resolution=a__     )
              if with_segmentation_maps:
                     _UpperCAmelCase     =     num_labels
                     if is_instance_map:
                            _UpperCAmelCase     =     list(range(a__     )     ) * 2
                            _UpperCAmelCase     =     dict(enumerate(a__     )     )
                     _UpperCAmelCase     =     [
                         np.random.randint(0    ,  high * 2    ,  (img.size[1], img.size[0])     ).astype(np.uinta     ) for img in image_inputs
                     ]
                     if segmentation_type == "pil":
                            _UpperCAmelCase     =     [Image.fromarray(a__     ) for annotation in annotations]

              _UpperCAmelCase     =     image_processor(
                  a__    ,  ['semantic'] * len(a__     )    ,  a__    ,  return_tensors='pt'    ,  instance_id_to_semantic_id=a__    ,  pad_and_return_pixel_mask=a__    ,  )

              return inputs






       def 							__A (   self     ):
              pass






       def 							__A (   self     ):
              def common(a__=False    ,  a__=None     ):
                     _UpperCAmelCase     =     self.comm_get_image_processor_inputs(
                         with_segmentation_maps=a__    ,  is_instance_map=a__    ,  segmentation_type=a__     )

                     _UpperCAmelCase     =     inputs['mask_labels']
                     _UpperCAmelCase     =     inputs['class_labels']
                     _UpperCAmelCase     =     inputs['pixel_values']
                     _UpperCAmelCase     =     inputs['text_inputs']

                     # check the batch_size
                     for mask_label, class_label, text_input in zip(a__    ,  a__    ,  a__     ):
                            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(a__     )    ,  self.image_processing_tester.num_text     )

              common()
              common(is_instance_map=a__     )
              common(is_instance_map=a__    ,  segmentation_type='pil'     )
              common(is_instance_map=a__    ,  segmentation_type='pil'     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     np.zeros((20, 50)     )
              _UpperCAmelCase     =     1
              _UpperCAmelCase     =     1
              _UpperCAmelCase     =     1

              _UpperCAmelCase     =     binary_mask_to_rle(a__     )
              self.assertEqual(len(a__     )    ,  4     )
              self.assertEqual(rle[0]    ,  21     )
              self.assertEqual(rle[1]    ,  45     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     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'    ,  )
              _UpperCAmelCase     =     self.image_processing_tester.get_fake_oneformer_outputs()

              _UpperCAmelCase     =     fature_extractor.post_process_semantic_segmentation(a__     )

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

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

              self.assertEqual(segmentation[0].shape    ,  target_sizes[0]     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     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'    ,  )
              _UpperCAmelCase     =     self.image_processing_tester.get_fake_oneformer_outputs()
              _UpperCAmelCase     =     image_processor.post_process_instance_segmentation(a__    ,  threshold=0     )

              self.assertTrue(len(a__     ) == 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']     )    ,  a__     )
                     self.assertEqual(
                         el['segmentation'].shape    ,  (self.image_processing_tester.height, self.image_processing_tester.width)     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     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'    ,  )
              _UpperCAmelCase     =     self.image_processing_tester.get_fake_oneformer_outputs()
              _UpperCAmelCase     =     image_processor.post_process_panoptic_segmentation(a__    ,  threshold=0     )

              self.assertTrue(len(a__     ) == 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']     )    ,  a__     )
                     self.assertEqual(
                         el['segmentation'].shape    ,  (self.image_processing_tester.height, self.image_processing_tester.width)     )













"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     make_linear_from_emb(model.model.shared							)

       return model


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    lowerCAmelCase_			=							parser.parse_args()
    lowerCAmelCase_			=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






