




"""simple docstring"""







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMWithLMHeadModel,
  )
  from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=1_3       ,					lowerCamelCase_=7       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_=False       ,					lowerCamelCase_=False       ,					lowerCamelCase_=False       ,					lowerCamelCase_=2       ,					lowerCamelCase_=9_9       ,					lowerCamelCase_=0       ,					lowerCamelCase_=3_2       ,					lowerCamelCase_=5       ,					lowerCamelCase_=4       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=5_1_2       ,					lowerCamelCase_=2       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=2       ,					lowerCamelCase_=4       ,					lowerCamelCase_="last"       ,					lowerCamelCase_=True       ,					lowerCamelCase_=None       ,					lowerCamelCase_=0       ,					)				->   Union[str, Any]:
       UpperCamelCase              =	parent
       UpperCamelCase              =	batch_size
       UpperCamelCase              =	seq_length
       UpperCamelCase              =	is_training
       UpperCamelCase              =	use_input_lengths
       UpperCamelCase              =	use_token_type_ids
       UpperCamelCase              =	use_labels
       UpperCamelCase              =	gelu_activation
       UpperCamelCase              =	sinusoidal_embeddings
       UpperCamelCase              =	causal
       UpperCamelCase              =	asm
       UpperCamelCase              =	n_langs
       UpperCamelCase              =	vocab_size
       UpperCamelCase              =	n_special
       UpperCamelCase              =	hidden_size
       UpperCamelCase              =	num_hidden_layers
       UpperCamelCase              =	num_attention_heads
       UpperCamelCase              =	hidden_dropout_prob
       UpperCamelCase              =	attention_probs_dropout_prob
       UpperCamelCase              =	max_position_embeddings
       UpperCamelCase              =	type_sequence_label_size
       UpperCamelCase              =	initializer_range
       UpperCamelCase              =	num_labels
       UpperCamelCase              =	num_choices
       UpperCamelCase              =	summary_type
       UpperCamelCase              =	use_proj
       UpperCamelCase              =	scope
       UpperCamelCase              =	bos_token_id







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size)
       UpperCamelCase              =	random_attention_mask([self.batch_size, self.seq_length])

       UpperCamelCase              =	None
       if self.use_input_lengths:
           UpperCamelCase              =	(
               ids_tensor([self.batch_size]       ,					vocab_size=2) + self.seq_length - 2
           )  # small variation of seq_length

       UpperCamelCase              =	None
       if self.use_token_type_ids:
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.n_langs)

       UpperCamelCase              =	None
       UpperCamelCase              =	None
       UpperCamelCase              =	None
       if self.use_labels:
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.type_sequence_label_size)
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					2).float()
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.num_choices)

       UpperCamelCase              =	self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       return XLMConfig(
           vocab_size=self.vocab_size       ,					n_special=self.n_special       ,					emb_dim=self.hidden_size       ,					n_layers=self.num_hidden_layers       ,					n_heads=self.num_attention_heads       ,					dropout=self.hidden_dropout_prob       ,					attention_dropout=self.attention_probs_dropout_prob       ,					gelu_activation=self.gelu_activation       ,					sinusoidal_embeddings=self.sinusoidal_embeddings       ,					asm=self.asm       ,					causal=self.causal       ,					n_langs=self.n_langs       ,					max_position_embeddings=self.max_position_embeddings       ,					initializer_range=self.initializer_range       ,					summary_type=self.summary_type       ,					use_proj=self.use_proj       ,					num_labels=self.num_labels       ,					bos_token_id=self.bos_token_id       ,					)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Dict:
       UpperCamelCase              =	XLMModel(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	model(lowerCamelCase_       ,					lengths=lowerCamelCase_       ,					langs=lowerCamelCase_)
       UpperCamelCase              =	model(lowerCamelCase_       ,					langs=lowerCamelCase_)
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Tuple:
       UpperCamelCase              =	XLMWithLMHeadModel(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       UpperCamelCase              =	model(lowerCamelCase_       ,					token_type_ids=lowerCamelCase_       ,					labels=lowerCamelCase_)
       self.parent.assertEqual(result.loss.shape       ,					())
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   int:
       UpperCamelCase              =	XLMForQuestionAnsweringSimple(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       UpperCamelCase              =	model(lowerCamelCase_)

       UpperCamelCase              =	model(lowerCamelCase_       ,					start_positions=lowerCamelCase_       ,					end_positions=lowerCamelCase_)
       UpperCamelCase              =	outputs
       self.parent.assertEqual(result.start_logits.shape       ,					(self.batch_size, self.seq_length))
       self.parent.assertEqual(result.end_logits.shape       ,					(self.batch_size, self.seq_length))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Any:
       UpperCamelCase              =	XLMForQuestionAnswering(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       UpperCamelCase              =	model(lowerCamelCase_)

       UpperCamelCase              =	model(
           lowerCamelCase_       ,					start_positions=lowerCamelCase_       ,					end_positions=lowerCamelCase_       ,					cls_index=lowerCamelCase_       ,					is_impossible=lowerCamelCase_       ,					p_mask=lowerCamelCase_       ,					)

       UpperCamelCase              =	model(
           lowerCamelCase_       ,					start_positions=lowerCamelCase_       ,					end_positions=lowerCamelCase_       ,					cls_index=lowerCamelCase_       ,					is_impossible=lowerCamelCase_       ,					)

       ((UpperCamelCase			)       ,				)              =	result_with_labels.to_tuple()

       UpperCamelCase              =	model(lowerCamelCase_       ,					start_positions=lowerCamelCase_       ,					end_positions=lowerCamelCase_)

       ((UpperCamelCase			)       ,				)              =	result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape       ,					())
       self.parent.assertEqual(result.start_top_log_probs.shape       ,					(self.batch_size, model.config.start_n_top))
       self.parent.assertEqual(result.start_top_index.shape       ,					(self.batch_size, model.config.start_n_top))
       self.parent.assertEqual(
           result.end_top_log_probs.shape       ,					(self.batch_size, model.config.start_n_top * model.config.end_n_top))
       self.parent.assertEqual(
           result.end_top_index.shape       ,					(self.batch_size, model.config.start_n_top * model.config.end_n_top))
       self.parent.assertEqual(result.cls_logits.shape       ,					(self.batch_size,))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   List[Any]:
       UpperCamelCase              =	XLMForSequenceClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       UpperCamelCase              =	model(lowerCamelCase_)
       UpperCamelCase              =	model(lowerCamelCase_       ,					labels=lowerCamelCase_)
       self.parent.assertEqual(result.loss.shape       ,					())
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Dict:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	XLMForTokenClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()

       UpperCamelCase              =	model(lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					labels=lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[int]:
       UpperCamelCase              =	self.num_choices
       UpperCamelCase              =	XLMForMultipleChoice(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase              =	input_ids.unsqueeze(1).expand(-1       ,					self.num_choices       ,					-1).contiguous()
       UpperCamelCase              =	token_type_ids.unsqueeze(1).expand(-1       ,					self.num_choices       ,					-1).contiguous()
       UpperCamelCase              =	input_mask.unsqueeze(1).expand(-1       ,					self.num_choices       ,					-1).contiguous()
       UpperCamelCase              =	model(
           lowerCamelCase_       ,					attention_mask=lowerCamelCase_       ,					token_type_ids=lowerCamelCase_       ,					labels=lowerCamelCase_       ,					)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices))







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,	(
           UpperCamelCase			
       )       ,				
       )              =	config_and_inputs
       UpperCamelCase              =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
       return config, inputs_dict

@require_torch
class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (
       (
           XLMModel,
           XLMWithLMHeadModel,
           XLMForQuestionAnswering,
           XLMForSequenceClassification,
           XLMForQuestionAnsweringSimple,
           XLMForTokenClassification,
           XLMForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   A_				      =       (
       (XLMWithLMHeadModel,) if is_torch_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   A_				      =       (
       {
           '''feature-extraction''': XLMModel,
           '''fill-mask''': XLMWithLMHeadModel,
           '''question-answering''': XLMForQuestionAnsweringSimple,
           '''text-classification''': XLMForSequenceClassification,
           '''text-generation''': XLMWithLMHeadModel,
           '''token-classification''': XLMForTokenClassification,
           '''zero-shot''': XLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[Any]:
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('''Fast''')
       ):
           # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
           # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
           # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
           return True

       return False







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_=False)				->   List[Any]:
       UpperCamelCase              =	super()._prepare_for_class(lowerCamelCase_       ,					lowerCamelCase_       ,					return_labels=lowerCamelCase_)

       if return_labels:
           if model_class.__name__ == "XLMForQuestionAnswering":
               UpperCamelCase              =	torch.zeros(
                   self.model_tester.batch_size       ,					dtype=torch.long       ,					device=lowerCamelCase_)
               UpperCamelCase              =	torch.zeros(
                   self.model_tester.batch_size       ,					dtype=torch.long       ,					device=lowerCamelCase_)

       return inputs_dict







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	XLMModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowerCamelCase_       ,					emb_dim=3_7)







   def   UpperCAmelCase__			(		self)				->   int:
       self.config_tester.run_common_tests()







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_=False       ,					lowerCamelCase_=1)				->   Optional[int]:
       self.assertIsInstance(lowerCamelCase_       ,					lowerCamelCase_)
       self.assertListEqual(
           [isinstance(lowerCamelCase_       ,					lowerCamelCase_) for iter_attentions in attentions]       ,					[True] * len(lowerCamelCase_))
       self.assertEqual(len(lowerCamelCase_)       ,					(max_length - min_length) * num_beam_groups)

       for idx, iter_attentions in enumerate(lowerCamelCase_):
           # adds PAD dummy token
           UpperCamelCase              =	min_length + idx + 1
           UpperCamelCase              =	min_length + idx + 1

           UpperCamelCase              =	(
               batch_size * num_beam_groups,
               config.num_attention_heads,
               tgt_len,
               src_len,
           )
           # check attn size
           self.assertListEqual(
               [layer_attention.shape for layer_attention in iter_attentions]       ,					[expected_shape] * len(lowerCamelCase_))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_=False       ,					lowerCamelCase_=1)				->   str:
       self.assertIsInstance(lowerCamelCase_       ,					lowerCamelCase_)
       self.assertListEqual(
           [isinstance(lowerCamelCase_       ,					lowerCamelCase_) for iter_hidden_states in hidden_states]       ,					[True] * len(lowerCamelCase_)       ,					)
       self.assertEqual(len(lowerCamelCase_)       ,					(max_length - min_length) * num_beam_groups)

       for idx, iter_hidden_states in enumerate(lowerCamelCase_):
           # adds PAD dummy token
           UpperCamelCase              =	min_length + idx + 1
           UpperCamelCase              =	(batch_size * num_beam_groups, seq_len, config.hidden_size)
           # check hidden size
           self.assertListEqual(
               [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]       ,					[expected_shape] * len(lowerCamelCase_)       ,					)
       pass







   @slow
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase              =	XLMModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)

@require_torch
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   @slow
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048''')
       model.to(lowerCamelCase_)
       UpperCamelCase              =	torch.tensor([[1_4, 4_4_7]]       ,					dtype=torch.long       ,					device=lowerCamelCase_)  # the president
       UpperCamelCase              =	[
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
       ]  # the president the president the president the president the president the president the president the president the president the president
       # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
       UpperCamelCase              =	model.generate(lowerCamelCase_       ,					do_sample=lowerCamelCase_)
       self.assertListEqual(output_ids[0].cpu().numpy().tolist()       ,					lowerCamelCase_)







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 (							__snake_case   ,      __snake_case="shi-labs/oneformer_demo" )				->						Any:
       with open(hf_hub_download(__snake_case   ,      __snake_case   ,      repo_type="""dataset""" )   ,      """r""" ) as f:
              _UpperCAmelCase							=    json.load(__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(__snake_case ) )
       _UpperCAmelCase							=    thing_ids
       _UpperCAmelCase							=    class_names
       return metadata
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]=7       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			Union[str, Any]=30       ,				lowerCamelCase		:			Optional[int]=400       ,				lowerCamelCase		:			Union[str, Any]=None       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Union[str, Any]=255       ,				lowerCamelCase		:			Tuple="shi-labs/oneformer_demo"       ,				lowerCamelCase		:			Tuple="ade20k_panoptic.json"       ,				lowerCamelCase		:			Optional[Any]=10       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    class_info_file
         _UpperCAmelCase							=    prepare_metadata(lowerCamelCase       ,				lowerCamelCase							)
         _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  lowerCamelCase       (      self		:			int							)						->	Union[str, Any]:


         """simple docstring"""
         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  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple=False							)						->	Any:


         """simple docstring"""
         if not batched:
                _UpperCAmelCase							=    image_inputs[0]
                if isinstance(lowerCamelCase       ,				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(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[0]							)[0]
                _UpperCAmelCase							=    max(lowerCamelCase       ,				key=lambda lowerCamelCase							: item[1]							)[1]

         return expected_height, expected_width





  def  lowerCamelCase       (      self		:			str							)						->	Optional[int]:


         """simple docstring"""
         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     ):

  '''simple docstring'''







  _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  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    OneFormerImageProcessorTester(self							)


  @property
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         return self.image_processing_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         # 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=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				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(lowerCamelCase							)

         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(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[Any]							)						->	List[Any]:


         """simple docstring"""
         # 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=lowerCamelCase       ,				numpify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				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(lowerCamelCase							)

         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(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         # 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=lowerCamelCase       ,				torchify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				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(lowerCamelCase							)

         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(lowerCamelCase       ,				batched=lowerCamelCase							)

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			Union[str, Any]=False       ,				lowerCamelCase		:			Union[str, Any]="np"							)						->	str:


         """simple docstring"""
         _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=lowerCamelCase							)
         if with_segmentation_maps:
                _UpperCAmelCase							=    num_labels
                if is_instance_map:
                       _UpperCAmelCase							=    list(range(lowerCamelCase							)							) * 2
                       _UpperCAmelCase							=    dict(enumerate(lowerCamelCase							)							)
                _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(lowerCamelCase							) for annotation in annotations]

         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         def common(lowerCamelCase		:			List[Any]=False       ,				lowerCamelCase		:			List[Any]=None							):
                _UpperCAmelCase							=    self.comm_get_image_processor_inputs(
                    with_segmentation_maps=lowerCamelCase       ,				is_instance_map=lowerCamelCase       ,				segmentation_type=lowerCamelCase							)

                _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(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  lowerCamelCase       (      self		:			int							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    np.zeros((20, 50)							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    1

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


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _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(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,
             )       ,				)

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

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


  def  lowerCamelCase       (      self		:			Dict							)						->	Any:


         """simple docstring"""
         _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(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  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _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(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)							)