

'''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   a__			(    a__ ,       a__="shi-labs/oneformer_demo"		):




     """simple docstring"""


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







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = num_channels
      __SCREAMING_SNAKE_CASE      = min_resolution
      __SCREAMING_SNAKE_CASE      = max_resolution
      __SCREAMING_SNAKE_CASE      = do_resize
      __SCREAMING_SNAKE_CASE      = {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
      __SCREAMING_SNAKE_CASE      = do_normalize
      __SCREAMING_SNAKE_CASE      = image_mean
      __SCREAMING_SNAKE_CASE      = image_std
      __SCREAMING_SNAKE_CASE      = class_info_file
      __SCREAMING_SNAKE_CASE      = prepare_metadata(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = num_text
      __SCREAMING_SNAKE_CASE      = repo_path

      # for the post_process_functions
      __SCREAMING_SNAKE_CASE      = 2
      __SCREAMING_SNAKE_CASE      = 10
      __SCREAMING_SNAKE_CASE      = 10
      __SCREAMING_SNAKE_CASE      = 3
      __SCREAMING_SNAKE_CASE      = 4
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = do_reduce_labels
      __SCREAMING_SNAKE_CASE      = ignore_index
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """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  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=False     )    ->					List[Any]:


      """simple docstring"""




      if not batched:
           __SCREAMING_SNAKE_CASE      = image_inputs[0]
           if isinstance(__SCREAMING_SNAKE_CASE   ,				Image.Image     ):
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = image.size
           else:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = image.shape[1], image.shape[2]
           if w < h:
                __SCREAMING_SNAKE_CASE      = int(self.size["""shortest_edge"""] * h / w     )
                __SCREAMING_SNAKE_CASE      = self.size["""shortest_edge"""]
           elif w > h:
                __SCREAMING_SNAKE_CASE      = self.size["""shortest_edge"""]
                __SCREAMING_SNAKE_CASE      = int(self.size["""shortest_edge"""] * w / h     )
           else:
                __SCREAMING_SNAKE_CASE      = self.size["""shortest_edge"""]
                __SCREAMING_SNAKE_CASE      = self.size["""shortest_edge"""]

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

      return expected_height, expected_width


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					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 		lowerCAmelCase__  ( a					,   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  UpperCAmelCase__						(						self   :			Dict     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = OneFormerImageProcessorTester(self     )
 @property
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					int:


      """simple docstring"""




      return self.image_processing_tester.prepare_image_processor_dict()
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.image_processing_class(**self.image_processor_dict     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""image_mean"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""image_std"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""do_normalize"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""do_resize"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""size"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""ignore_index"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""class_info_file"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""num_text"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""repo_path"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""metadata"""     )     )
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""do_reduce_labels"""     )     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      pass
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.image_processing_class(**self.image_processor_dict     )
      # create random PIL images
      __SCREAMING_SNAKE_CASE      = prepare_image_inputs(self.image_processing_tester   ,				equal_resolution=__SCREAMING_SNAKE_CASE     )
      for image in image_inputs:
           self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,				Image.Image     )

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

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE     )

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

      # Test batched
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE   ,				batched=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = image_processor(
          __SCREAMING_SNAKE_CASE   ,				["""semantic"""] * len(__SCREAMING_SNAKE_CASE     )   ,				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  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.image_processing_class(**self.image_processor_dict     )
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE      = prepare_image_inputs(self.image_processing_tester   ,				equal_resolution=__SCREAMING_SNAKE_CASE   ,				numpify=__SCREAMING_SNAKE_CASE     )
      for image in image_inputs:
           self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,				np.ndarray     )

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

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE     )

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

      # Test batched
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE   ,				batched=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = image_processor(
          __SCREAMING_SNAKE_CASE   ,				["""semantic"""] * len(__SCREAMING_SNAKE_CASE     )   ,				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  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.image_processing_class(**self.image_processor_dict     )
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE      = prepare_image_inputs(self.image_processing_tester   ,				equal_resolution=__SCREAMING_SNAKE_CASE   ,				torchify=__SCREAMING_SNAKE_CASE     )
      for image in image_inputs:
           self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,				torch.Tensor     )

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

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE     )

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

      # Test batched
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE   ,				batched=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = image_processor(
          __SCREAMING_SNAKE_CASE   ,				["""semantic"""] * len(__SCREAMING_SNAKE_CASE     )   ,				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  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=False   ,				__SCREAMING_SNAKE_CASE   :			int=False   ,				__SCREAMING_SNAKE_CASE   :			str="np"     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.image_processing_class(**self.image_processor_dict     )
      # prepare image and target
      __SCREAMING_SNAKE_CASE      = self.image_processing_tester.num_labels
      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = prepare_image_inputs(self.image_processing_tester   ,				equal_resolution=__SCREAMING_SNAKE_CASE     )
      if with_segmentation_maps:
           __SCREAMING_SNAKE_CASE      = num_labels
           if is_instance_map:
                __SCREAMING_SNAKE_CASE      = list(range(__SCREAMING_SNAKE_CASE     )     ) * 2
                __SCREAMING_SNAKE_CASE      = dict(enumerate(__SCREAMING_SNAKE_CASE     )     )
           __SCREAMING_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":
                __SCREAMING_SNAKE_CASE      = [Image.fromarray(__SCREAMING_SNAKE_CASE     ) for annotation in annotations]

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

      return inputs
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					str:


      """simple docstring"""




      pass
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      def common(__SCREAMING_SNAKE_CASE   :			List[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			str=None     ):
           __SCREAMING_SNAKE_CASE      = self.comm_get_image_processor_inputs(
               with_segmentation_maps=__SCREAMING_SNAKE_CASE   ,				is_instance_map=__SCREAMING_SNAKE_CASE   ,				segmentation_type=__SCREAMING_SNAKE_CASE     )

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

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

      common()
      common(is_instance_map=__SCREAMING_SNAKE_CASE     )
      common(is_instance_map=__SCREAMING_SNAKE_CASE   ,				segmentation_type="""pil"""     )
      common(is_instance_map=__SCREAMING_SNAKE_CASE   ,				segmentation_type="""pil"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = np.zeros((20, 50)     )
      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 1

      __SCREAMING_SNAKE_CASE      = binary_mask_to_rle(__SCREAMING_SNAKE_CASE     )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				4     )
      self.assertEqual(rle[0]   ,				21     )
      self.assertEqual(rle[1]   ,				45     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_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"""   ,				)
      __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_fake_oneformer_outputs()

      __SCREAMING_SNAKE_CASE      = fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE     )

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

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

      self.assertEqual(segmentation[0].shape   ,				target_sizes[0]     )
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_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"""   ,				)
      __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_fake_oneformer_outputs()
      __SCREAMING_SNAKE_CASE      = image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE   ,				threshold=0     )

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


 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_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"""   ,				)
      __SCREAMING_SNAKE_CASE      = self.image_processing_tester.get_fake_oneformer_outputs()
      __SCREAMING_SNAKE_CASE      = image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE   ,				threshold=0     )

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









'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
  raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
  raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
UpperCAmelCase					:			List[str]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Tuple       =	'Hello, World!'
UpperCAmelCase					:			Any       =	'en_XX'







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path("""data_bin"""		)
     __SCREAMING_SNAKE_CASE      = FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(a__		).parent		) ,       checkpoint_file=Path(a__		).name ,       _name="""xmod_base""" ,       arch="""xmod_base""" ,       task="""multilingual_masked_lm""" ,       data_name_or_path=str(a__		) ,       bpe="""sentencepiece""" ,       sentencepiece_model=str(Path(a__		).parent / """sentencepiece.bpe.model"""		) ,       src_dict=str(data_dir / """dict.txt"""		) ,       )
     xmod.eval()  # disable dropout
     print(a__		)

     __SCREAMING_SNAKE_CASE      = xmod.model.encoder.sentence_encoder
     __SCREAMING_SNAKE_CASE      = XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,       hidden_size=xmod.cfg.model.encoder_embed_dim ,       num_hidden_layers=xmod.cfg.model.encoder_layers ,       num_attention_heads=xmod.cfg.model.encoder_attention_heads ,       intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,       max_position_embeddings=5_14 ,       type_vocab_size=1 ,       layer_norm_eps=1E-5 ,       pre_norm=xmod.cfg.model.encoder_normalize_before ,       adapter_reduction_factor=getattr(xmod.cfg.model ,       """bottleneck""" ,       2		) ,       adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,       adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,       ln_before_adapter=xmod.cfg.model.ln_before_adapter ,       languages=xmod.cfg.model.languages ,       )
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:""" ,       a__		)

     __SCREAMING_SNAKE_CASE      = XmodForSequenceClassification(a__		) if classification_head else XmodForMaskedLM(a__		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_tokens.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_positions.weight
     __SCREAMING_SNAKE_CASE      = torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c xmod doesn't use them.

     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers		):
          # Encoder: start of layer
          __SCREAMING_SNAKE_CASE      = model.roberta.encoder.layer[i]
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layers[i]

          # self attention
          __SCREAMING_SNAKE_CASE      = layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""		)

          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.bias

          # self-attention output
          __SCREAMING_SNAKE_CASE      = layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.bias

          # intermediate
          __SCREAMING_SNAKE_CASE      = layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias

          # output
          __SCREAMING_SNAKE_CASE      = layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.weight
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("""Lists of language adapters do not match."""		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               __SCREAMING_SNAKE_CASE      = bert_output.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.bias

     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __SCREAMING_SNAKE_CASE      = xmod.encode(a__		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(a__		)

     __SCREAMING_SNAKE_CASE      = model(a__		)[0]
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__		)		)
     else:
          __SCREAMING_SNAKE_CASE      = xmod.model(a__ ,       lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape ,       their_output.shape		)
     __SCREAMING_SNAKE_CASE      = torch.max(torch.abs(our_output - their_output		)		).item()
     print(F'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
     __SCREAMING_SNAKE_CASE      = torch.allclose(a__ ,       a__ ,       atol=1E-3		)
     print("""Do both models output the same tensors?""" ,       """🔥""" if success else """💩"""		)
     if not success:
          raise Exception("""Something went wRoNg"""		)

     Path(a__		).mkdir(parents=a__ ,       exist_ok=a__		)
     print(F'Saving model to {pytorch_dump_folder_path}'		)
     model.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			Tuple       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--classification_head', action='store_true', help='Whether to convert a final classification head.'
  )
  UpperCAmelCase					:			Optional[int]       =	parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






