






"""simple docstring"""


import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
		from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor




class 	__UpperCamelCase    :
					def __init__(       self       :  Optional[Any]    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  Any=2    ,   UpperCAmelCase       :  Dict=32    ,   UpperCAmelCase       :  Tuple=16    ,   UpperCAmelCase       :  Tuple=3    ,   UpperCAmelCase       :  Any=True    ,   UpperCAmelCase       :  str=True    ,   UpperCAmelCase       :  List[str]=32    ,   UpperCAmelCase       :  int=4    ,   UpperCAmelCase       :  str=[0, 1, 2, 3]    ,   UpperCAmelCase       :  str=4    ,   UpperCAmelCase       :  Tuple=37    ,   UpperCAmelCase       :  Optional[int]="gelu"    ,   UpperCAmelCase       :  List[str]=0.1    ,   UpperCAmelCase       :  Optional[Any]=0.1    ,   UpperCAmelCase       :  int=0.0_2    ,   UpperCAmelCase       :  List[Any]=3    ,   UpperCAmelCase       :  Tuple=False    ,   UpperCAmelCase       :  Tuple=None    ,   )  ->					int:
											lowerCAmelCase      :Any				  = parent
											lowerCAmelCase      :Optional[Any]				  = batch_size
											lowerCAmelCase      :Tuple				  = image_size
											lowerCAmelCase      :Optional[int]				  = patch_size
											lowerCAmelCase      :Union[str, Any]				  = num_channels
											lowerCAmelCase      :Union[str, Any]				  = is_training
											lowerCAmelCase      :Tuple				  = use_labels
											lowerCAmelCase      :Dict				  = hidden_size
											lowerCAmelCase      :List[Any]				  = num_hidden_layers
											lowerCAmelCase      :List[Any]				  = backbone_out_indices
											lowerCAmelCase      :Any				  = num_attention_heads
											lowerCAmelCase      :Dict				  = intermediate_size
											lowerCAmelCase      :Union[str, Any]				  = hidden_act
											lowerCAmelCase      :Dict				  = hidden_dropout_prob
											lowerCAmelCase      :Optional[Any]				  = attention_probs_dropout_prob
											lowerCAmelCase      :str				  = initializer_range
											lowerCAmelCase      :Dict				  = num_labels
											lowerCAmelCase      :int				  = scope
											lowerCAmelCase      :Dict				  = is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											lowerCAmelCase      :str				  = (image_size // patch_size) ** 2
											lowerCAmelCase      :List[str]				  = num_patches + 1
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					List[str]:
											lowerCAmelCase      :int				  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

											lowerCAmelCase      :Any				  = None
											if self.use_labels:
																	lowerCAmelCase      :Any				  = ids_tensor([self.batch_size, self.image_size, self.image_size]    ,   self.num_labels      )

											lowerCAmelCase      :int				  = self.get_config()

											return config, pixel_values, labels
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Dict:
											return DPTConfig(
											    image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   backbone_out_indices=self.backbone_out_indices    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   is_decoder=__snake_case    ,   initializer_range=self.initializer_range    ,   is_hybrid=self.is_hybrid    ,   )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  Union[str, Any]    ,   UpperCAmelCase       :  List[str]      )  ->					str:
											lowerCAmelCase      :Tuple				  = DPTModel(config=__snake_case      )
											model.to(__snake_case      )
											model.eval()
											lowerCAmelCase      :Dict				  = model(__snake_case      )
											self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)      )
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  List[str]    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  List[str]      )  ->					int:
											lowerCAmelCase      :Tuple				  = self.num_labels
											lowerCAmelCase      :Union[str, Any]				  = DPTForDepthEstimation(__snake_case      )
											model.to(__snake_case      )
											model.eval()
											lowerCAmelCase      :str				  = model(__snake_case      )
											self.parent.assertEqual(result.predicted_depth.shape    ,   (self.batch_size, self.image_size, self.image_size)      )
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  Union[str, Any]    ,   UpperCAmelCase       :  Tuple      )  ->					Optional[int]:
											lowerCAmelCase      :Tuple				  = self.num_labels
											lowerCAmelCase      :Optional[int]				  = DPTForSemanticSegmentation(__snake_case      )
											model.to(__snake_case      )
											model.eval()
											lowerCAmelCase      :Optional[Any]				  = model(__snake_case    ,   labels=__snake_case      )
											self.parent.assertEqual(
											    result.logits.shape    ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					str:
											lowerCAmelCase      :Optional[Any]				  = self.prepare_config_and_inputs()
											lowerCAmelCase  ,   lowerCAmelCase  ,   lowerCAmelCase      :List[str]				  = config_and_inputs
											lowerCAmelCase      :int				  = {'pixel_values': pixel_values}
											return config, inputs_dict




@require_torch
class 	__UpperCamelCase    (    snake_case__      ,    snake_case__      ,    unittest.TestCase	):
					lowercase_						:							Dict			=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					lowercase_						:							List[str]			=     (
					    {
					        """depth-estimation""": DPTForDepthEstimation,
					        """feature-extraction""": DPTModel,
					        """image-segmentation""": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					lowercase_						:							str			=     False
					lowercase_						:							Tuple			=     False
					lowercase_						:							Optional[Any]			=     False
					def 					UpperCAmelCase__   (       self       :  Tuple      )  ->					int:
											lowerCAmelCase      :Tuple				  = DPTModelTester(self      )
											lowerCAmelCase      :Union[str, Any]				  = ConfigTester(self    ,   config_class=__snake_case    ,   has_text_modality=__snake_case    ,   hidden_size=37      )
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					List[str]:
											self.config_tester.run_common_tests()
					@unittest.skip(reason='DPT does not use inputs_embeds'      )
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Optional[int]:
											pass
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					List[Any]:
											lowerCAmelCase  ,   lowerCAmelCase      :int				  = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowerCAmelCase      :Union[str, Any]				  = model_class(__snake_case      )
																	self.assertIsInstance(model.get_input_embeddings()    ,   (nn.Module)      )
																	lowerCAmelCase      :Optional[Any]				  = model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__snake_case    ,   nn.Linear      )      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Optional[int]:
											lowerCAmelCase  ,   lowerCAmelCase      :List[str]				  = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowerCAmelCase      :Dict				  = model_class(__snake_case      )
																	lowerCAmelCase      :str				  = inspect.signature(model.forward      )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowerCAmelCase      :Optional[int]				  = [*signature.parameters.keys()]

																	lowerCAmelCase      :Dict				  = ['pixel_values']
																	self.assertListEqual(arg_names[:1]    ,   __snake_case      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					str:
											lowerCAmelCase      :List[str]				  = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case      )
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Union[str, Any]:
											lowerCAmelCase      :int				  = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*__snake_case      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Dict:
											lowerCAmelCase      :Union[str, Any]				  = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					Optional[Any]:
											for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																							continue

																	lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = self.model_tester.prepare_config_and_inputs_for_common()
																	lowerCAmelCase      :str				  = True

																	if model_class in get_values(__snake_case      ):
																							continue

																	lowerCAmelCase      :Optional[Any]				  = model_class(__snake_case      )
																	model.to(__snake_case      )
																	model.train()
																	lowerCAmelCase      :Optional[int]				  = self._prepare_for_class(__snake_case    ,   __snake_case    ,   return_labels=__snake_case      )
																	lowerCAmelCase      :Union[str, Any]				  = model(**__snake_case      ).loss
																	loss.backward()
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Optional[int]:
											for model_class in self.all_model_classes:
																	if model_class.__name__ == "DPTForDepthEstimation":
																							continue

																	lowerCAmelCase  ,   lowerCAmelCase      :str				  = self.model_tester.prepare_config_and_inputs_for_common()
																	lowerCAmelCase      :Dict				  = False
																	lowerCAmelCase      :Tuple				  = True

																	if model_class in get_values(__snake_case      ) or not model_class.supports_gradient_checkpointing:
																							continue
																	lowerCAmelCase      :List[str]				  = model_class(__snake_case      )
																	model.to(__snake_case      )
																	model.gradient_checkpointing_enable()
																	model.train()
																	lowerCAmelCase      :List[str]				  = self._prepare_for_class(__snake_case    ,   __snake_case    ,   return_labels=__snake_case      )
																	lowerCAmelCase      :Optional[int]				  = model(**__snake_case      ).loss
																	loss.backward()
					def 					UpperCAmelCase__   (       self       :  int      )  ->					List[str]:
											lowerCAmelCase  ,   lowerCAmelCase      :int				  = self.model_tester.prepare_config_and_inputs_for_common()

											lowerCAmelCase      :int				  = _config_zero_init(__snake_case      )
											for model_class in self.all_model_classes:
																	lowerCAmelCase      :Union[str, Any]				  = model_class(config=__snake_case      )
																	# Skip the check for the backbone
																	lowerCAmelCase      :Union[str, Any]				  = []
																	for name, module in model.named_modules():
																							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																													lowerCAmelCase      :Tuple				  = [f"""{name}.{key}""" for key in module.state_dict().keys()]
																													break

																	for name, param in model.named_parameters():
																							if param.requires_grad:
																													if name in backbone_params:
																																			continue
																													self.assertIn(
																													    ((param.data.mean() * 1e9).round() / 1e9).item()    ,   [0.0, 1.0]    ,   msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""    ,   )
					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					Optional[Any]:
											pass
					@slow
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Any:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCAmelCase      :Optional[Any]				  = DPTModel.from_pretrained(__snake_case      )
																	self.assertIsNotNone(__snake_case      )


def 					UpperCAmelCase			(				):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
						return image







@require_torch
@require_vision
@slow
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Optional[int]:
											lowerCAmelCase      :Tuple				  = DPTImageProcessor.from_pretrained('Intel/dpt-large'      )
											lowerCAmelCase      :Tuple				  = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'      ).to(__snake_case      )

											lowerCAmelCase      :List[Any]				  = prepare_img()
											lowerCAmelCase      :Any				  = image_processor(images=__snake_case    ,   return_tensors='pt'      ).to(__snake_case      )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase      :Any				  = model(**__snake_case      )
																	lowerCAmelCase      :List[Any]				  = outputs.predicted_depth

											# verify the predicted depth
											lowerCAmelCase      :Dict				  = torch.Size((1, 384, 384)      )
											self.assertEqual(predicted_depth.shape    ,   __snake_case      )

											lowerCAmelCase      :List[str]				  = torch.tensor(
											    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]      ).to(__snake_case      )

											self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]    ,   __snake_case    ,   atol=1e-4      )      )
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Dict:
											lowerCAmelCase      :List[str]				  = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'      )
											lowerCAmelCase      :Optional[Any]				  = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'      ).to(__snake_case      )

											lowerCAmelCase      :List[str]				  = prepare_img()
											lowerCAmelCase      :Optional[int]				  = image_processor(images=__snake_case    ,   return_tensors='pt'      ).to(__snake_case      )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase      :List[str]				  = model(**__snake_case      )

											# verify the logits
											lowerCAmelCase      :int				  = torch.Size((1, 150, 480, 480)      )
											self.assertEqual(outputs.logits.shape    ,   __snake_case      )

											lowerCAmelCase      :List[str]				  = torch.tensor(
											    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]      ).to(__snake_case      )

											self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]    ,   __snake_case    ,   atol=1e-4      )      )
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					List[str]:
											lowerCAmelCase      :str				  = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'      )
											lowerCAmelCase      :str				  = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'      ).to(__snake_case      )

											lowerCAmelCase      :Dict				  = prepare_img()
											lowerCAmelCase      :Dict				  = image_processor(images=__snake_case    ,   return_tensors='pt'      ).to(__snake_case      )

											# forward pass
											with torch.no_grad():
																	lowerCAmelCase      :Any				  = model(**__snake_case      )

											lowerCAmelCase      :Dict				  = outputs.logits.detach().cpu()

											lowerCAmelCase      :str				  = image_processor.post_process_semantic_segmentation(outputs=__snake_case    ,   target_sizes=[(500, 300)]      )
											lowerCAmelCase      :List[str]				  = torch.Size((500, 300)      )
											self.assertEqual(segmentation[0].shape    ,   __snake_case      )

											lowerCAmelCase      :Optional[int]				  = image_processor.post_process_semantic_segmentation(outputs=__snake_case      )
											lowerCAmelCase      :Optional[Any]				  = torch.Size((480, 480)      )
											self.assertEqual(segmentation[0].shape    ,   __snake_case      )







"""simple docstring"""


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE      =      {'stas/tiny-wmt19-en-de': 10_24}
__SCREAMING_SNAKE_CASE      =      {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 10_24,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = set()
						lowerCAmelCase      :Optional[Any]				  = word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)   )
												lowerCAmelCase      :int				  = char
						return pairs


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = text.replace('，'    ,			','   )
						lowerCAmelCase      :str				  = re.sub(R'。\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :int				  = text.replace('、'    ,			','   )
						lowerCAmelCase      :str				  = text.replace('”'    ,			'"'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('“'    ,			'"'   )
						lowerCAmelCase      :List[Any]				  = text.replace('∶'    ,			':'   )
						lowerCAmelCase      :List[Any]				  = text.replace('：'    ,			':'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('？'    ,			'?'   )
						lowerCAmelCase      :Tuple				  = text.replace('《'    ,			'"'   )
						lowerCAmelCase      :Dict				  = text.replace('》'    ,			'"'   )
						lowerCAmelCase      :List[str]				  = text.replace('）'    ,			')'   )
						lowerCAmelCase      :Dict				  = text.replace('！'    ,			'!'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('（'    ,			'('   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('；'    ,			';'   )
						lowerCAmelCase      :Any				  = text.replace('１'    ,			'1'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('」'    ,			'"'   )
						lowerCAmelCase      :Any				  = text.replace('「'    ,			'"'   )
						lowerCAmelCase      :str				  = text.replace('０'    ,			'0'   )
						lowerCAmelCase      :Dict				  = text.replace('３'    ,			'3'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('２'    ,			'2'   )
						lowerCAmelCase      :str				  = text.replace('５'    ,			'5'   )
						lowerCAmelCase      :Tuple				  = text.replace('６'    ,			'6'   )
						lowerCAmelCase      :Optional[int]				  = text.replace('９'    ,			'9'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('７'    ,			'7'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('８'    ,			'8'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('４'    ,			'4'   )
						lowerCAmelCase      :int				  = re.sub(R'．\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :List[Any]				  = text.replace('～'    ,			'~'   )
						lowerCAmelCase      :int				  = text.replace('’'    ,			'\''   )
						lowerCAmelCase      :Dict				  = text.replace('…'    ,			'...'   )
						lowerCAmelCase      :Any				  = text.replace('━'    ,			'-'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('〈'    ,			'<'   )
						lowerCAmelCase      :Any				  = text.replace('〉'    ,			'>'   )
						lowerCAmelCase      :str				  = text.replace('【'    ,			'['   )
						lowerCAmelCase      :Any				  = text.replace('】'    ,			']'   )
						lowerCAmelCase      :int				  = text.replace('％'    ,			'%'   )
						return text


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = []
						for char in text:
												lowerCAmelCase      :Optional[Any]				  = unicodedata.category(a__   )
												if cat.startswith('C'   ):
																		continue
												output.append(a__   )
						return "".join(a__   )







class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Dict			=     VOCAB_FILES_NAMES
					lowercase_						:							Any			=     PRETRAINED_VOCAB_FILES_MAP
					lowercase_						:							Dict			=     PRETRAINED_INIT_CONFIGURATION
					lowercase_						:							Optional[Any]			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_						:							Union[str, Any]			=     ["""input_ids""", """attention_mask"""]
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  str=False    ,   UpperCAmelCase       :  Optional[int]="<unk>"    ,   UpperCAmelCase       :  List[Any]="<s>"    ,   UpperCAmelCase       :  List[str]="</s>"    ,   UpperCAmelCase       :  Optional[int]="<pad>"    ,   **UpperCAmelCase       :  int    ,   )  ->					int:
											super().__init__(
											    langs=UpperCAmelCase    ,   src_vocab_file=UpperCAmelCase    ,   tgt_vocab_file=UpperCAmelCase    ,   merges_file=UpperCAmelCase    ,   do_lower_case=UpperCAmelCase    ,   unk_token=UpperCAmelCase    ,   bos_token=UpperCAmelCase    ,   sep_token=UpperCAmelCase    ,   pad_token=UpperCAmelCase    ,   **UpperCAmelCase    ,   )

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses

											lowerCAmelCase      :str				  = src_vocab_file
											lowerCAmelCase      :List[str]				  = tgt_vocab_file
											lowerCAmelCase      :str				  = merges_file
											lowerCAmelCase      :Optional[Any]				  = do_lower_case

											# cache of sm.MosesPunctNormalizer instance
											lowerCAmelCase      :str				  = {}
											# cache of sm.MosesTokenizer instance
											lowerCAmelCase      :Any				  = {}
											lowerCAmelCase      :List[str]				  = {}

											if langs and len(UpperCAmelCase      ) == 2:
																	lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = langs
											else:
																	raise ValueError(
																	    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
																	    'Usually that means that tokenizer can\'t find a mapping for the given model path '
																	    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'      )

											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as src_vocab_handle:
																	lowerCAmelCase      :Tuple				  = json.load(UpperCAmelCase      )
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as tgt_vocab_handle:
																	lowerCAmelCase      :str				  = json.load(UpperCAmelCase      )
																	lowerCAmelCase      :int				  = {v: k for k, v in tgt_vocab.items()}
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as merges_handle:
																	lowerCAmelCase      :Optional[int]				  = merges_handle.read().split('\n'      )[:-1]
											lowerCAmelCase      :List[Any]				  = [tuple(merge.split()[:2]      ) for merge in merges]
											lowerCAmelCase      :int				  = dict(zip(UpperCAmelCase    ,   range(len(UpperCAmelCase      )      )      )      )
											lowerCAmelCase      :str				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Dict[str, int]:
											return self.get_src_vocab()
					@property
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					int:
											return self.src_vocab_size
					def 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Tuple      )  ->					List[Any]:
											if lang not in self.cache_moses_punct_normalizer:
																	lowerCAmelCase      :str				  = self.sm.MosesPunctNormalizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = punct_normalizer
											return self.cache_moses_punct_normalizer[lang].normalize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											if lang not in self.cache_moses_tokenizer:
																	lowerCAmelCase      :Optional[int]				  = self.sm.MosesTokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :Any				  = moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    UpperCAmelCase    ,   aggressive_dash_splits=UpperCAmelCase    ,   return_str=UpperCAmelCase    ,   escape=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  str      )  ->					List[Any]:
											if lang not in self.cache_moses_detokenizer:
																	lowerCAmelCase      :Any				  = self.sm.MosesDetokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :str				  = moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  List[Any]      )  ->					int:
											lowerCAmelCase      :Dict				  = replace_unicode_punct(UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = self.moses_punct_norm(UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = remove_non_printing_char(UpperCAmelCase      )
											return text
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Union[str, Any]:
											return len(self.encoder      )
					@property
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											return len(self.decoder      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Tuple:
											return dict(self.encoder    ,   **self.added_tokens_encoder      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return dict(self.decoder    ,   **self.added_tokens_decoder      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  List[str]      )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = tuple(token[:-1]      ) + (token[-1] + '</w>',)
											if token in self.cache:
																	return self.cache[token]
											lowerCAmelCase      :Dict				  = get_pairs(UpperCAmelCase      )

											if not pairs:
																	return token + "</w>"

											while True:
																	lowerCAmelCase      :Optional[Any]				  = min(UpperCAmelCase    ,   key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,   float('inf'      )      )      )
																	if bigram not in self.bpe_ranks:
																							break
																	lowerCAmelCase  ,   lowerCAmelCase      :Tuple				  = bigram
																	lowerCAmelCase      :int				  = []
																	lowerCAmelCase      :Union[str, Any]				  = 0
																	while i < len(UpperCAmelCase      ):
																							try:
																													lowerCAmelCase      :List[Any]				  = word.index(UpperCAmelCase    ,   UpperCAmelCase      )
																							except ValueError:
																													new_word.extend(word[i:]      )
																													break
																							else:
																													new_word.extend(word[i:j]      )
																													lowerCAmelCase      :Any				  = j

																							if word[i] == first and i < len(UpperCAmelCase      ) - 1 and word[i + 1] == second:
																													new_word.append(first + second      )
																													i += 2
																							else:
																													new_word.append(word[i]      )
																													i += 1
																	lowerCAmelCase      :Optional[Any]				  = tuple(UpperCAmelCase      )
																	lowerCAmelCase      :Dict				  = new_word
																	if len(UpperCAmelCase      ) == 1:
																							break
																	else:
																							lowerCAmelCase      :Any				  = get_pairs(UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = ' '.join(UpperCAmelCase      )
											if word == "\n  </w>":
																	lowerCAmelCase      :List[Any]				  = '\n</w>'
											lowerCAmelCase      :Optional[Any]				  = word
											return word
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  List[str]="en"    ,   UpperCAmelCase       :  int=False      )  ->					List[Any]:
											lowerCAmelCase      :Optional[Any]				  = self.src_lang

											if self.do_lower_case:
																	lowerCAmelCase      :Tuple				  = text.lower()

											if bypass_tokenizer:
																	lowerCAmelCase      :List[str]				  = text.split()
											else:
																	lowerCAmelCase      :Tuple				  = self.moses_pipeline(UpperCAmelCase    ,   lang=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = self.moses_tokenize(UpperCAmelCase    ,   lang=UpperCAmelCase      )

											lowerCAmelCase      :int				  = []
											for token in text:
																	if token:
																							split_tokens.extend(list(self.bpe(UpperCAmelCase      ).split(' '      )      )      )

											return split_tokens
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Any      )  ->					List[Any]:
											return self.encoder.get(UpperCAmelCase    ,   self.encoder.get(self.unk_token      )      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Any      )  ->					Dict:
											return self.decoder.get(UpperCAmelCase    ,   self.unk_token      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Optional[Any]      )  ->					int:
											lowerCAmelCase      :Optional[int]				  = [t.replace(' '    ,   ''      ).replace('</w>'    ,   ' '      ) for t in tokens]
											lowerCAmelCase      :Optional[int]				  = ''.join(UpperCAmelCase      ).split()
											# detokenize
											lowerCAmelCase      :Dict				  = self.moses_detokenize(UpperCAmelCase    ,   self.tgt_lang      )
											return text
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Union[str, Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return token_ids_a + sep
											return token_ids_a + sep + token_ids_a + sep
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None    ,   UpperCAmelCase       :  bool = False      )  ->					List[int]:

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=UpperCAmelCase    ,   token_ids_a=UpperCAmelCase    ,   already_has_special_tokens=UpperCAmelCase      )
											# no bos used in fairseq
											if token_ids_a is not None:
																	return ([0] * len(UpperCAmelCase      )) + [1] + ([0] * len(UpperCAmelCase      )) + [1]
											return ([0] * len(UpperCAmelCase      )) + [1]
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Optional[Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return len(token_ids_a + sep      ) * [0]
											return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[str] = None      )  ->					Tuple[str]:
											if not os.path.isdir(UpperCAmelCase      ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																	return

											lowerCAmelCase      :Any				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']      )
											lowerCAmelCase      :Tuple				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']      )
											lowerCAmelCase      :Optional[int]				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	f.write(json.dumps(self.encoder    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	lowerCAmelCase      :int				  = {v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(UpperCAmelCase    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											lowerCAmelCase      :Optional[Any]				  = 0
											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,   key=lambda UpperCAmelCase      : kv[1]      ):
																							if index != token_index:
																													logger.warning(
																													    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																													    ' Please check that the tokenizer is not corrupted!'      )
																													lowerCAmelCase      :List[Any]				  = token_index
																							writer.write(' '.join(UpperCAmelCase      ) + '\n'      )
																							index += 1

											return src_vocab_file, tgt_vocab_file, merges_file
					def __getstate__(       self       :  Union[str, Any]      )  ->					int:
											lowerCAmelCase      :Any				  = self.__dict__.copy()
											lowerCAmelCase      :Tuple				  = None
											return state
					def __setstate__(       self       :  Any    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					List[Any]:
											lowerCAmelCase      :Tuple				  = d

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses