

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				List[str]:
	__A :			Optional[int]		    =			384
	if "tiny" in model_name:
		__A :			Optional[Any]		    =			[3, 3, 9, 3]
		__A :			Tuple		    =			[96, 192, 384, 768]
	if "small" in model_name:
		__A :			Any		    =			[3, 3, 27, 3]
		__A :			Dict		    =			[96, 192, 384, 768]
	if "base" in model_name:
		__A :			Tuple		    =			[3, 3, 27, 3]
		__A :			int		    =			[128, 256, 512, 1024]
		__A :			Optional[Any]		    =			512
	if "large" in model_name:
		__A :			List[str]		    =			[3, 3, 27, 3]
		__A :			int		    =			[192, 384, 768, 1536]
		__A :			int		    =			768
	if "xlarge" in model_name:
		__A :			Optional[Any]		    =			[3, 3, 27, 3]
		__A :			Optional[int]		    =			[256, 512, 1024, 2048]
		__A :			List[Any]		    =			1024

	# set label information
	__A :			Any		    =			150
	__A :			Optional[Any]		    =			"""huggingface/label-files"""
	__A :			Dict		    =			"""ade20k-id2label.json"""
	__A :			str		    =			json.load(open(hf_hub_download(a__			,a__			,repo_type="""dataset"""       )			,"""r"""       )       )
	__A :			Optional[int]		    =			{int(a__       ): v for k, v in idalabel.items()}
	__A :			str		    =			{v: k for k, v in idalabel.items()}

	__A :			int		    =			ConvNextConfig(
	    depths=a__			,hidden_sizes=a__			,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
	__A :			Union[str, Any]		    =			UperNetConfig(
	    backbone_config=a__			,auxiliary_in_channels=a__			,num_labels=a__			,idalabel=a__			,labelaid=a__			,)

	return config
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				Dict:
	__A :			Any		    =			[]

	# fmt: off
	# stem
	rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")       )
	rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")       )
	rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")       )
	rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")       )
	# stages
	for i in range(len(config.backbone_config.depths       )       ):
		for j in range(config.backbone_config.depths[i]       ):
			rename_keys.append((f"""backbone.stages.{i}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")       )
			rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")       )
		if i > 0:
			rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")       )
			rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")       )
			rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")       )
			rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""")       )

		rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")       )
		rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")       )

	# decode head
	rename_keys.extend(
	    [
	        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
	        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
	        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
	        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
	    ]       )
	# fmt: on

	return rename_keys
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Dict			,a__    :   List[Any]       )					->				int:
	__A :			Any		    =			dct.pop(a__       )
	__A :			Dict		    =			val
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   List[str]			,a__    :   Optional[int]       )					->				int:
	__A :			Optional[int]		    =			{
	    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
	    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
	    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
	    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
	    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
	}
	__A :			Dict		    =			model_name_to_url[model_name]
	__A :			Optional[int]		    =			torch.hub.load_state_dict_from_url(a__			,map_location="""cpu"""       )["""state_dict"""]

	__A :			Dict		    =			get_upernet_config(a__       )
	__A :			Tuple		    =			UperNetForSemanticSegmentation(a__       )
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		__A :			Optional[Any]		    =			state_dict.pop(a__       )
		if "bn" in key:
			__A :			Dict		    =			key.replace("""bn"""			,"""batch_norm"""       )
		__A :			List[Any]		    =			val

	# rename keys
	__A :			Optional[int]		    =			create_rename_keys(a__       )
	for src, dest in rename_keys:
		rename_key(a__			,a__			,a__       )

	model.load_state_dict(a__       )

	# verify on image
	__A :			Dict		    =			"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
	__A :			Optional[Any]		    =			Image.open(requests.get(a__			,stream=a__       ).raw       ).convert("""RGB"""       )

	__A :			Optional[int]		    =			SegformerImageProcessor()
	__A :			Union[str, Any]		    =			processor(a__			,return_tensors="""pt"""       ).pixel_values

	with torch.no_grad():
		__A :			List[str]		    =			model(a__       )

	if model_name == "upernet-convnext-tiny":
		__A :			int		    =			torch.tensor(
		    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]       )
	elif model_name == "upernet-convnext-small":
		__A :			Optional[int]		    =			torch.tensor(
		    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]       )
	elif model_name == "upernet-convnext-base":
		__A :			List[Any]		    =			torch.tensor(
		    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]       )
	elif model_name == "upernet-convnext-large":
		__A :			str		    =			torch.tensor(
		    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]       )
	elif model_name == "upernet-convnext-xlarge":
		__A :			Optional[Any]		    =			torch.tensor(
		    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]       )
	print("""Logits:"""			,outputs.logits[0, 0, :3, :3]       )
	assert torch.allclose(outputs.logits[0, 0, :3, :3]			,a__			,atol=1E-4       )
	print("""Looks ok!"""       )

	if pytorch_dump_folder_path is not None:
		print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""       )
		model.save_pretrained(a__       )
		print(f"""Saving processor to {pytorch_dump_folder_path}"""       )
		processor.save_pretrained(a__       )

	if push_to_hub:
		print(f"""Pushing model and processor for {model_name} to hub"""       )
		model.push_to_hub(f"""openmmlab/{model_name}"""       )
		processor.push_to_hub(f"""openmmlab/{model_name}"""       )


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''upernet-convnext-tiny''',
			    type=str,
			    choices=[f"""upernet-convnext-{size}""" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
			    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						"▁"

UpperCAmelCase__							  =						{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__							  =						{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase__							  =						{
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
UpperCAmelCase__							  =						["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    VOCAB_FILES_NAMES
      __snake_case	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case	    =    PRETRAINED_VOCAB_FILES_MAP
      __snake_case	    =    ['''input_ids''', '''attention_mask''']

      __snake_case	    =    []
      __snake_case	    =    []







      def __init__( self :		int					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int]="<s>"					,   __UpperCAmelCase :		Union[str, Any]="</s>"					,   __UpperCAmelCase :		List[Any]="</s>"					,   __UpperCAmelCase :		List[Any]="<s>"					,   __UpperCAmelCase :		Optional[int]="<unk>"					,   __UpperCAmelCase :		Optional[int]="<pad>"					,   __UpperCAmelCase :		Tuple="<mask>"					,   __UpperCAmelCase :		Any=None					,   __UpperCAmelCase :		Dict=None					,   __UpperCAmelCase :		int=None					,   __UpperCAmelCase :		Optional[Dict[str, Any]] = None					,   __UpperCAmelCase :		List[Any]=None					,   __UpperCAmelCase :		int=False					,   **__UpperCAmelCase :		Dict					,   )       ->List[str]:




       """simple docstring"""


       a						    = AddedToken(__UpperCAmelCase					,   lstrip=__UpperCAmelCase					,   rstrip=__UpperCAmelCase    ) if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ) else mask_token

       a						    = {} if sp_model_kwargs is None else sp_model_kwargs
       a						    = legacy_behaviour

       super().__init__(
           bos_token=__UpperCAmelCase					,   eos_token=__UpperCAmelCase					,   unk_token=__UpperCAmelCase					,   sep_token=__UpperCAmelCase					,   cls_token=__UpperCAmelCase					,   pad_token=__UpperCAmelCase					,   mask_token=__UpperCAmelCase					,   tokenizer_file=__UpperCAmelCase					,   src_lang=__UpperCAmelCase					,   tgt_lang=__UpperCAmelCase					,   additional_special_tokens=__UpperCAmelCase					,   sp_model_kwargs=self.sp_model_kwargs					,   legacy_behaviour=__UpperCAmelCase					,   **__UpperCAmelCase					,   )

       a						    = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(str(__UpperCAmelCase    )    )
       a						    = vocab_file
       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
       # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       a						    = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       a						    = 1

       a						    = len(self.sp_model    )
       a						    = {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCAmelCase    )
       }
       a						    = {v: k for k, v in self.lang_code_to_id.items()}
       a						    = len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
       a						    = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       a						    = list(self.lang_code_to_id.keys()    )

       if additional_special_tokens is not None:
        # Only add those special tokens if they are not already there.
        self._additional_special_tokens.extend(
            [t for t in additional_special_tokens if t not in self._additional_special_tokens]    )

       a						    = src_lang if src_lang is not None else '''eng_Latn'''
       a						    = self.lang_code_to_id[self._src_lang]
       a						    = tgt_lang
       self.set_src_lang_special_tokens(self._src_lang    )







      def __getstate__( self :		str    )       ->Any:




       """simple docstring"""


       a						    = self.__dict__.copy()
       a						    = None
       a						    = self.sp_model.serialized_model_proto()
       return state







      def __setstate__( self :		List[str]					,   __UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       a						    = d

       # for backward compatibility
       if not hasattr(self					,   '''sp_model_kwargs'''    ):
        a						    = {}

       a						    = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->str:




       """simple docstring"""


       return self._src_lang







      @src_lang.setter
      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->None:




       """simple docstring"""


       a						    = new_src_lang
       self.set_src_lang_special_tokens(self._src_lang    )







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None					,   __UpperCAmelCase :		bool = False    )       ->List[int]:




       """simple docstring"""


       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__UpperCAmelCase					,   token_ids_a=__UpperCAmelCase					,   already_has_special_tokens=__UpperCAmelCase    )

       a						    = [1] * len(self.prefix_tokens    )
       a						    = [1] * len(self.suffix_tokens    )
       if token_ids_a is None:
        return prefix_ones + ([0] * len(__UpperCAmelCase    )) + suffix_ones
       return prefix_ones + ([0] * len(__UpperCAmelCase    )) + ([0] * len(__UpperCAmelCase    )) + suffix_ones







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None    )       ->List[int]:




       """simple docstring"""


       if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







      def  __lowerCAmelCase   ( self :		int					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None    )       ->List[int]:




       """simple docstring"""


       a						    = [self.sep_token_id]
       a						    = [self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep    ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str]					,   __UpperCAmelCase :		Optional[str]					,   **__UpperCAmelCase :		Tuple    )       ->Tuple:




       """simple docstring"""


       if src_lang is None or tgt_lang is None:
        raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''    )
       a						    = src_lang
       a						    = self(__UpperCAmelCase					,   add_special_tokens=__UpperCAmelCase					,   return_tensors=__UpperCAmelCase					,   **__UpperCAmelCase    )
       a						    = self.convert_tokens_to_ids(__UpperCAmelCase    )
       a						    = tgt_lang_id
       return inputs







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




       """simple docstring"""


       a						    = {self.convert_ids_to_tokens(__UpperCAmelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->List[str]:




       """simple docstring"""


       return self.sp_model.encode(__UpperCAmelCase					,   out_type=__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		str    )       ->List[Any]:




       """simple docstring"""


       if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
       a						    = self.sp_model.PieceToId(__UpperCAmelCase    )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		int    )       ->Optional[Any]:




       """simple docstring"""


       if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset    )







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		List[str]    )       ->List[Any]:




       """simple docstring"""


       a						    = ''''''.join(__UpperCAmelCase    ).replace(__UpperCAmelCase					,   ''' '''    ).strip()
       return out_string







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str] = None    )       ->Tuple[str]:




       """simple docstring"""


       if not os.path.isdir(__UpperCAmelCase    ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
        return
       a						    = os.path.join(
           __UpperCAmelCase					,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(__UpperCAmelCase    ) and os.path.isfile(self.vocab_file    ):
        copyfile(self.vocab_file					,   __UpperCAmelCase    )
       elif not os.path.isfile(self.vocab_file    ):
        with open(__UpperCAmelCase					,   '''wb'''    ) as fi:
         a						    = self.sp_model.serialized_model_proto()
         fi.write(__UpperCAmelCase    )

       return (out_vocab_file,)







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str = "eng_Latn"					,   __UpperCAmelCase :		Optional[List[str]] = None					,   __UpperCAmelCase :		str = "fra_Latn"					,   **__UpperCAmelCase :		Optional[int]					,   )       ->BatchEncoding:




       """simple docstring"""


       a						    = src_lang
       a						    = tgt_lang
       return super().prepare_seqaseq_batch(__UpperCAmelCase					,   __UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->List[str]:




       """simple docstring"""


       return self.set_src_lang_special_tokens(self.src_lang    )







      def  __lowerCAmelCase   ( self :		Any    )       ->Any:




       """simple docstring"""


       return self.set_tgt_lang_special_tokens(self.tgt_lang    )







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		Tuple    )       ->None:




       """simple docstring"""


       a						    = self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
        a						    = []
        a						    = [self.eos_token_id, self.cur_lang_code]
       else:
        a						    = [self.cur_lang_code]
        a						    = [self.eos_token_id]







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		str    )       ->None:




       """simple docstring"""


       a						    = self.lang_code_to_id[lang]
       if self.legacy_behaviour:
        a						    = []
        a						    = [self.eos_token_id, self.cur_lang_code]
       else:
        a						    = [self.cur_lang_code]
        a						    = [self.eos_token_id]


