




'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ 		=      logging.get_logger(__name__)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]			):
  __a								=					SwinConfig(
      embed_dim=192				,					depths=(2, 2, 18, 2)				,					num_heads=(6, 12, 24, 48)				,					window_size=12				,					out_features=['stage2', 'stage3', 'stage4']				,					)

  __a								=					DetaConfig(
      backbone_config=a__				,					num_queries=900				,					encoder_ffn_dim=2048				,					decoder_ffn_dim=2048				,					num_feature_levels=5				,					assign_first_stage=a__				,					with_box_refine=a__				,					two_stage=a__				,					)

  # set labels
  __a								=					'huggingface/label-files'
  if "o365" in model_name:
    __a								=					366
    __a								=					'object365-id2label.json'
  else:
    __a								=					91
    __a								=					'coco-detection-id2label.json'

  __a								=					num_labels
  __a								=					json.load(open(cached_download(hf_hub_url(a__				,					a__				,					repo_type='dataset'			)			)				,					'r'			)			)
  __a								=					{int(a__			): v for k, v in idalabel.items()}
  __a								=					idalabel
  __a								=					{v: k for k, v in idalabel.items()}

  return config


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  __a								=					[]

  # stem
  # fmt: off
  rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')			)
  rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')			)
  rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')			)
  rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.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.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")			)

    if i < 3:
      rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")			)
      rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")			)

  rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')			)
  rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')			)
  rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')			)
  rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')			)
  rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')			)
  rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')			)

  # transformer encoder
  for i in range(config.encoder_layers			):
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")			)
    rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")			)

  # transformer decoder
  for i in range(config.decoder_layers			):
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")			)
    rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")			)

  # fmt: on

  return rename_keys


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]				,					a_	:							Any				,					a_	:							Any			):
  __a								=					dct.pop(a__			)
  __a								=					val


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							Optional[Any]			):
  __a								=					[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
  for i in range(len(backbone_config.depths			)			):
    __a								=					num_features[i]
    for j in range(backbone_config.depths[i]			):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      __a								=					state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"			)
      __a								=					state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"			)
      # next, add query, keys and values (in that order) to the state dict
      __a								=					in_proj_weight[:dim, :]
      __a								=					in_proj_bias[: dim]
      __a								=					in_proj_weight[
          dim : dim * 2, :
      ]
      __a								=					in_proj_bias[
          dim : dim * 2
      ]
      __a								=					in_proj_weight[
          -dim :, :
      ]
      __a								=					in_proj_bias[-dim :]
      # fmt: on


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							str			):
  __a								=					config.d_model
  for i in range(config.decoder_layers			):
    # read in weights + bias of input projection layer of self-attention
    __a								=					state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"			)
    __a								=					state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias"			)
    # next, add query, keys and values (in that order) to the state dict
    __a								=					in_proj_weight[:hidden_size, :]
    __a								=					in_proj_bias[:hidden_size]
    __a								=					in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    __a								=					in_proj_bias[hidden_size : hidden_size * 2]
    __a								=					in_proj_weight[-hidden_size:, :]
    __a								=					in_proj_bias[-hidden_size:]


def 						SCREAMING_SNAKE_CASE       (      ):
  __a								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  __a								=					Image.open(requests.get(a__				,					stream=a__			).raw			)

  return im


@torch.no_grad()
def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							List[str]				,					a_	:							str			):
  __a								=					get_deta_config(a__			)

  # load original state dict
  if model_name == "deta-swin-large":
    __a								=					hf_hub_download(repo_id='nielsr/deta-checkpoints'				,					filename='adet_swin_ft.pth'			)
  elif model_name == "deta-swin-large-o365":
    __a								=					hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'				,					filename='deta_swin_pt_o365.pth'			)
  else:
    raise ValueError(f"Model name {model_name} not supported"			)

  __a								=					torch.load(a__				,					map_location='cpu'			)['model']

  # original state dict
  for name, param in state_dict.items():
    print(a__				,					param.shape			)

  # rename keys
  __a								=					create_rename_keys(a__			)
  for src, dest in rename_keys:
    rename_key(a__				,					a__				,					a__			)
  read_in_swin_q_k_v(a__				,					config.backbone_config			)
  read_in_decoder_q_k_v(a__				,					a__			)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      __a								=					state_dict.pop(a__			)
      __a								=					val
    if "input_proj" in key:
      __a								=					state_dict.pop(a__			)
      __a								=					val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      __a								=					state_dict.pop(a__			)
      __a								=					val

    # finally, create HuggingFace model and load state dict
  __a								=					DetaForObjectDetection(a__			)
  model.load_state_dict(a__			)
  model.eval()

  __a								=					'cuda' if torch.cuda.is_available() else 'cpu'
  model.to(a__			)

  # load image processor
  __a								=					DetaImageProcessor(format='coco_detection'			)

  # verify our conversion on image
  __a								=					prepare_img()
  __a								=					processor(images=a__				,					return_tensors='pt'			)
  __a								=					encoding['pixel_values']
  __a								=					model(pixel_values.to(a__			)			)

  # verify logits
  print('Logits:'				,					outputs.logits[0, :3, :3]			)
  print('Boxes:'				,					outputs.pred_boxes[0, :3, :3]			)
  if model_name == "deta-swin-large":
    __a								=					torch.tensor(
        [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]			)
    __a								=					torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]			)
  elif model_name == "deta-swin-large-o365":
    __a								=					torch.tensor(
        [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]			)
    __a								=					torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]			)
  assert torch.allclose(outputs.logits[0, :3, :3]				,					expected_logits.to(a__			)				,					atol=1e-4			)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,					expected_boxes.to(a__			)				,					atol=1e-4			)
  print('Everything ok!'			)

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."			)
    Path(a__			).mkdir(exist_ok=a__			)
    model.save_pretrained(a__			)
    processor.save_pretrained(a__			)

  # Push to hub
  if push_to_hub:
    print('Pushing model and processor to hub...'			)
    model.push_to_hub(f"jozhang97/{model_name}"			)
    processor.push_to_hub(f"jozhang97/{model_name}"			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser()

  parser.add_argument(
      "--model_name",
      type=str,
      default="deta-swin-large",
      choices=["deta-swin-large", "deta-swin-large-o365"],
      help="Name of the model you\'d like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      help="Path to the folder to output PyTorch model.",
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )
  UpperCAmelCase_ 		=      parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_ 		=      get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
  from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase_ 		=      25_00_04
UpperCAmelCase_ 		=      25_00_20





@require_sentencepiece
@require_tokenizers
class    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _a							=      MBartaaTokenizer
   _a							=      MBartaaTokenizerFast
   _a							=      True
   _a							=      True







   def 		UpperCamelCase__			(	self      )				->    Dict:
     super().setUp()

     # We have a SentencePiece fixture for testing
     __a			=					MBartaaTokenizer(UpperCamelCase      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      ,							keep_accents=UpperCamelCase      )
     tokenizer.save_pretrained(self.tmpdirname      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					'<s>'
     __a			=					0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase      )      ,							UpperCamelCase      )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase      )      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					list(self.get_tokenizer().get_vocab().keys()      )

     self.assertEqual(vocab_keys[0]      ,							'<s>'      )
     self.assertEqual(vocab_keys[1]      ,							'<pad>'      )
     self.assertEqual(vocab_keys[-1]      ,							'<mask>'      )
     self.assertEqual(len(UpperCamelCase      )      ,							1054      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     self.assertEqual(self.get_tokenizer().vocab_size      ,							1054      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					MBartaaTokenizer(UpperCamelCase      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      ,							keep_accents=UpperCamelCase      )

     __a			=					tokenizer.tokenize('This is a test'      )
     self.assertListEqual(UpperCamelCase      ,							['▁This', '▁is', '▁a', '▁t', 'est']      )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(UpperCamelCase      )      ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,							)

     __a			=					tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
     self.assertListEqual(
         UpperCamelCase      ,							[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']      ,							)
     __a			=					tokenizer.convert_tokens_to_ids(UpperCamelCase      )
     self.assertListEqual(
         UpperCamelCase      ,							[
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
         ]      ,							)

     __a			=					tokenizer.convert_ids_to_tokens(UpperCamelCase      )
     self.assertListEqual(
         UpperCamelCase      ,							[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']      ,							)







   @slow
   def 		UpperCamelCase__			(	self      )				->    Dict:
     # fmt: off
     __a			=					{'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=UpperCamelCase      ,							model_name='facebook/mbart-large-50'      ,							revision='d3913889c59cd5c9e456b269c376325eabad57e2'      ,							)







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     if not self.test_slow_tokenizer:
       # as we don't have a slow version, we can't compare the outputs between slow and fast versions
       return

     __a			=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
         __a			=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )
         __a			=					self.tokenizer_class.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )

         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

         # Checks it save with the same files + the tokenizer.json file for the fast one
         self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
         __a			=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
         self.assertSequenceEqual(UpperCamelCase      ,							UpperCamelCase      )

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.from_pretrained(UpperCamelCase      )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(UpperCamelCase      ,							UpperCamelCase      )      )
           # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
           # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

         shutil.rmtree(UpperCamelCase      )

         # Save tokenizer rust, legacy_format=True
         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      ,							legacy_format=UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

         # Checks it save with the same files
         self.assertSequenceEqual(UpperCamelCase      ,							UpperCamelCase      )

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.from_pretrained(UpperCamelCase      )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(UpperCamelCase      ,							UpperCamelCase      )      )

         shutil.rmtree(UpperCamelCase      )

         # Save tokenizer rust, legacy_format=False
         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      ,							legacy_format=UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

         # Checks it saved the tokenizer.json file
         self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					tokenizer_p.from_pretrained(UpperCamelCase      )

         # Check special tokens are set accordingly on Rust and Python
         for key in tokenizer_pp.special_tokens_map:
           self.assertTrue(hasattr(UpperCamelCase      ,							UpperCamelCase      )      )

         shutil.rmtree(UpperCamelCase      )





@require_torch
@require_sentencepiece
@require_tokenizers
class    __lowercase						(  unittest.TestCase ):
   _a							=      """facebook/mbart-large-50-one-to-many-mmt"""
   _a							=      [
       """ UN Chief Says There Is No Military Solution in Syria""",
       """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
   ]
   _a							=      [
       """Şeful ONU declară că nu există o soluţie militară în Siria""",
       """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
       """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
       """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
   ]
   _a							=      [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Optional[int]:
     __a			=					MBartaaTokenizer.from_pretrained(
         cls.checkpoint_name      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      )
     __a			=					1
     return cls







   def 		UpperCamelCase__			(	self      )				->    int:
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']      ,							25_0001      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']      ,							25_0004      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']      ,							25_0020      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']      ,							25_0038      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     self.assertIn(UpperCamelCase      ,							self.tokenizer.all_special_ids      )
     __a			=					[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
     __a			=					self.tokenizer.decode(UpperCamelCase      ,							skip_special_tokens=UpperCamelCase      )
     __a			=					self.tokenizer.decode(generated_ids[1:]      ,							skip_special_tokens=UpperCamelCase      )
     self.assertEqual(UpperCamelCase      ,							UpperCamelCase      )
     self.assertNotIn(self.tokenizer.eos_token      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					['this is gunna be a long sentence ' * 20]
     assert isinstance(src_text[0]      ,							UpperCamelCase      )
     __a			=					10
     __a			=					self.tokenizer(UpperCamelCase      ,							max_length=UpperCamelCase      ,							truncation=UpperCamelCase      ).input_ids[0]
     self.assertEqual(ids[0]      ,							UpperCamelCase      )
     self.assertEqual(ids[-1]      ,							2      )
     self.assertEqual(len(UpperCamelCase      )      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    str:
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )      ,							[25_0053, 25_0001]      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					tempfile.mkdtemp()
     __a			=					self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(UpperCamelCase      )
     __a			=					MBartaaTokenizer.from_pretrained(UpperCamelCase      )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,							UpperCamelCase      )







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					self.tokenizer(self.src_text      ,							text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							return_tensors='pt'      )
     __a			=					shift_tokens_right(batch['labels']      ,							self.tokenizer.pad_token_id      )

     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == RO_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer(
         self.src_text      ,							text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=len(self.expected_src_tokens      )      ,							return_tensors='pt'      ,							)
     __a			=					shift_tokens_right(batch['labels']      ,							self.tokenizer.pad_token_id      )

     self.assertIsInstance(UpperCamelCase      ,							UpperCamelCase      )

     self.assertEqual((2, 14)      ,							batch.input_ids.shape      )
     self.assertEqual((2, 14)      ,							batch.attention_mask.shape      )
     __a			=					batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens      ,							UpperCamelCase      )
     self.assertEqual(2      ,							batch.decoder_input_ids[0, 0]      )  # decoder_start_token_id
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens      ,							[EN_CODE]      )
     self.assertEqual(self.tokenizer.suffix_tokens      ,							[self.tokenizer.eos_token_id]      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.tokenizer(self.src_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=3      ,							return_tensors='pt'      )
     __a			=					self.tokenizer(
         text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=10      ,							return_tensors='pt'      )
     __a			=					targets['input_ids']
     __a			=					shift_tokens_right(UpperCamelCase      ,							self.tokenizer.pad_token_id      )

     self.assertEqual(batch.input_ids.shape[1]      ,							3      )
     self.assertEqual(batch.decoder_input_ids.shape[1]      ,							10      )







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.tokenizer._build_translation_inputs(
         'A test'      ,							return_tensors='pt'      ,							src_lang='en_XX'      ,							tgt_lang='ar_AR'      )

     self.assertEqual(
         nested_simplify(UpperCamelCase      )      ,							{
             # en_XX, A, test, EOS
             'input_ids': [[25_0004, 62, 3034, 2]],
             'attention_mask': [[1, 1, 1, 1]],
             # ar_AR
             'forced_bos_token_id': 25_0001,
         }      ,							)



