




'''simple docstring'''


import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


UpperCAmelCase_ 		=      {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 13_10_72,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
}


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							Optional[int]			):
  return torch.atana(a_				,					a_			) / math.pi * 2


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]			):
  __a			=					torch.sin(t * math.pi / 2			) ** 2
  __a			=					(1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(a_				,					a_			)





class    __lowercase						(  __magic_name__ ):
   pass





class    __lowercase						(  nn.Module ):







   def __init__(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     super().__init__()

     __a			=					DiffusionAttnUnetaD(UpperCamelCase      ,							n_attn_layers=4      )
     __a			=					deepcopy(self.diffusion      )
     __a			=					torch.quasirandom.SobolEngine(1      ,							scramble=UpperCamelCase      )


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  __a			=					MODELS_MAP[model_name]['url']
  os.system(f"wget {url} ./"			)

  return f"./{model_name}.ckpt"


UpperCAmelCase_ 		=      {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
UpperCAmelCase_ 		=      {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
UpperCAmelCase_ 		=      {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
UpperCAmelCase_ 		=      {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

UpperCAmelCase_ 		=      {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

UpperCAmelCase_ 		=      {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  if name.startswith('skip'			):
    return name.replace('skip'				,					RES_CONV_MAP['skip']			)

  # name has to be of format main.{digit}
  if not name.startswith('main.'			):
    raise ValueError(f"ResConvBlock error with {name}"			)

  return name.replace(name[:6]				,					RES_CONV_MAP[name[:6]]			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  for key, value in ATTN_MAP.items():
    if name.startswith(a_			) and not isinstance(a_				,					a_			):
      return name.replace(a_				,					a_			)
    elif name.startswith(a_			):
      return [name.replace(a_				,					a_			) for v in value]
  raise ValueError(f"Attn error with {name}"			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]				,					a_	:							int=13			):
  __a			=					input_string

  if string.split('.'			)[0] == "timestep_embed":
    return string.replace('timestep_embed'				,					'time_proj'			)

  __a			=					0
  if string.startswith('net.3.'			):
    depth += 1
    __a			=					string[6:]
  elif string.startswith('net.'			):
    __a			=					string[4:]

  while string.startswith('main.7.'			):
    depth += 1
    __a			=					string[7:]

  if string.startswith('main.'			):
    __a			=					string[5:]

  # mid block
  if string[:2].isdigit():
    __a			=					string[:2]
    __a			=					string[2:]
  else:
    __a			=					string[0]
    __a			=					string[1:]

  if depth == max_depth:
    __a			=					MID_NUM_TO_LAYER[layer_num]
    __a			=					'mid_block'
  elif depth > 0 and int(a_			) < 7:
    __a			=					DOWN_NUM_TO_LAYER[layer_num]
    __a			=					f"down_blocks.{depth}"
  elif depth > 0 and int(a_			) > 7:
    __a			=					UP_NUM_TO_LAYER[layer_num]
    __a			=					f"up_blocks.{max_depth - depth - 1}"
  elif depth == 0:
    __a			=					DEPTH_0_TO_LAYER[layer_num]
    __a			=					f"up_blocks.{max_depth - 1}" if int(a_			) > 3 else 'down_blocks.0'

  if not string_left.startswith('.'			):
    raise ValueError(f"Naming error with {input_string} and string_left: {string_left}."			)

  __a			=					string_left[1:]

  if "resnets" in new_layer:
    __a			=					convert_resconv_naming(a_			)
  elif "attentions" in new_layer:
    __a			=					convert_attn_naming(a_			)
    __a			=					new_string_left

  if not isinstance(a_				,					a_			):
    __a			=					prefix + '.' + new_layer + '.' + string_left
  else:
    __a			=					[prefix + '.' + new_layer + '.' + s for s in string_left]
  return new_string


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  __a			=					{}
  for k, v in state_dict.items():
    if k.endswith('kernel'			):
      # up- and downsample layers, don't have trainable weights
      continue

    __a			=					rename(a_			)

    # check if we need to transform from Conv => Linear for attention
    if isinstance(a_				,					a_			):
      __a			=					transform_conv_attns(a_				,					a_				,					a_			)
    else:
      __a			=					v

  return new_state_dict


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							List[str]				,					a_	:							List[str]			):
  if len(a_			) == 1:
    if len(v.shape			) == 3:
      # weight
      __a			=					v[:, :, 0]
    else:
      # bias
      __a			=					v
  else:
    # qkv matrices
    __a			=					v.shape[0]
    __a			=					trippled_shape // 3
    for i in range(3			):
      if len(v.shape			) == 3:
        __a			=					v[i * single_shape : (i + 1) * single_shape, :, 0]
      else:
        __a			=					v[i * single_shape : (i + 1) * single_shape]
  return new_state_dict


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  __a			=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

  __a			=					args.model_path.split('/'			)[-1].split('.'			)[0]
  if not os.path.isfile(args.model_path			):
    assert (
        model_name == args.model_path
    ), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
    __a			=					download(a_			)

  __a			=					MODELS_MAP[model_name]['sample_rate']
  __a			=					MODELS_MAP[model_name]['sample_size']

  __a			=					Object()
  __a			=					sample_size
  __a			=					sample_rate
  __a			=					0

  __a			=					UNetaDModel(sample_size=a_				,					sample_rate=a_			)
  __a			=					diffusers_model.state_dict()

  __a			=					DiffusionUncond(a_			)
  orig_model.load_state_dict(torch.load(args.model_path				,					map_location=a_			)['state_dict']			)
  __a			=					orig_model.diffusion_ema.eval()
  __a			=					orig_model.state_dict()
  __a			=					rename_orig_weights(a_			)

  __a			=					set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
  __a			=					set(diffusers_state_dict.keys()			) - set(renamed_state_dict.keys()			)

  assert len(a_			) == 0, f"Problem with {renamed_minus_diffusers}"
  assert all(k.endswith('kernel'			) for k in list(a_			)			), f"Problem with {diffusers_minus_renamed}"

  for key, value in renamed_state_dict.items():
    assert (
        diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
    ), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
    if key == "time_proj.weight":
      __a			=					value.squeeze()

    __a			=					value

  diffusers_model.load_state_dict(a_			)

  __a			=					100
  __a			=					33

  __a			=					IPNDMScheduler(num_train_timesteps=a_			)

  __a			=					torch.manual_seed(a_			)
  __a			=					torch.randn([1, 2, config.sample_size]				,					generator=a_			).to(a_			)

  __a			=					torch.linspace(1				,					0				,					steps + 1				,					device=a_			)[:-1]
  __a			=					get_crash_schedule(a_			)

  __a			=					DanceDiffusionPipeline(unet=a_				,					scheduler=a_			)

  __a			=					torch.manual_seed(33			)
  __a			=					pipe(num_inference_steps=a_				,					generator=a_			).audios

  __a			=					sampling.iplms_sample(a_				,					a_				,					a_				,					{}			)
  __a			=					generated.clamp(-1				,					1			)

  __a			=					(generated - audio).abs().sum()
  __a			=					(generated - audio).abs().max()

  if args.save:
    pipe.save_pretrained(args.checkpoint_path			)

  print('Diff sum'				,					a_			)
  print('Diff max'				,					a_			)

  assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/"

  print(f"Conversion for {model_name} successful!"			)


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

  parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
  parser.add_argument(
      "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
  )
  parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
  UpperCAmelCase_ 		=      parser.parse_args()

  main(args)









'''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,
         }      ,							)



