






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  _a    :   Dict        =			None


_a    :   str        =			logging.get_logger(__name__)


_a    :   List[str]        =			{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_a    :   Union[str, Any]        =			{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

_a    :   Optional[int]        =			{
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
_a    :   Optional[int]        =			["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    VOCAB_FILES_NAMES
      A          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A          =    PRETRAINED_VOCAB_FILES_MAP
      A          =    ['''input_ids''', '''attention_mask''']
      A          =    MBartTokenizer

      A          =    []
      A          =    []
      def __init__(   self    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase="<s>"    ,	_lowerCAmelCase="</s>"    ,	_lowerCAmelCase="</s>"    ,	_lowerCAmelCase="<s>"    ,	_lowerCAmelCase="<unk>"    ,	_lowerCAmelCase="<pad>"    ,	_lowerCAmelCase="<mask>"    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=None    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				AddedToken(_lowerCAmelCase    ,	lstrip=_lowerCAmelCase    ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) else mask_token

          super().__init__(
              vocab_file=_lowerCAmelCase    ,	tokenizer_file=_lowerCAmelCase    ,	bos_token=_lowerCAmelCase    ,	eos_token=_lowerCAmelCase    ,	sep_token=_lowerCAmelCase    ,	cls_token=_lowerCAmelCase    ,	unk_token=_lowerCAmelCase    ,	pad_token=_lowerCAmelCase    ,	mask_token=_lowerCAmelCase    ,	src_lang=_lowerCAmelCase    ,	tgt_lang=_lowerCAmelCase    ,	additional_special_tokens=_lowerCAmelCase    ,	**_lowerCAmelCase    ,	)

          lowerCAmelCase__   :Tuple      							=				vocab_file
          lowerCAmelCase__   :List[Any]      							=				False if not self.vocab_file else True

          lowerCAmelCase__   :int      							=				FAIRSEQ_LANGUAGE_CODES.copy()

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

          self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}		)
          lowerCAmelCase__   :List[str]      							=				{
              lang_code: self.convert_tokens_to_ids(_lowerCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          lowerCAmelCase__   :Optional[int]      							=				src_lang if src_lang is not None else 'en_XX'
          lowerCAmelCase__   :Union[str, Any]      							=				self.convert_tokens_to_ids(self._src_lang		)
          lowerCAmelCase__   :str      							=				tgt_lang
          self.set_src_lang_special_tokens(self._src_lang		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self._src_lang
      @src_lang.setter
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				new_src_lang
          self.set_src_lang_special_tokens(self._src_lang		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				[self.sep_token_id]
          lowerCAmelCase__   :Tuple      							=				[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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		):



          '''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"		)
          lowerCAmelCase__   :Union[str, Any]      							=				src_lang
          lowerCAmelCase__   :Optional[Any]      							=				self(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase    ,	return_tensors=_lowerCAmelCase    ,	**_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				self.convert_tokens_to_ids(_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				tgt_lang_id
          return inputs
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = "en_XX"    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = "ro_RO"    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				src_lang
          lowerCAmelCase__   :Tuple      							=				tgt_lang
          return super().prepare_seqaseq_batch(_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.set_src_lang_special_tokens(self.src_lang		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.set_tgt_lang_special_tokens(self.tgt_lang		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.convert_tokens_to_ids(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[Any]      							=				[]
          lowerCAmelCase__   :Union[str, Any]      							=				[self.eos_token_id, self.cur_lang_code]

          lowerCAmelCase__   :Union[str, Any]      							=				self.convert_ids_to_tokens(self.prefix_tokens		)
          lowerCAmelCase__   :List[Any]      							=				self.convert_ids_to_tokens(self.suffix_tokens		)

          lowerCAmelCase__   :Dict      							=				processors.TemplateProcessing(
              single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,	pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,	self.prefix_tokens + self.suffix_tokens		)		)    ,	)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.convert_tokens_to_ids(_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				[]
          lowerCAmelCase__   :Any      							=				[self.eos_token_id, self.cur_lang_code]

          lowerCAmelCase__   :Optional[Any]      							=				self.convert_ids_to_tokens(self.prefix_tokens		)
          lowerCAmelCase__   :List[str]      							=				self.convert_ids_to_tokens(self.suffix_tokens		)

          lowerCAmelCase__   :Optional[int]      							=				processors.TemplateProcessing(
              single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,	pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,	self.prefix_tokens + self.suffix_tokens		)		)    ,	)



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = None		):



          '''simple docstring'''







          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

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

          if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		):
              copyfile(self.vocab_file    ,	_lowerCAmelCase		)

          return (out_vocab_file,)











import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    KandinskyVaaPipeline
      A          =    [
          '''image_embeds''',
          '''negative_image_embeds''',
      ]
      A          =    ['''image_embeds''', '''negative_image_embeds''']
      A          =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      A          =    False
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 32
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 32
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.time_input_dim
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.time_input_dim * 4
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 100
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)

          lowerCAmelCase__   :List[str]      							=				{
              "in_channels": 4,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 8,
              "addition_embed_type": "image",
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "encoder_hid_dim": self.text_embedder_hidden_size,
              "encoder_hid_dim_type": "image_proj",
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": None,
          }

          lowerCAmelCase__   :List[str]      							=				UNetaDConditionModel(**_lowerCAmelCase		)
          return model
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)
          lowerCAmelCase__   :Optional[Any]      							=				VQModel(**self.dummy_movq_kwargs		)
          return model
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.dummy_unet
          lowerCAmelCase__   :List[str]      							=				self.dummy_movq

          lowerCAmelCase__   :List[Any]      							=				DDIMScheduler(
              num_train_timesteps=1_000    ,	beta_schedule="linear"    ,	beta_start=0.0_0085    ,	beta_end=0.012    ,	clip_sample=_lowerCAmelCase    ,	set_alpha_to_one=_lowerCAmelCase    ,	steps_offset=1    ,	prediction_type="epsilon"    ,	thresholding=_lowerCAmelCase    ,	)

          lowerCAmelCase__   :Optional[Any]      							=				{
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }
          return components
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				floats_tensor((1, self.text_embedder_hidden_size)    ,	rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				floats_tensor((1, self.text_embedder_hidden_size)    ,	rng=random.Random(seed + 1		)		).to(
              _lowerCAmelCase		)
          if str(_lowerCAmelCase		).startswith("mps"		):
              lowerCAmelCase__   :List[Any]      							=				torch.manual_seed(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :Optional[int]      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				{
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "generator": generator,
              "height": 64,
              "width": 64,
              "guidance_scale": 4.0,
              "num_inference_steps": 2,
              "output_type": "np",
          }
          return inputs



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				"cpu"

          lowerCAmelCase__   :Dict      							=				self.get_dummy_components()

          lowerCAmelCase__   :Tuple      							=				self.pipeline_class(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				pipe.to(_lowerCAmelCase		)

          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				pipe(**self.get_dummy_inputs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :Dict      							=				output.images

          lowerCAmelCase__   :str      							=				pipe(
              **self.get_dummy_inputs(_lowerCAmelCase		)    ,	return_dict=_lowerCAmelCase    ,	)[0]

          lowerCAmelCase__   :Tuple      							=				image[0, -3:, -3:, -1]
          lowerCAmelCase__   :List[Any]      							=				image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          lowerCAmelCase__   :Tuple      							=				np.array(
              [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]		)

          assert (
              np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
          ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
          ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







@slow
@require_torch_gpu
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"		)

          lowerCAmelCase__   :str      							=				KandinskyVaaPriorPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-prior"    ,	torch_dtype=torch.floataa		)
          pipe_prior.to(_lowerCAmelCase		)

          lowerCAmelCase__   :str      							=				KandinskyVaaPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-decoder"    ,	torch_dtype=torch.floataa		)
          lowerCAmelCase__   :Union[str, Any]      							=				pipeline.to(_lowerCAmelCase		)
          pipeline.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[int]      							=				"red cat, 4k photo"

          lowerCAmelCase__   :Tuple      							=				torch.Generator(device="cuda"		).manual_seed(0		)
          lowerCAmelCase__							,lowerCAmelCase__   :Dict      							=				pipe_prior(
              _lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=5    ,	negative_prompt=""    ,	).to_tuple()

          lowerCAmelCase__   :List[Any]      							=				torch.Generator(device="cuda"		).manual_seed(0		)
          lowerCAmelCase__   :List[str]      							=				pipeline(
              image_embeds=_lowerCAmelCase    ,	negative_image_embeds=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	num_inference_steps=100    ,	output_type="np"    ,	)

          lowerCAmelCase__   :int      							=				output.images[0]

          assert image.shape == (512, 512, 3)

          assert_mean_pixel_difference(_lowerCAmelCase    ,	_lowerCAmelCase		)



