






'''simple docstring'''




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:
		lowerCAmelCase__          =						None


lowerCAmelCase__          =						logging.get_logger(__name__)


lowerCAmelCase__          =						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__          =						{
    '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',
    },
}

lowerCAmelCase__          =						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowerCAmelCase__          =						['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       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  ['''input_ids''', '''attention_mask''']
	_lowerCamelCase              =  MBartTokenizer

	_lowerCamelCase              =  []
	_lowerCamelCase              =  []

	def __init__(      self   :						int  ,					UpperCAmelCase_   :						List[Any]=None  ,					UpperCAmelCase_   :						int=None  ,					UpperCAmelCase_   :						List[str]="<s>"  ,					UpperCAmelCase_   :						Union[str, Any]="</s>"  ,					UpperCAmelCase_   :						Any="</s>"  ,					UpperCAmelCase_   :						Tuple="<s>"  ,					UpperCAmelCase_   :						Tuple="<unk>"  ,					UpperCAmelCase_   :						Dict="<pad>"  ,					UpperCAmelCase_   :						Any="<mask>"  ,					UpperCAmelCase_   :						Any=None  ,					UpperCAmelCase_   :						str=None  ,					UpperCAmelCase_   :						Union[str, Any]=None  ,					**UpperCAmelCase_   :						List[Any]  ,					):
			# Mask token behave like a normal word, i.e. include the space before it
			UpperCamelCase__				:					Union[str, Any]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else mask_token

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

			UpperCamelCase__				:					Union[str, Any]					=					vocab_file
			UpperCamelCase__				:					Dict					=					False if not self.vocab_file else True

			UpperCamelCase__				:					Optional[Any]					=					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})
			UpperCamelCase__				:					Optional[Any]					=					{
			    lang_code: self.convert_tokens_to_ids(UpperCAmelCase_) for lang_code in FAIRSEQ_LANGUAGE_CODES
			}

			UpperCamelCase__				:					Tuple					=					src_lang if src_lang is not None else 'en_XX'
			UpperCamelCase__				:					Dict					=					self.convert_tokens_to_ids(self._src_lang)
			UpperCamelCase__				:					str					=					tgt_lang
			self.set_src_lang_special_tokens(self._src_lang)

	@property
	def      __UpperCamelCase							(      self   :						Any):
			return self._src_lang

	@src_lang.setter
	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					List[str]					=					new_src_lang
			self.set_src_lang_special_tokens(self._src_lang)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			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      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			UpperCamelCase__				:					Any					=					[self.sep_token_id]
			UpperCamelCase__				:					List[str]					=					[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      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str]  ,					UpperCAmelCase_   :						Optional[str]  ,					**UpperCAmelCase_   :						Optional[int]):
			if src_lang is None or tgt_lang is None:
					raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
			UpperCamelCase__				:					int					=					src_lang
			UpperCamelCase__				:					Tuple					=					self(UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					**UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					self.convert_tokens_to_ids(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[Any]					=					tgt_lang_id
			return inputs

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						List[str]  ,					UpperCAmelCase_   :						str = "en_XX"  ,					UpperCAmelCase_   :						Optional[List[str]] = None  ,					UpperCAmelCase_   :						str = "ro_RO"  ,					**UpperCAmelCase_   :						Dict  ,					):
			UpperCamelCase__				:					List[Any]					=					src_lang
			UpperCamelCase__				:					Tuple					=					tgt_lang
			return super().prepare_seqaseq_batch(UpperCAmelCase_  ,					UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			return self.set_src_lang_special_tokens(self.src_lang)

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			return self.set_tgt_lang_special_tokens(self.tgt_lang)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					Any					=					self.convert_tokens_to_ids(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					[]
			UpperCamelCase__				:					str					=					[self.eos_token_id, self.cur_lang_code]

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

			UpperCamelCase__				:					List[Any]					=					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      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str):
			UpperCamelCase__				:					Tuple					=					self.convert_tokens_to_ids(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[Any]					=					[]
			UpperCamelCase__				:					Optional[Any]					=					[self.eos_token_id, self.cur_lang_code]

			UpperCamelCase__				:					List[str]					=					self.convert_ids_to_tokens(self.prefix_tokens)
			UpperCamelCase__				:					Tuple					=					self.convert_ids_to_tokens(self.suffix_tokens)

			UpperCamelCase__				:					List[Any]					=					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      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
			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(UpperCAmelCase_):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory.')
					return
			UpperCamelCase__				:					str					=					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_):
					copyfile(self.vocab_file  ,					UpperCAmelCase_)

			return (out_vocab_file,)












'''simple docstring'''




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowerCAmelCase__          =						False







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	@property
	def      __UpperCamelCase							(      self   :						int):
			return 12

	@property
	def      __UpperCamelCase							(      self   :						Tuple):
			return 12

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return 32

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					VQModel(
			    block_out_channels=[32, 64]  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,					latent_channels=3  ,					num_vq_embeddings=self.num_embed  ,					vq_embed_dim=3  ,					)
			return model

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Any					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
			return tokenizer

	@property
	def      __UpperCamelCase							(      self   :						List[str]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[int]					=					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=self.text_embedder_hidden_size  ,					intermediate_size=37  ,					layer_norm_eps=1e-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1_000  ,					)
			return CLIPTextModel(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[int]):
			torch.manual_seed(0)

			UpperCamelCase__				:					List[Any]					=					12
			UpperCamelCase__				:					Dict					=					12

			UpperCamelCase__				:					Union[str, Any]					=					{
			    'attention_bias': True,
			    'cross_attention_dim': 32,
			    'attention_head_dim': height * width,
			    'num_attention_heads': 1,
			    'num_vector_embeds': self.num_embed,
			    'num_embeds_ada_norm': self.num_embeds_ada_norm,
			    'norm_num_groups': 32,
			    'sample_size': width,
			    'activation_fn': 'geglu-approximate',
			}

			UpperCamelCase__				:					Tuple					=					TransformeraDModel(**UpperCAmelCase_)
			return model

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					List[Any]					=					'cpu'

			UpperCamelCase__				:					List[str]					=					self.dummy_vqvae
			UpperCamelCase__				:					List[str]					=					self.dummy_text_encoder
			UpperCamelCase__				:					Optional[int]					=					self.dummy_tokenizer
			UpperCamelCase__				:					List[str]					=					self.dummy_transformer
			UpperCamelCase__				:					Dict					=					VQDiffusionScheduler(self.num_embed)
			UpperCamelCase__				:					List[Any]					=					LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					VQDiffusionPipeline(
			    vqvae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					transformer=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					learned_classifier_free_sampling_embeddings=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					Optional[Any]					=					pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'teddy bear playing in the pool'

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe([prompt]  ,					generator=UpperCAmelCase_  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					Optional[Any]					=					output.images

			UpperCamelCase__				:					int					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					num_inference_steps=2)[0]

			UpperCamelCase__				:					Optional[Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Any					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 24, 24, 3)

			UpperCamelCase__				:					Any					=					np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92])

			assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Optional[int]					=					'cpu'

			UpperCamelCase__				:					str					=					self.dummy_vqvae
			UpperCamelCase__				:					Any					=					self.dummy_text_encoder
			UpperCamelCase__				:					List[Any]					=					self.dummy_tokenizer
			UpperCamelCase__				:					Dict					=					self.dummy_transformer
			UpperCamelCase__				:					Optional[Any]					=					VQDiffusionScheduler(self.num_embed)
			UpperCamelCase__				:					Optional[Any]					=					LearnedClassifierFreeSamplingEmbeddings(
			    learnable=UpperCAmelCase_  ,					hidden_size=self.text_embedder_hidden_size  ,					length=tokenizer.model_max_length)

			UpperCamelCase__				:					str					=					VQDiffusionPipeline(
			    vqvae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					transformer=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					learned_classifier_free_sampling_embeddings=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					str					=					pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					'teddy bear playing in the pool'

			UpperCamelCase__				:					Union[str, Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Any					=					pipe([prompt]  ,					generator=UpperCAmelCase_  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					int					=					output.images

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					num_inference_steps=2)[0]

			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Dict					=					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 24, 24, 3)

			UpperCamelCase__				:					str					=					np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88])

			assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







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

	def      __UpperCamelCase							(      self   :						Any):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()





	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[Any]					=					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

			UpperCamelCase__				:					List[Any]					=					VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
			UpperCamelCase__				:					Any					=					pipeline.to(UpperCAmelCase_)
			pipeline.set_progress_bar_config(disable=UpperCAmelCase_)

			# requires GPU generator for gumbel softmax
			# don't use GPU generator in tests though
			UpperCamelCase__				:					Optional[int]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					int					=					pipeline(
			    'teddy bear playing in the pool'  ,					num_images_per_prompt=1  ,					generator=UpperCAmelCase_  ,					output_type='np'  ,					)

			UpperCamelCase__				:					int					=					output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image).max() < 2.0




