





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:
						UpperCamelCase_									=None


UpperCamelCase_									=logging.get_logger(__name__)


UpperCamelCase_									={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_									={
    """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""",
    },
}

UpperCamelCase_									={
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
UpperCamelCase_									=["""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 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   ['''input_ids''', '''attention_mask''']
				UpperCamelCase								=   MBartTokenizer

				UpperCamelCase								=   []
				UpperCamelCase								=   []






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




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   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__,			)

										_UpperCamelCase      : Optional[Any]         =   vocab_file
										_UpperCamelCase      : Tuple         =   False if not self.vocab_file else True

										_UpperCamelCase      : Tuple         =   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[int]         =   {
										    lang_code: self.convert_tokens_to_ids(lowerCAmelCase__							) 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      : Tuple         =   tgt_lang
										self.set_src_lang_special_tokens(self._src_lang							)






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				str:




										'''simple docstring'''



										return self._src_lang






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




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   new_src_lang
										self.set_src_lang_special_tokens(self._src_lang							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   [self.sep_token_id]
										_UpperCamelCase      : Union[str, Any]         =   [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		:   int,			lowerCAmelCase__		:   Tuple,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str],			lowerCAmelCase__		:   Optional[str],			**lowerCAmelCase__		:   Optional[int]							)     ->				Any:




										'''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'''							)
										_UpperCamelCase      : Tuple         =   src_lang
										_UpperCamelCase      : Union[str, Any]         =   self(lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   self.convert_tokens_to_ids(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   tgt_lang_id
										return inputs






				def     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   str = "en_XX",			lowerCAmelCase__		:   Optional[List[str]] = None,			lowerCAmelCase__		:   str = "ro_RO",			**lowerCAmelCase__		:   Any,			)     ->				BatchEncoding:




										'''simple docstring'''



										_UpperCamelCase      : int         =   src_lang
										_UpperCamelCase      : Tuple         =   tgt_lang
										return super().prepare_seqaseq_batch(lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							)






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




										'''simple docstring'''



										return self.set_src_lang_special_tokens(self.src_lang							)






				def     snake_case     (			self		:   List[Any]							)     ->				str:




										'''simple docstring'''



										return self.set_tgt_lang_special_tokens(self.tgt_lang							)






				def     snake_case     (			self		:   Optional[int],			lowerCAmelCase__		:   Any							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : str         =   self.convert_tokens_to_ids(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   []
										_UpperCamelCase      : int         =   [self.eos_token_id, self.cur_lang_code]

										_UpperCamelCase      : Any         =   self.convert_ids_to_tokens(self.prefix_tokens							)
										_UpperCamelCase      : Dict         =   self.convert_ids_to_tokens(self.suffix_tokens							)

										_UpperCamelCase      : 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		:   Optional[int],			lowerCAmelCase__		:   str							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.convert_tokens_to_ids(lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   []
										_UpperCamelCase      : str         =   [self.eos_token_id, self.cur_lang_code]

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

										_UpperCamelCase      : List[str]         =   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		:   List[Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str] = None							)     ->				Tuple[str]:




										'''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
										_UpperCamelCase      : Optional[Any]         =   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,)










"""simple docstring"""




import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			_a						(				_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   AltDiffusionPipeline
				UpperCamelCase								=   TEXT_TO_IMAGE_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCamelCase								=   TEXT_TO_IMAGE_IMAGE_PARAMS






				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4),			layers_per_block=2,			sample_size=3_2,			in_channels=4,			out_channels=4,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),			cross_attention_dim=3_2,			)
										_UpperCamelCase      : Union[str, Any]         =   DDIMScheduler(
										    beta_start=0.00_085,			beta_end=0.012,			beta_schedule='''scaled_linear''',			clip_sample=lowerCAmelCase__,			set_alpha_to_one=lowerCAmelCase__,			)
										torch.manual_seed(0							)
										_UpperCamelCase      : List[str]         =   AutoencoderKL(
										    block_out_channels=[3_2, 6_4],			in_channels=3,			out_channels=3,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],			latent_channels=4,			)

										# TODO: address the non-deterministic text encoder (fails for save-load tests)
										# torch.manual_seed(0)
										# text_encoder_config = RobertaSeriesConfig(
										#     hidden_size=32,
										#     project_dim=32,
										#     intermediate_size=37,
										#     layer_norm_eps=1e-05,
										#     num_attention_heads=4,
										#     num_hidden_layers=5,
										#     vocab_size=5002,
										# )
										# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

										torch.manual_seed(0							)
										_UpperCamelCase      : str         =   CLIPTextConfig(
										    bos_token_id=0,			eos_token_id=2,			hidden_size=3_2,			projection_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=5_0_0_2,			)
										_UpperCamelCase      : List[Any]         =   CLIPTextModel(lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
										_UpperCamelCase      : str         =   7_7

										_UpperCamelCase      : int         =   {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   int=0							)     ->				Optional[int]:




										'''simple docstring'''



										if str(lowerCAmelCase__							).startswith('''mps'''							):
																_UpperCamelCase      : Any         =   torch.manual_seed(lowerCAmelCase__							)
										else:
																_UpperCamelCase      : Dict         =   torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs






				def     snake_case     (			self		:   List[Any]							)     ->				List[str]:




										'''simple docstring'''



										super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)






				def     snake_case     (			self		:   List[Any]							)     ->				Tuple:




										'''simple docstring'''



										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)






				def     snake_case     (			self		:   List[str]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										_UpperCamelCase      : int         =   self.get_dummy_components()
										torch.manual_seed(0							)
										_UpperCamelCase      : Any         =   RobertaSeriesConfig(
										    hidden_size=3_2,			project_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=5_0_0_2,			)
										# TODO: remove after fixing the non-deterministic text encoder
										_UpperCamelCase      : Tuple         =   RobertaSeriesModelWithTransformation(lowerCAmelCase__							)
										_UpperCamelCase      : str         =   text_encoder

										_UpperCamelCase      : List[Any]         =   AltDiffusionPipeline(**lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   self.get_dummy_inputs(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[Any]         =   '''A photo of an astronaut'''
										_UpperCamelCase      : Any         =   alt_pipe(**lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   output.images
										_UpperCamelCase      : Union[str, Any]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]							)

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






				def     snake_case     (			self		:   int							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

										_UpperCamelCase      : Optional[Any]         =   self.get_dummy_components()
										_UpperCamelCase      : str         =   PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										torch.manual_seed(0							)
										_UpperCamelCase      : int         =   RobertaSeriesConfig(
										    hidden_size=3_2,			project_dim=3_2,			intermediate_size=3_7,			layer_norm_eps=1e-0_5,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=5_0_0_2,			)
										# TODO: remove after fixing the non-deterministic text encoder
										_UpperCamelCase      : Tuple         =   RobertaSeriesModelWithTransformation(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   text_encoder
										_UpperCamelCase      : str         =   AltDiffusionPipeline(**lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Union[str, Any]         =   self.get_dummy_inputs(lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   alt_pipe(**lowerCAmelCase__							)
										_UpperCamelCase      : List[str]         =   output.images
										_UpperCamelCase      : Optional[int]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase      : List[Any]         =   np.array(
										    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]							)

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




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






				def     snake_case     (			self		:   List[str]							)     ->				Any:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : str         =   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''',			safety_checker=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : int         =   '''A painting of a squirrel eating a burger'''
										_UpperCamelCase      : int         =   torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   alt_pipe([prompt],			generator=lowerCAmelCase__,			guidance_scale=6.0,			num_inference_steps=2_0,			output_type='''np'''							)

										_UpperCamelCase      : Optional[int]         =   output.images

										_UpperCamelCase      : Optional[int]         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : List[str]         =   np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]							)

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






				def     snake_case     (			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''',			subfolder='''scheduler'''							)

										_UpperCamelCase      : Dict         =   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''',			scheduler=lowerCAmelCase__,			safety_checker=lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   alt_pipe.to(lowerCAmelCase__							)
										alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   '''A painting of a squirrel eating a burger'''
										_UpperCamelCase      : Optional[Any]         =   torch.manual_seed(0							)

										_UpperCamelCase      : Union[str, Any]         =   alt_pipe([prompt],			generator=lowerCAmelCase__,			num_inference_steps=2,			output_type='''numpy'''							)
										_UpperCamelCase      : Tuple         =   output.images

										_UpperCamelCase      : Dict         =   image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : str         =   np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]							)

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



