


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     '''▁'''

_snake_case             =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

_snake_case             =     {
    '''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'''
        ),
    }
}

_snake_case             =     {
    '''facebook/mbart-large-en-ro''': 10_24,
    '''facebook/mbart-large-cc25''': 10_24,
}

# fmt: off
_snake_case             =     ['''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_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[Any]     		=		VOCAB_FILES_NAMES
   __A :      List[str]     		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A :      str     		=		PRETRAINED_VOCAB_FILES_MAP
   __A :      Optional[int]     		=		["input_ids", "attention_mask"]

   __A :      List[int]     		=		[]
   __A :      List[int]     		=		[]






   def __init__(		self						,				__A						,				__A="<s>"						,				__A="</s>"						,				__A="</s>"						,				__A="<s>"						,				__A="<unk>"						,				__A="<pad>"						,				__A="<mask>"						,				__A=None						,				__A=None						,				__A=None						,				__A = None						,				__A=None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Dict    =				AddedToken(__A						,				lstrip=__A						,				rstrip=__A ) if isinstance(__A						,				__A ) else mask_token

     lowerCamelCase :					Any    =				{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=__A						,				eos_token=__A						,				unk_token=__A						,				sep_token=__A						,				cls_token=__A						,				pad_token=__A						,				mask_token=__A						,				tokenizer_file=__A						,				src_lang=__A						,				tgt_lang=__A						,				additional_special_tokens=__A						,				sp_model_kwargs=self.sp_model_kwargs						,				**__A						,				)

     lowerCamelCase :					Optional[int]    =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(str(__A ) )
     lowerCamelCase :					Optional[int]    =				vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # Mimic fairseq token-to-id alignment for the first 4 token
     lowerCamelCase :					Dict    =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     lowerCamelCase :					List[str]    =				1

     lowerCamelCase :					int    =				len(self.sp_model )
     lowerCamelCase :					List[Any]    =				{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A )
     }
     lowerCamelCase :					List[str]    =				{v: k for k, v in self.lang_code_to_id.items()}
     lowerCamelCase :					Any    =				len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
     lowerCamelCase :					Optional[int]    =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     lowerCamelCase :					Union[str, Any]    =				list(self.lang_code_to_id.keys() )

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

     lowerCamelCase :					Dict    =				src_lang if src_lang is not None else "en_XX"
     lowerCamelCase :					str    =				self.lang_code_to_id[self._src_lang]
     lowerCamelCase :					Dict    =				tgt_lang
     self.set_src_lang_special_tokens(self._src_lang )






   def __getstate__(		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				self.__dict__.copy()
     lowerCamelCase :					Tuple    =				None
     lowerCamelCase :					int    =				self.sp_model.serialized_model_proto()
     return state






   def __setstate__(		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				d

     # for backward compatibility
     if not hasattr(self						,				"sp_model_kwargs" ):
       lowerCamelCase :					str    =				{}

     lowerCamelCase :					List[Any]    =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return self._src_lang






   @src_lang.setter
   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					int    =				new_src_lang
     self.set_src_lang_special_tokens(self._src_lang )






   def _snake_case    (		self						,				__A						,				__A = None						,				__A = False ):






     """simple docstring"""





     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__A						,				token_ids_a=__A						,				already_has_special_tokens=__A )

     lowerCamelCase :					Optional[Any]    =				[1] * len(self.prefix_tokens )
     lowerCamelCase :					Dict    =				[1] * len(self.suffix_tokens )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(__A )) + suffix_ones
     return prefix_ones + ([0] * len(__A )) + ([0] * len(__A )) + suffix_ones






   def _snake_case    (		self						,				__A						,				__A = 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						,				__A						,				__A = None ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				[self.sep_token_id]
     lowerCamelCase :					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 _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """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 :					str    =				src_lang
     lowerCamelCase :					List[Any]    =				self(__A						,				add_special_tokens=__A						,				return_tensors=__A						,				**__A )
     lowerCamelCase :					Tuple    =				self.convert_tokens_to_ids(__A )
     lowerCamelCase :					Optional[Any]    =				tgt_lang_id
     return inputs






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				{self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.sp_model.encode(__A						,				out_type=__A )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     lowerCamelCase :					Optional[int]    =				self.sp_model.PieceToId(__A )

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				"".join(__A ).replace(__A						,				" " ).strip()
     return out_string






   def _snake_case    (		self						,				__A						,				__A = None ):






     """simple docstring"""





     if not os.path.isdir(__A ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
       return
     lowerCamelCase :					Union[str, Any]    =				os.path.join(
         __A						,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
       copyfile(self.vocab_file						,				__A )
     elif not os.path.isfile(self.vocab_file ):
       with open(__A						,				"wb" ) as fi:
         lowerCamelCase :					Optional[Any]    =				self.sp_model.serialized_model_proto()
         fi.write(__A )

     return (out_vocab_file,)






   def _snake_case    (		self						,				__A						,				__A = "en_XX"						,				__A = None						,				__A = "ro_RO"						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Any    =				src_lang
     lowerCamelCase :					str    =				tgt_lang
     return super().prepare_seqaseq_batch(__A						,				__A						,				**__A )






   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						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.lang_code_to_id[src_lang]
     lowerCamelCase :					List[str]    =				[]
     lowerCamelCase :					Optional[int]    =				[self.eos_token_id, self.cur_lang_code]






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				self.lang_code_to_id[lang]
     lowerCamelCase :					int    =				[]
     lowerCamelCase :					Optional[Any]    =				[self.eos_token_id, self.cur_lang_code]







import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 	UpperCAmelCase_					(					UpperCamelCase		, UpperCamelCase		, UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		StableUnCLIPPipeline
   __A :      Optional[int]     		=		TEXT_TO_IMAGE_PARAMS
   __A :      str     		=		TEXT_TO_IMAGE_BATCH_PARAMS
   __A :      int     		=		TEXT_TO_IMAGE_IMAGE_PARAMS
   __A :      Tuple     		=		TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   __A :      Union[str, Any]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				32
     lowerCamelCase :					Dict    =				embedder_hidden_size

     # prior components

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				CLIPTextModelWithProjection(
         CLIPTextConfig(
             bos_token_id=0						,				eos_token_id=2						,				hidden_size=__A						,				projection_dim=__A						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				) )

     torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				PriorTransformer(
         num_attention_heads=2						,				attention_head_dim=12						,				embedding_dim=__A						,				num_layers=1						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					Dict    =				DDPMScheduler(
         variance_type="fixed_small_log"						,				prediction_type="sample"						,				num_train_timesteps=1000						,				clip_sample=__A						,				clip_sample_range=5.0						,				beta_schedule="squaredcos_cap_v2"						,				)

     # regular denoising components

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				StableUnCLIPImageNormalizer(embedding_dim=__A )
     lowerCamelCase :					Tuple    =				DDPMScheduler(beta_schedule="squaredcos_cap_v2" )

     torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

     torch.manual_seed(0 )
     lowerCamelCase :					str    =				CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0						,				eos_token_id=2						,				hidden_size=__A						,				projection_dim=32						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				) )

     torch.manual_seed(0 )
     lowerCamelCase :					Any    =				UNetaDConditionModel(
         sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")						,				up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")						,				block_out_channels=(32, 64)						,				attention_head_dim=(2, 4)						,				class_embed_type="projection"						,				projection_class_embeddings_input_dim=embedder_projection_dim * 2						,				cross_attention_dim=__A						,				layers_per_block=1						,				upcast_attention=__A						,				use_linear_projection=__A						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					int    =				DDIMScheduler(
         beta_schedule="scaled_linear"						,				beta_start=0.00085						,				beta_end=0.012						,				prediction_type="v_prediction"						,				set_alpha_to_one=__A						,				steps_offset=1						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[Any]    =				AutoencoderKL()

     lowerCamelCase :					Optional[int]    =				{
         # prior components
         "prior_tokenizer": prior_tokenizer,
         "prior_text_encoder": prior_text_encoder,
         "prior": prior,
         "prior_scheduler": prior_scheduler,
         # image noising components
         "image_normalizer": image_normalizer,
         "image_noising_scheduler": image_noising_scheduler,
         # regular denoising components
         "tokenizer": tokenizer,
         "text_encoder": text_encoder,
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
     }

     return components






   def _snake_case    (		self						,				__A						,				__A=0 ):






     """simple docstring"""





     if str(__A ).startswith("mps" ):
       lowerCamelCase :					Optional[int]    =				torch.manual_seed(__A )
     else:
       lowerCamelCase :					Optional[Any]    =				torch.Generator(device=__A ).manual_seed(__A )
     lowerCamelCase :					Tuple    =				{
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "prior_num_inference_steps": 2,
         "output_type": "numpy",
     }
     return inputs






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				torch_device == "cpu"

     self._test_attention_slicing_forward_pass(test_max_difference=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				torch_device in ["cpu", "mps"]

     self._test_inference_batch_single_identical(test_max_difference=__A )



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

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )

     lowerCamelCase :					str    =				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa )
     pipe.to(__A )
     pipe.set_progress_bar_config(disable=__A )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     lowerCamelCase :					List[Any]    =				torch.Generator(device="cpu" ).manual_seed(0 )
     lowerCamelCase :					Dict    =				pipe("anime turle"						,				generator=__A						,				output_type="np" )

     lowerCamelCase :					Dict    =				output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(__A						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     lowerCamelCase :					int    =				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa )
     lowerCamelCase :					Union[str, Any]    =				pipe.to(__A )
     pipe.set_progress_bar_config(disable=__A )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     lowerCamelCase :					Any    =				pipe(
         "anime turtle"						,				prior_num_inference_steps=2						,				num_inference_steps=2						,				output_type="np"						,				)

     lowerCamelCase :					List[str]    =				torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



