

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE  :						List[str]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Dict         =    '''▁'''

_SCREAMING_SNAKE_CASE  :						List[str]         =    {'''vocab_file''': '''prophetnet.tokenizer'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					collections.OrderedDict()
     with open(_A   ,	'''r'''   ,	encoding='''utf-8'''       ) as reader:
          SCREAMING_SNAKE_CASE__					=					reader.readlines()
     for index, token in enumerate(_A       ):
          SCREAMING_SNAKE_CASE__					=					token.rstrip('''\n'''       )
          SCREAMING_SNAKE_CASE__					=					index
     return vocab







class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      ["input_ids", "attention_mask"]






  def __init__(							self		:	Dict			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Any="[SEP]"			,     __lowerCamelCase		:	Optional[Any]="[SEP]"			,     __lowerCamelCase		:	Any="[SEP]"			,     __lowerCamelCase		:	int="[UNK]"			,     __lowerCamelCase		:	str="[PAD]"			,     __lowerCamelCase		:	Optional[Any]="[CLS]"			,     __lowerCamelCase		:	Dict="[MASK]"			,     __lowerCamelCase		:	Optional[Dict[str, Any]] = None			,     **__lowerCamelCase		:	int			,     )	->							None:
       SCREAMING_SNAKE_CASE__					=					{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__lowerCamelCase			,     eos_token=__lowerCamelCase			,     sep_token=__lowerCamelCase			,     unk_token=__lowerCamelCase			,     pad_token=__lowerCamelCase			,     cls_token=__lowerCamelCase			,     mask_token=__lowerCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__lowerCamelCase			,     )

       try:
            import sentencepiece as spm
       except ImportError:
            logger.warning(
                '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                ''' pip install sentencepiece'''      )
            raise

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(str(__lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					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'

       # put special tokens and [unused] tokens into the vocab
       SCREAMING_SNAKE_CASE__					=					{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

       for i in range(10      ):
            SCREAMING_SNAKE_CASE__					=					f'''[unused{i}]'''
            SCREAMING_SNAKE_CASE__					=					5 + i

       # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
       SCREAMING_SNAKE_CASE__					=					12
       SCREAMING_SNAKE_CASE__					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
       for k in self.fairseq_tokens_to_ids.keys():
            self.unique_no_split_tokens.append(__lowerCamelCase      )






  def __getstate__(							self		:	Optional[Any]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.__dict__.copy()
       SCREAMING_SNAKE_CASE__					=					None
       return state






  def __setstate__(							self		:	List[Any]			,     __lowerCamelCase		:	List[str]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					d
       try:
            import sentencepiece as spm
       except ImportError:
            logger.warning(
                '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                ''' pip install sentencepiece'''      )
            raise

       # for backward compatibility
       if not hasattr(self			,     '''sp_model_kwargs'''      ):
            SCREAMING_SNAKE_CASE__					=					{}

       SCREAMING_SNAKE_CASE__					=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None			,     __lowerCamelCase		:	bool = False      )	->							List[int]:

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

       if token_ids_a is None:
            return ([0] * len(__lowerCamelCase      )) + [1]
       return ([0] * len(__lowerCamelCase      )) + [1] + ([0] * len(__lowerCamelCase      )) + [1]






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]

       if token_ids_a is None:
            return len(token_ids_a + sep      ) * [0]
       return len(token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






  @property
  def    lowercase_	(							self		:	int      )	->							Optional[int]:
       return len(self.sp_model      ) + self.fairseq_offset






  def    lowercase_	(							self		:	Any      )	->							int:
       SCREAMING_SNAKE_CASE__					=					{self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	str      )	->							str:
       return self.sp_model.encode(__lowerCamelCase			,     out_type=__lowerCamelCase      )






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	Optional[int]      )	->							Any:
       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       SCREAMING_SNAKE_CASE__					=					self.sp_model.PieceToId(__lowerCamelCase      )

       # 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    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	int      )	->							Tuple:
       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    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	int      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					''''''.join(__lowerCamelCase      ).replace(__lowerCamelCase			,     ''' '''      ).strip()
       return out_string






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       if not os.path.isdir(__lowerCamelCase      ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
            return
       SCREAMING_SNAKE_CASE__					=					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      ) and os.path.isfile(self.vocab_file      ):
            copyfile(self.vocab_file			,     __lowerCamelCase      )
       elif not os.path.isfile(self.vocab_file      ):
            with open(__lowerCamelCase			,     '''wb'''      ) as fi:
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.serialized_model_proto()
                 fi.write(__lowerCamelCase      )

       return (out_vocab_file,)







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

       if token_ids_a is None:
            return token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE__					=					[self.sep_token_id]
       return token_ids_a + sep + token_ids_a + sep





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  UpperCAmelCase__	( A__		,  A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      AltDiffusionPipeline
  a     =      TEXT_TO_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_BATCH_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS






  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(
           block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,     cross_attention_dim=32			,     )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     clip_sample=__lowerCamelCase			,     set_alpha_to_one=__lowerCamelCase			,     )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					AutoencoderKL(
           block_out_channels=[32, 64]			,     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      )
       SCREAMING_SNAKE_CASE__					=					CLIPTextConfig(
           bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     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=5002			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
       SCREAMING_SNAKE_CASE__					=					77

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






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[int]=0      )	->							Union[str, Any]:
       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''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    lowercase_	(							self		:	List[Any]      )	->							str:
       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )






  def    lowercase_	(							self		:	List[Any]      )	->							int:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3      )






  def    lowercase_	(							self		:	Dict      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       # TODO: remove after fixing the non-deterministic text encoder
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesModelWithTransformation(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder

       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					'''A photo of an astronaut'''
       SCREAMING_SNAKE_CASE__					=					alt_pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]      )

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







  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(skip_prk_steps=__lowerCamelCase      )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       # TODO: remove after fixing the non-deterministic text encoder
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesModelWithTransformation(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder
       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]      )

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






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






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							str:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	str      )	->							Any:
       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe([prompt]			,     generator=__lowerCamelCase			,     guidance_scale=6.0			,     num_inference_steps=20			,     output_type='''np'''      )

       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]      )

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







  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''			,     subfolder='''scheduler'''      )

       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					alt_pipe([prompt]			,     generator=__lowerCamelCase			,     num_inference_steps=2			,     output_type='''numpy'''      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]      )

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


