






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


A__	:			str			   = logging.get_logger(__name__)

A__	:			Union[str, Any]			   = '▁'

A__	:			Dict			   = {'vocab_file': 'prophetnet.tokenizer'}

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

A__	:			Union[str, Any]			   = {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

A__	:			Any			   = {
    'microsoft/xprophetnet-large-wiki100-cased': 5_12,
}






def   a			(						lowerCamelCase_				):






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




class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




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




				def __init__(     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    str="[SEP]",  lowerCamelCase						:    Dict="[SEP]",  lowerCamelCase						:    Optional[int]="[SEP]",  lowerCamelCase						:    List[str]="[UNK]",  lowerCamelCase						:    Optional[int]="[PAD]",  lowerCamelCase						:    Optional[int]="[CLS]",  lowerCamelCase						:    Union[str, Any]="[MASK]",  lowerCamelCase						:    Optional[Dict[str, Any]] = None,  **lowerCamelCase						:    Tuple,  ):

								'''simple docstring'''
								lowercase__    =			{} 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

								lowercase__    =			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(lowerCamelCase  )  )
								lowercase__    =			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
								lowercase__    =			{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

								for i in range(10  ):
												lowercase__    =			F"""[unused{i}]"""
												lowercase__    =			5 + i

								# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
								lowercase__    =			12
								lowercase__    =			{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						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.__dict__.copy()
								lowercase__    =			None
								return state




				def __setstate__(     self						:    Optional[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			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'''  ):
												lowercase__    =			{}

								lowercase__    =			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  ):

								'''simple docstring'''

								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						:    List[str],  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''simple docstring'''
								lowercase__    =			[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						:    str  ):

								'''simple docstring'''
								return len(self.sp_model  ) + self.fairseq_offset




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			{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[Any],  lowerCamelCase						:    str  ):

								'''simple docstring'''
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase  )




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								lowercase__    =			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						:    Optional[Any],  lowerCamelCase						:    int  ):

								'''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 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			''''''.join(lowerCamelCase  ).replace(lowerCamelCase,  ''' '''  ).strip()
								return out_string




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[str] = None  ):

								'''simple docstring'''
								if not os.path.isdir(lowerCamelCase  ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
												return
								lowercase__    =			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:
																lowercase__    =			self.sp_model.serialized_model_proto()
																fi.write(lowerCamelCase  )

								return (out_vocab_file,)




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''simple docstring'''

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












import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       TextToVideoSDPipeline
				lowercase__											=       TEXT_TO_IMAGE_PARAMS
				lowercase__											=       TEXT_TO_IMAGE_BATCH_PARAMS
				# No `output_type`.
				lowercase__											=       frozenset(
				    [
				        """num_inference_steps""",
				        """generator""",
				        """latents""",
				        """return_dict""",
				        """callback""",
				        """callback_steps""",
				    ]	)




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			UNetaDConditionModel(
								    block_out_channels=(32, 64, 64, 64),  layers_per_block=2,  sample_size=32,  in_channels=4,  out_channels=4,  down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D'''),  up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D'''),  cross_attention_dim=32,  attention_head_dim=4,  )
								lowercase__    =			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  )
								lowercase__    =			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,  sample_size=128,  )
								torch.manual_seed(0  )
								lowercase__    =			CLIPTextConfig(
								    bos_token_id=0,  eos_token_id=2,  hidden_size=32,  intermediate_size=37,  layer_norm_eps=1E-05,  num_attention_heads=4,  num_hidden_layers=5,  pad_token_id=1,  vocab_size=1_000,  hidden_act='''gelu''',  projection_dim=512,  )
								lowercase__    =			CLIPTextModel(lowerCamelCase  )
								lowercase__    =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__    =			{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								}
								return components




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    int=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__    =			torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowercase__    =			{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''pt''',
								}
								return inputs




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			TextToVideoSDPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			'''np'''
								lowercase__    =			sd_pipe(**lowerCamelCase  ).frames
								lowercase__    =			frames[0][-3:, -3:, -1]

								assert frames[0].shape == (64, 64, 3)
								lowercase__    =			np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]  )

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




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase,  expected_max_diff=3E-3  )




				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available(),  reason='''XFormers attention is only available with CUDA and `xformers` installed''',  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase,  expected_max_diff=1E-2  )




				@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''  )
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''  )
				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''  )
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								return super().test_progress_bar()



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

				"""simple docstring"""




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'''  )

								lowercase__    =			TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''  )
								lowercase__    =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								lowercase__    =			pipe.to('''cuda'''  )

								lowercase__    =			'''Spiderman is surfing'''
								lowercase__    =			torch.Generator(device='''cpu'''  ).manual_seed(0  )

								lowercase__    =			pipe(lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=25,  output_type='''pt'''  ).frames
								lowercase__    =			video_frames.cpu().numpy()

								assert np.abs(expected_video - video  ).mean() < 5E-2




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'''  )

								lowercase__    =			TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b'''  )
								lowercase__    =			pipe.to('''cuda'''  )

								lowercase__    =			'''Spiderman is surfing'''
								lowercase__    =			torch.Generator(device='''cpu'''  ).manual_seed(0  )

								lowercase__    =			pipe(lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=2,  output_type='''pt'''  ).frames
								lowercase__    =			video_frames.cpu().numpy()

								assert np.abs(expected_video - video  ).mean() < 5E-2




