





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


_lowerCAmelCase							:					str		    =		logging.get_logger(__name__)

_lowerCAmelCase							:					Union[str, Any]		    =		'▁'

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

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

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

_lowerCAmelCase							:					Any		    =		{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}

def    __snake_case  (    _lowerCAmelCase       :       List[Any]		)       ->  str:
   A_    : Optional[int]								=	collections.OrderedDict()
   with open(lowerCamelCase_ , "r" , encoding="utf-8"		) as reader:
      A_    : Union[str, Any]								=	reader.readlines()
   for index, token in enumerate(lowerCamelCase_		):
      A_    : Any								=	token.rstrip("\n"		)
      A_    : List[Any]								=	index
   return vocab





class     __magic_name__     (							A__							):





     """simple docstring"""



     __UpperCamelCase    			=   VOCAB_FILES_NAMES
     __UpperCamelCase    			=   PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase    			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase    			=   ['''input_ids''', '''attention_mask''']



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





        '''simple docstring'''




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

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

        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

        A_    : Optional[int]								=	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(str(snake_case     )     )
        A_    : Dict								=	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
        A_    : str								=	{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

        for i in range(10     ):
           A_    : Tuple								=	f"[unused{i}]"
           A_    : Dict								=	5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        A_    : str								=	12
        A_    : Dict								=	{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(snake_case     )



     def __getstate__(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : List[str]								=	self.__dict__.copy()
        A_    : int								=	None
        return state



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





        '''simple docstring'''




        A_    : Union[str, Any]								=	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"     ):
           A_    : List[Any]								=	{}

        A_    : Optional[Any]								=	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(self.vocab_file     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:List[int]	,					snake_case				:Optional[List[int]] = None	,					snake_case				:bool = False     ):





        '''simple docstring'''




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

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



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:List[int]	,					snake_case				:Optional[List[int]] = None     ):





        '''simple docstring'''




        A_    : Tuple								=	[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   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




        return len(self.sp_model     ) + self.fairseq_offset



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]	,					snake_case				:str     ):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:int	,					snake_case				:Any     ):





        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        A_    : Tuple								=	self.sp_model.PieceToId(snake_case     )

        # 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   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				: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   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:Dict     ):





        '''simple docstring'''




        A_    : int								=	"".join(snake_case     ).replace(snake_case	,					" "     ).strip()
        return out_string



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]	,					snake_case				:str	,					snake_case				:Optional[str] = None     ):





        '''simple docstring'''




        if not os.path.isdir(snake_case     ):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
           return
        A_    : Optional[Any]								=	os.path.join(
            snake_case	,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

        return (out_vocab_file,)





     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:List[int]	,					snake_case				:Optional[List[int]] = None     ):





        '''simple docstring'''




        if token_ids_a is None:
           return token_ids_a + [self.sep_token_id]
        A_    : Union[str, Any]								=	[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




