


import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


UpperCAmelCase_      : Dict  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[str]        =						'''AutoTokenizer'''
      snake_case__				:     Optional[Any]        =						['''tokenizer''']

      snake_case__				:     int        =						{
          '''semantic_prompt''': 1,
          '''coarse_prompt''': 2,
          '''fine_prompt''': 2,
      }
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any=None     )		->			Optional[Any]:
             super().__init__(SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  speaker_embeddings
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Tuple="speaker_embeddings_path.json"     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[Any]:

             if speaker_embeddings_dict_path is not None:
                    a_					:		int							=  get_file_from_repo(
                        SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							subfolder=kwargs.pop('subfolder'     ,							SCREAMING_SNAKE_CASE__     )     ,							cache_dir=kwargs.pop('cache_dir'     ,							SCREAMING_SNAKE_CASE__     )     ,							force_download=kwargs.pop('force_download'     ,							SCREAMING_SNAKE_CASE__     )     ,							proxies=kwargs.pop('proxies'     ,							SCREAMING_SNAKE_CASE__     )     ,							resume_download=kwargs.pop('resume_download'     ,							SCREAMING_SNAKE_CASE__     )     ,							local_files_only=kwargs.pop('local_files_only'     ,							SCREAMING_SNAKE_CASE__     )     ,							use_auth_token=kwargs.pop('use_auth_token'     ,							SCREAMING_SNAKE_CASE__     )     ,							revision=kwargs.pop('revision'     ,							SCREAMING_SNAKE_CASE__     )     ,							)
                    if speaker_embeddings_path is None:
                           logger.warning(
                               F"""`{os.path.join(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""     )
                           a_					:		Any							=  None
                    else:
                           with open(SCREAMING_SNAKE_CASE__     ) as speaker_embeddings_json:
                                  a_					:		int							=  json.load(SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		str							=  None

             a_					:		List[str]							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             return cls(tokenizer=SCREAMING_SNAKE_CASE__     ,							speaker_embeddings=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]="speaker_embeddings_path.json"     ,							SCREAMING_SNAKE_CASE__				:       Dict="speaker_embeddings"     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			Union[str, Any]:
             if self.speaker_embeddings is not None:
                    os.makedirs(os.path.join(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							'v2'     )     ,							exist_ok=SCREAMING_SNAKE_CASE__     )

                    a_					:		Union[str, Any]							=  {}

                    a_					:		List[str]							=  save_directory

                    for prompt_key in self.speaker_embeddings:
                           if prompt_key != "repo_or_path":
                                  a_					:		Tuple							=  self._load_voice_preset(SCREAMING_SNAKE_CASE__     )

                                  a_					:		Optional[int]							=  {}
                                  for key in self.speaker_embeddings[prompt_key]:
                                         np.save(
                                             os.path.join(
                                                 embeddings_dict['repo_or_path']     ,							SCREAMING_SNAKE_CASE__     ,							F"""{prompt_key}_{key}"""     )     ,							voice_preset[key]     ,							allow_pickle=SCREAMING_SNAKE_CASE__     ,							)
                                         a_					:		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__     ,							F"""{prompt_key}_{key}.npy"""     )

                                  a_					:		int							=  tmp_dict

                    with open(os.path.join(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )     ,							'w'     ) as fp:
                           json.dump(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             super().save_pretrained(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       str = None     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			int:
             a_					:		Union[str, Any]							=  self.speaker_embeddings[voice_preset]

             a_					:		str							=  {}
             for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                    if key not in voice_preset_paths:
                           raise ValueError(
                               F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""     )

                    a_					:		Dict							=  get_file_from_repo(
                        self.speaker_embeddings.get('repo_or_path'     ,							'/'     )     ,							voice_preset_paths[key]     ,							subfolder=kwargs.pop('subfolder'     ,							SCREAMING_SNAKE_CASE__     )     ,							cache_dir=kwargs.pop('cache_dir'     ,							SCREAMING_SNAKE_CASE__     )     ,							force_download=kwargs.pop('force_download'     ,							SCREAMING_SNAKE_CASE__     )     ,							proxies=kwargs.pop('proxies'     ,							SCREAMING_SNAKE_CASE__     )     ,							resume_download=kwargs.pop('resume_download'     ,							SCREAMING_SNAKE_CASE__     )     ,							local_files_only=kwargs.pop('local_files_only'     ,							SCREAMING_SNAKE_CASE__     )     ,							use_auth_token=kwargs.pop('use_auth_token'     ,							SCREAMING_SNAKE_CASE__     )     ,							revision=kwargs.pop('revision'     ,							SCREAMING_SNAKE_CASE__     )     ,							)
                    if path is None:
                           raise ValueError(
                               F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"     ,							"/"     )     ,							voice_preset_paths[key]     )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""     )

                    a_					:		str							=  np.load(SCREAMING_SNAKE_CASE__     )

             return voice_preset_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[dict] = None     )		->			int:
             for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                    if key not in voice_preset:
                           raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""     )

                    if not isinstance(voice_preset[key]     ,							np.ndarray     ):
                           raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray."""     )

                    if len(voice_preset[key].shape     ) != self.preset_shape[key]:
                           raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray."""     )

      def __call__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]="pt"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2_5_6     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Tuple=True     ,							SCREAMING_SNAKE_CASE__				:       Any=False     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			List[str]:
             if voice_preset is not None and not isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    if (
                        isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                        and self.speaker_embeddings is not None
                        and voice_preset in self.speaker_embeddings
                    ):
                           a_					:		Optional[int]							=  self._load_voice_preset(SCREAMING_SNAKE_CASE__     )

                    else:
                           if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) and not voice_preset.endswith('.npz'     ):
                                  a_					:		Optional[Any]							=  voice_preset + '.npz'

                           a_					:		Any							=  np.load(SCREAMING_SNAKE_CASE__     )

             if voice_preset is not None:
                    self._validate_voice_preset_dict(SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
                    a_					:		Dict							=  BatchFeature(data=SCREAMING_SNAKE_CASE__     ,							tensor_type=SCREAMING_SNAKE_CASE__     )

             a_					:		List[Any]							=  self.tokenizer(
                 SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							padding='max_length'     ,							max_length=SCREAMING_SNAKE_CASE__     ,							return_attention_mask=SCREAMING_SNAKE_CASE__     ,							return_token_type_ids=SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

             if voice_preset is not None:
                    a_					:		Optional[int]							=  voice_preset

             return encoded_text






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  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Optional[Any]        =						TextToVideoSDPipeline
      snake_case__				:     Optional[int]        =						TEXT_TO_IMAGE_PARAMS
      snake_case__				:     str        =						TEXT_TO_IMAGE_BATCH_PARAMS
      # No `output_type`.
      snake_case__				:     Optional[Any]        =						frozenset(
          [
              '''num_inference_steps''',
              '''generator''',
              '''latents''',
              '''return_dict''',
              '''callback''',
              '''callback_steps''',
          ]			)
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Union[str, Any]:
             torch.manual_seed(0     )
             a_					:		Optional[int]							=  UNetaDConditionModel(
                 block_out_channels=(3_2, 6_4, 6_4, 6_4)     ,							layers_per_block=2     ,							sample_size=3_2     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')     ,							up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')     ,							cross_attention_dim=3_2     ,							attention_head_dim=4     ,							)
             a_					:		int							=  DDIMScheduler(
                 beta_start=0.00085     ,							beta_end=0.012     ,							beta_schedule='scaled_linear'     ,							clip_sample=SCREAMING_SNAKE_CASE__     ,							set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,							)
             torch.manual_seed(0     )
             a_					:		int							=  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     ,							sample_size=1_2_8     ,							)
             torch.manual_seed(0     )
             a_					:		Optional[int]							=  CLIPTextConfig(
                 bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=3_2     ,							intermediate_size=3_7     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=1_0_0_0     ,							hidden_act='gelu'     ,							projection_dim=5_1_2     ,							)
             a_					:		Dict							=  CLIPTextModel(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

             a_					:		Union[str, Any]							=  {
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
             }
             return components
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0     )		->			List[str]:
             if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                    a_					:		Dict							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		Optional[Any]							=  torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  {
                 '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        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             a_					:		List[Any]							=  'cpu'  # ensure determinism for the device-dependent torch.Generator
             a_					:		Dict							=  self.get_dummy_components()
             a_					:		str							=  TextToVideoSDPipeline(**SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  sd_pipe.to(SCREAMING_SNAKE_CASE__     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  'np'
             a_					:		Dict							=  sd_pipe(**SCREAMING_SNAKE_CASE__     ).frames
             a_					:		int							=  frames[0][-3:, -3:, -1]

             assert frames[0].shape == (6_4, 6_4, 3)
             a_					:		Union[str, Any]							=  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        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[Any]:
             self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__     ,							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        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__     ,							expected_max_diff=1E-2     )
      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Union[str, Any]:
             pass
      @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[str]:
             pass
      @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			List[Any]:
             pass

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Union[str, Any]:
             return super().test_progress_bar()
@slow
@skip_mps
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			List[Any]:
             a_					:		str							=  load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy'     )

             a_					:		Any							=  TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b'     )
             a_					:		Optional[Any]							=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
             a_					:		Optional[Any]							=  pipe.to('cuda'     )

             a_					:		Any							=  'Spiderman is surfing'
             a_					:		List[Any]							=  torch.Generator(device='cpu'     ).manual_seed(0     )

             a_					:		Optional[Any]							=  pipe(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							num_inference_steps=2_5     ,							output_type='pt'     ).frames
             a_					:		str							=  video_frames.cpu().numpy()

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

      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Any:
             a_					:		Dict							=  load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy'     )

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

             a_					:		Any							=  'Spiderman is surfing'
             a_					:		List[str]							=  torch.Generator(device='cpu'     ).manual_seed(0     )

             a_					:		List[Any]							=  pipe(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ,							num_inference_steps=2     ,							output_type='pt'     ).frames
             a_					:		List[str]							=  video_frames.cpu().numpy()

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


