




'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class    __lowercase						(  __magic_name__ ):
   _a							=      """Wav2Vec2FeatureExtractor"""
   _a							=      """AutoTokenizer"""







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Any:
     super().__init__(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					self.feature_extractor
     __a			=					False







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Optional[int]:
     try:
       return super().from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )
     except OSError:
       warnings.warn(
           f"Loading a tokenizer inside {cls.__name__} from a config that does not"
           ' include a `tokenizer_class` attribute is deprecated and will be '
           'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
           ' attribute to either your `config.json` or `tokenizer_config.json` '
           'file to suppress this warning: '      ,							UpperCamelCase      ,							)

       __a			=					WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )
       __a			=					WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )

       return cls(feature_extractor=UpperCamelCase      ,							tokenizer=UpperCamelCase      )







   def __call__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    List[str]:
     # For backward compatibility
     if self._in_target_context_manager:
       return self.current_processor(*UpperCamelCase      ,							**UpperCamelCase      )

     if "raw_speech" in kwargs:
       warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'      )
       __a			=					kwargs.pop('raw_speech'      )
     else:
       __a			=					kwargs.pop('audio'      ,							UpperCamelCase      )
     __a			=					kwargs.pop('sampling_rate'      ,							UpperCamelCase      )
     __a			=					kwargs.pop('text'      ,							UpperCamelCase      )
     if len(UpperCamelCase      ) > 0:
       __a			=					args[0]
       __a			=					args[1:]

     if audio is None and text is None:
       raise ValueError('You need to specify either an `audio` or `text` input to process.'      )

     if audio is not None:
       __a			=					self.feature_extractor(UpperCamelCase      ,							*UpperCamelCase      ,							sampling_rate=UpperCamelCase      ,							**UpperCamelCase      )
     if text is not None:
       __a			=					self.tokenizer(UpperCamelCase      ,							**UpperCamelCase      )

     if text is None:
       return inputs
     elif audio is None:
       return encodings
     else:
       __a			=					encodings['input_ids']
       return inputs







   def 		UpperCamelCase__			(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Any:
     # For backward compatibility
     if self._in_target_context_manager:
       return self.current_processor.pad(*UpperCamelCase      ,							**UpperCamelCase      )

     __a			=					kwargs.pop('input_features'      ,							UpperCamelCase      )
     __a			=					kwargs.pop('labels'      ,							UpperCamelCase      )
     if len(UpperCamelCase      ) > 0:
       __a			=					args[0]
       __a			=					args[1:]

     if input_features is not None:
       __a			=					self.feature_extractor.pad(UpperCamelCase      ,							*UpperCamelCase      ,							**UpperCamelCase      )
     if labels is not None:
       __a			=					self.tokenizer.pad(UpperCamelCase      ,							**UpperCamelCase      )

     if labels is None:
       return input_features
     elif input_features is None:
       return labels
     else:
       __a			=					labels['input_ids']
       return input_features







   def 		UpperCamelCase__			(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     return self.tokenizer.batch_decode(*UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase      ,							**UpperCamelCase      )







   @contextmanager
   def 		UpperCamelCase__			(	self      )				->    Any:
     warnings.warn(
         '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
         'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
         'your audio inputs, or in a separate call.'      )
     __a			=					True
     __a			=					self.tokenizer
     yield
     __a			=					self.feature_extractor
     __a			=					False









'''simple docstring'''


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class    __lowercase						:







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=1      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=3      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=[1, 2]      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=6      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							class_embed_type='timestep'      ,							mid_block_scale_factor=1.414      ,							time_embedding_act_fn='gelu'      ,							time_embedding_dim=32      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['prompt']
     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     if "image" in inputs:
       __a			=					inputs['image']
     else:
       __a			=					None

     if "mask_image" in inputs:
       __a			=					inputs['mask_image']
     else:
       __a			=					None

     if "original_image" in inputs:
       __a			=					inputs['original_image']
     else:
       __a			=					None

     __a	,       __a			=					pipe.encode_prompt(UpperCamelCase      )

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
       setattr(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(UpperCamelCase      ,							UpperCamelCase      ) is None      ,							f"`{optional_component}` did not stay set to None after loading."      ,							)

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )



