'''simple docstring'''


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
 from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


snake_case__	:     str      =    get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = tempfile.mkdtemp()

       UpperCAmelCase_				:  Union[str, Any]  = ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
       UpperCAmelCase_				:  Tuple  = dict(zip(snake_case_ ,						range(len(snake_case_		)		)		)		)
       UpperCAmelCase_				:  Tuple  = Path(self.tmpdirname		)
       save_json(snake_case_ ,						save_dir / VOCAB_FILES_NAMES['vocab_file']		)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
            copyfile(snake_case_ ,						save_dir / VOCAB_FILES_NAMES['spm_file']		)

       UpperCAmelCase_				:  List[str]  = SpeechaTextTokenizer.from_pretrained(self.tmpdirname		)
       tokenizer.save_pretrained(self.tmpdirname		)

       UpperCAmelCase_				:  str  = {
           'feature_size': 2_4,
           'num_mel_bins': 2_4,
           'padding_value': 0.0,
           'sampling_rate': 1_6_0_0_0,
           'return_attention_mask': False,
           'do_normalize': True,
       }
       save_json(snake_case_ ,						save_dir / FEATURE_EXTRACTOR_NAME		)


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname ,						**snake_case_		)


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname ,						**snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.get_tokenizer()
       UpperCAmelCase_				:  int  = self.get_feature_extractor()

       UpperCAmelCase_				:  List[str]  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       processor.save_pretrained(self.tmpdirname		)
       UpperCAmelCase_				:  Dict  = SpeechaTextProcessor.from_pretrained(self.tmpdirname		)

       self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()		)
       self.assertIsInstance(processor.tokenizer ,						snake_case_		)

       self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor.to_json_string()		)
       self.assertIsInstance(processor.feature_extractor ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = SpeechaTextProcessor(
           tokenizer=self.get_tokenizer() ,						feature_extractor=self.get_feature_extractor()		)
       processor.save_pretrained(self.tmpdirname		)

       UpperCAmelCase_				:  Optional[Any]  = self.get_tokenizer(bos_token='(BOS)' ,						eos_token='(EOS)'		)
       UpperCAmelCase_				:  Union[str, Any]  = self.get_feature_extractor(do_normalize=snake_case_ ,						padding_value=1.0		)

       UpperCAmelCase_				:  Any  = SpeechaTextProcessor.from_pretrained(
           self.tmpdirname ,						bos_token='(BOS)' ,						eos_token='(EOS)' ,						do_normalize=snake_case_ ,						padding_value=1.0		)

       self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()		)
       self.assertIsInstance(processor.tokenizer ,						snake_case_		)

       self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor_add_kwargs.to_json_string()		)
       self.assertIsInstance(processor.feature_extractor ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Union[str, Any]  = self.get_tokenizer()

       UpperCAmelCase_				:  Union[str, Any]  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  List[Any]  = floats_list((3, 1_0_0_0)		)

       UpperCAmelCase_				:  Union[str, Any]  = feature_extractor(snake_case_ ,						return_tensors='np'		)
       UpperCAmelCase_				:  Any  = processor(snake_case_ ,						return_tensors='np'		)

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum() ,						input_processor[key].sum() ,						delta=1E-2		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Tuple  = self.get_tokenizer()

       UpperCAmelCase_				:  Tuple  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Tuple  = 'This is a test string'

       UpperCAmelCase_				:  List[str]  = processor(text=snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = tokenizer(snake_case_		)

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Dict  = self.get_tokenizer()

       UpperCAmelCase_				:  Any  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Dict  = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCAmelCase_				:  str  = processor.batch_decode(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.batch_decode(snake_case_		)

       self.assertListEqual(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Union[str, Any]  = self.get_tokenizer()

       UpperCAmelCase_				:  int  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       self.assertListEqual(
           processor.model_input_names ,						feature_extractor.model_input_names ,						msg='`processor` and `feature_extractor` model input names do not match' ,						)









import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)  # pylint: disable=invalid-name





def  _snake_case		(self					:     Optional[Any]    ,       snake_case_					:     Optional[Any]    ,       snake_case_					:     Dict    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     Optional[Any]  )							->		Union[str, Any]:
  __UpperCAmelCase	:  Any		=     image.to(device=snake_case_  )
  __UpperCAmelCase	:  Tuple		=     image  # take image as image_embeddings
  __UpperCAmelCase	:  str		=     image_embeddings.unsqueeze(1  )

  # duplicate image embeddings for each generation per prompt, using mps friendly method
  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  int		=     image_embeddings.shape
  __UpperCAmelCase	:  int		=     image_embeddings.repeat(1    ,       snake_case_    ,       1  )
  __UpperCAmelCase	:  Optional[Any]		=     image_embeddings.view(bs_embed * num_images_per_prompt    ,       snake_case_    ,       -1  )

  if do_classifier_free_guidance:
    __UpperCAmelCase	:  List[Any]		=     torch.zeros_like(snake_case_  )

    # For classifier free guidance, we need to do two forward passes.
    # Here we concatenate the unconditional and text embeddings into a single batch
    # to avoid doing two forward passes
    __UpperCAmelCase	:  Union[str, Any]		=     torch.cat([uncond_embeddings, image_embeddings]  )

  return image_embeddings







class      lowerCamelCase_ (			_lowerCAmelCase):




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  ):
   super().__init__()

   __UpperCAmelCase	:  List[Any]		=     {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   __UpperCAmelCase	:  Optional[Any]		=     decoder_pipe_kwargs.get('torch_dtype'  ,  _UpperCAmelCase							) or prior.dtype

   __UpperCAmelCase	:  str		=     StableDiffusionImageVariationPipeline.from_pretrained(
       'lambdalabs/sd-image-variations-diffusers'  ,  **_UpperCAmelCase							)

   # replace `_encode_image` method
   __UpperCAmelCase	:  Dict		=     types.MethodType(_encode_image  ,  self.decoder_pipe							)

   self.register_modules(
       prior=_UpperCAmelCase  ,  tokenizer=_UpperCAmelCase  ,  text_encoder=_UpperCAmelCase  ,  prior_scheduler=_UpperCAmelCase  ,  )




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  ):
   if text_model_output is None:
     __UpperCAmelCase	:  Any		=     len(_UpperCAmelCase							) if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							) else 1
     # get prompt text embeddings
     __UpperCAmelCase	:  str		=     self.tokenizer(
         _UpperCAmelCase  ,  padding='max_length'  ,  max_length=self.tokenizer.model_max_length  ,  return_tensors='pt'  ,  )
     __UpperCAmelCase	:  int		=     text_inputs.input_ids
     __UpperCAmelCase	:  str		=     text_inputs.attention_mask.bool().to(_UpperCAmelCase							)

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       __UpperCAmelCase	:  Any		=     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
       logger.warning(
           'The following part of your input was truncated because CLIP can only handle sequences up to'
           F" {self.tokenizer.model_max_length} tokens: {removed_text}"							)
       __UpperCAmelCase	:  int		=     text_input_ids[:, : self.tokenizer.model_max_length]

     __UpperCAmelCase	:  Any		=     self.text_encoder(text_input_ids.to(_UpperCAmelCase							)							)

     __UpperCAmelCase	:  str		=     text_encoder_output.text_embeds
     __UpperCAmelCase	:  str		=     text_encoder_output.last_hidden_state

   else:
     __UpperCAmelCase	:  str		=     text_model_output[0].shape[0]
     __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     text_model_output[0], text_model_output[1]
     __UpperCAmelCase	:  Optional[Any]		=     text_attention_mask

   __UpperCAmelCase	:  Optional[Any]		=     text_embeddings.repeat_interleave(_UpperCAmelCase  ,  dim=0							)
   __UpperCAmelCase	:  Tuple		=     text_encoder_hidden_states.repeat_interleave(_UpperCAmelCase  ,  dim=0							)
   __UpperCAmelCase	:  Optional[Any]		=     text_mask.repeat_interleave(_UpperCAmelCase  ,  dim=0							)

   if do_classifier_free_guidance:
     __UpperCAmelCase	:  int		=     [''] * batch_size

     __UpperCAmelCase	:  Optional[int]		=     self.tokenizer(
         _UpperCAmelCase  ,  padding='max_length'  ,  max_length=self.tokenizer.model_max_length  ,  truncation=_UpperCAmelCase  ,  return_tensors='pt'  ,  )
     __UpperCAmelCase	:  List[str]		=     uncond_input.attention_mask.bool().to(_UpperCAmelCase							)
     __UpperCAmelCase	:  int		=     self.text_encoder(uncond_input.input_ids.to(_UpperCAmelCase							)							)

     __UpperCAmelCase	:  Optional[Any]		=     uncond_embeddings_text_encoder_output.text_embeds
     __UpperCAmelCase	:  List[str]		=     uncond_embeddings_text_encoder_output.last_hidden_state

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

     __UpperCAmelCase	:  List[str]		=     uncond_embeddings.shape[1]
     __UpperCAmelCase	:  Tuple		=     uncond_embeddings.repeat(1  ,  _UpperCAmelCase							)
     __UpperCAmelCase	:  List[str]		=     uncond_embeddings.view(batch_size * num_images_per_prompt  ,  _UpperCAmelCase							)

     __UpperCAmelCase	:  Tuple		=     uncond_text_encoder_hidden_states.shape[1]
     __UpperCAmelCase	:  Union[str, Any]		=     uncond_text_encoder_hidden_states.repeat(1  ,  _UpperCAmelCase  ,  1							)
     __UpperCAmelCase	:  Any		=     uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt  ,  _UpperCAmelCase  ,  -1							)
     __UpperCAmelCase	:  int		=     uncond_text_mask.repeat_interleave(_UpperCAmelCase  ,  dim=0							)

     # done duplicates

     # For classifier free guidance, we need to do two forward passes.
     # Here we concatenate the unconditional and text embeddings into a single batch
     # to avoid doing two forward passes
     __UpperCAmelCase	:  List[str]		=     torch.cat([uncond_embeddings, text_embeddings]							)
     __UpperCAmelCase	:  str		=     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

     __UpperCAmelCase	:  Any		=     torch.cat([uncond_text_mask, text_mask]							)

   return text_embeddings, text_encoder_hidden_states, text_mask




 @property
 def     a       (			self							):
   if self.device != torch.device('meta'							) or not hasattr(self.prior  ,  '_hf_hook'							):
     return self.device
   for module in self.prior.modules():
     if (
         hasattr(_UpperCAmelCase  ,  '_hf_hook'							)
         and hasattr(module._hf_hook  ,  'execution_device'							)
         and module._hf_hook.execution_device is not None
     ):
       return torch.device(module._hf_hook.execution_device							)
   return self.device




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   if latents is None:
     __UpperCAmelCase	:  Any		=     randn_tensor(_UpperCAmelCase  ,  generator=_UpperCAmelCase  ,  device=_UpperCAmelCase  ,  dtype=_UpperCAmelCase							)
   else:
     if latents.shape != shape:
       raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"							)
     __UpperCAmelCase	:  Optional[int]		=     latents.to(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     latents * scheduler.init_noise_sigma
   return latents




 def     a       (			self  ,  _UpperCAmelCase = None							):
   self.decoder_pipe.to(_UpperCAmelCase							)
   super().to(_UpperCAmelCase							)




 @torch.no_grad()
 def __call__(			self  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = 1  ,  _UpperCAmelCase = 25  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = 4.0  ,  _UpperCAmelCase = 8.0  ,  _UpperCAmelCase = 50  ,  _UpperCAmelCase = 1  ,  _UpperCAmelCase = 0.0  ,  _UpperCAmelCase = "pil"  ,  _UpperCAmelCase = True  ,  ):
   if prompt is not None:
     if isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
       __UpperCAmelCase	:  List[Any]		=     1
     elif isinstance(_UpperCAmelCase  ,  _UpperCAmelCase							):
       __UpperCAmelCase	:  Tuple		=     len(_UpperCAmelCase							)
     else:
       raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase							)}"							)
   else:
     __UpperCAmelCase	:  Tuple		=     text_model_output[0].shape[0]

   __UpperCAmelCase	:  Any		=     self._execution_device

   __UpperCAmelCase	:  Any		=     batch_size * num_images_per_prompt

   __UpperCAmelCase	:  List[str]		=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self._encode_prompt(
       _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

   # prior

   self.prior_scheduler.set_timesteps(_UpperCAmelCase  ,  device=_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.prior_scheduler.timesteps

   __UpperCAmelCase	:  Tuple		=     self.prior.config.embedding_dim

   __UpperCAmelCase	:  Dict		=     self.prepare_latents(
       (batch_size, embedding_dim)  ,  text_embeddings.dtype  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  self.prior_scheduler  ,  )

   for i, t in enumerate(self.progress_bar(_UpperCAmelCase							)							):
     # expand the latents if we are doing classifier free guidance
     __UpperCAmelCase	:  Dict		=     torch.cat([prior_latents] * 2							) if do_classifier_free_guidance else prior_latents

     __UpperCAmelCase	:  Union[str, Any]		=     self.prior(
         _UpperCAmelCase  ,  timestep=_UpperCAmelCase  ,  proj_embedding=_UpperCAmelCase  ,  encoder_hidden_states=_UpperCAmelCase  ,  attention_mask=_UpperCAmelCase  ,  ).predicted_image_embedding

     if do_classifier_free_guidance:
       __UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     predicted_image_embedding.chunk(2							)
       __UpperCAmelCase	:  Optional[Any]		=     predicted_image_embedding_uncond + prior_guidance_scale * (
           predicted_image_embedding_text - predicted_image_embedding_uncond
       )

     if i + 1 == prior_timesteps_tensor.shape[0]:
       __UpperCAmelCase	:  int		=     None
     else:
       __UpperCAmelCase	:  Any		=     prior_timesteps_tensor[i + 1]

     __UpperCAmelCase	:  Union[str, Any]		=     self.prior_scheduler.step(
         _UpperCAmelCase  ,  timestep=_UpperCAmelCase  ,  sample=_UpperCAmelCase  ,  generator=_UpperCAmelCase  ,  prev_timestep=_UpperCAmelCase  ,  ).prev_sample

   __UpperCAmelCase	:  Union[str, Any]		=     self.prior.post_process_latents(_UpperCAmelCase							)

   __UpperCAmelCase	:  Any		=     prior_latents

   __UpperCAmelCase	:  List[Any]		=     self.decoder_pipe(
       image=_UpperCAmelCase  ,  height=_UpperCAmelCase  ,  width=_UpperCAmelCase  ,  num_inference_steps=_UpperCAmelCase  ,  guidance_scale=_UpperCAmelCase  ,  generator=_UpperCAmelCase  ,  output_type=_UpperCAmelCase  ,  return_dict=_UpperCAmelCase  ,  num_images_per_prompt=_UpperCAmelCase  ,  eta=_UpperCAmelCase  ,  )
   return output





