





"""simple docstring"""




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


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





def 			a_							(				self			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       image.to(device=_lowercase  )
						_UpperCamelCase     :   int      =       image  # take image as image_embeddings
						_UpperCamelCase     :   Optional[Any]      =       image_embeddings.unsqueeze(1  )

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

						if do_classifier_free_guidance:
												_UpperCamelCase     :   Dict      =       torch.zeros_like(_lowercase  )

												# 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     :   Any      =       torch.cat([uncond_embeddings, image_embeddings]  )

						return image_embeddings






class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       str,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       UnCLIPScheduler,					lowerCAmelCase__						:       Optional[dict] = None,					)      -> int:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase     :   Any      =       {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										_UpperCamelCase     :   Union[str, Any]      =       decoder_pipe_kwargs.get('''torch_dtype''',					lowerCAmelCase__				) or prior.dtype

										_UpperCamelCase     :   Dict      =       StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers''',					**lowerCAmelCase__				)

										# replace `_encode_image` method
										_UpperCamelCase     :   Optional[Any]      =       types.MethodType(_encode_image,					self.decoder_pipe				)

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






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Optional[Union[CLIPTextModelOutput, Tuple]] = None,					lowerCAmelCase__						:       Optional[torch.Tensor] = None,					)      -> Tuple:




										'''simple docstring'''



										if text_model_output is None:
																_UpperCamelCase     :   List[Any]      =       len(lowerCAmelCase__				) if isinstance(lowerCAmelCase__,					lowerCAmelCase__				) else 1
																# get prompt text embeddings
																_UpperCamelCase     :   Any      =       self.tokenizer(
																    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					return_tensors='''pt''',					)
																_UpperCamelCase     :   List[str]      =       text_inputs.input_ids
																_UpperCamelCase     :   List[str]      =       text_inputs.attention_mask.bool().to(lowerCAmelCase__				)

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						_UpperCamelCase     :   str      =       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     :   Optional[Any]      =       text_input_ids[:, : self.tokenizer.model_max_length]

																_UpperCamelCase     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)

																_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds
																_UpperCamelCase     :   List[Any]      =       text_encoder_output.last_hidden_state

										else:
																_UpperCamelCase     :   Any      =       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(lowerCAmelCase__,					dim=0				)
										_UpperCamelCase     :   Dict      =       text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__,					dim=0				)
										_UpperCamelCase     :   int      =       text_mask.repeat_interleave(lowerCAmelCase__,					dim=0				)

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

																_UpperCamelCase     :   Tuple      =       self.tokenizer(
																    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
																_UpperCamelCase     :   Tuple      =       uncond_input.attention_mask.bool().to(lowerCAmelCase__				)
																_UpperCamelCase     :   str      =       self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__				)				)

																_UpperCamelCase     :   Tuple      =       uncond_embeddings_text_encoder_output.text_embeds
																_UpperCamelCase     :   List[Any]      =       uncond_embeddings_text_encoder_output.last_hidden_state

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

																_UpperCamelCase     :   Dict      =       uncond_embeddings.shape[1]
																_UpperCamelCase     :   Optional[Any]      =       uncond_embeddings.repeat(1,					lowerCAmelCase__				)
																_UpperCamelCase     :   Union[str, Any]      =       uncond_embeddings.view(batch_size * num_images_per_prompt,					lowerCAmelCase__				)

																_UpperCamelCase     :   Optional[Any]      =       uncond_text_encoder_hidden_states.shape[1]
																_UpperCamelCase     :   List[Any]      =       uncond_text_encoder_hidden_states.repeat(1,					lowerCAmelCase__,					1				)
																_UpperCamelCase     :   List[str]      =       uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt,					lowerCAmelCase__,					-1				)
																_UpperCamelCase     :   Any      =       uncond_text_mask.repeat_interleave(lowerCAmelCase__,					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     :   Any      =       torch.cat([uncond_embeddings, text_embeddings]				)
																_UpperCamelCase     :   Dict      =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

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

										return text_embeddings, text_encoder_hidden_states, text_mask






				@property
				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										if self.device != torch.device('''meta'''				) or not hasattr(self.prior,					'''_hf_hook'''				):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(lowerCAmelCase__,					'''_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 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int]				)      -> int:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Union[str, Any]      =       randn_tensor(lowerCAmelCase__,					generator=lowerCAmelCase__,					device=lowerCAmelCase__,					dtype=lowerCAmelCase__				)
										else:
																if latents.shape != shape:
																						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
																_UpperCamelCase     :   Union[str, Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[Union[str, torch.device]] = None				)      -> Optional[Any]:




										'''simple docstring'''



										self.decoder_pipe.to(lowerCAmelCase__				)
										super().to(lowerCAmelCase__				)






				@torch.no_grad()
				def __call__(					self						:       Tuple,					lowerCAmelCase__						:       Optional[Union[str, List[str]]] = None,					lowerCAmelCase__						:       Optional[int] = None,					lowerCAmelCase__						:       Optional[int] = None,					lowerCAmelCase__						:       int = 1,					lowerCAmelCase__						:       int = 2_5,					lowerCAmelCase__						:       Optional[torch.Generator] = None,					lowerCAmelCase__						:       Optional[torch.FloatTensor] = None,					lowerCAmelCase__						:       Optional[Union[CLIPTextModelOutput, Tuple]] = None,					lowerCAmelCase__						:       Optional[torch.Tensor] = None,					lowerCAmelCase__						:       float = 4.0,					lowerCAmelCase__						:       float = 8.0,					lowerCAmelCase__						:       int = 5_0,					lowerCAmelCase__						:       Optional[int] = 1,					lowerCAmelCase__						:       float = 0.0,					lowerCAmelCase__						:       Optional[str] = "pil",					lowerCAmelCase__						:       bool = True,					)      -> Tuple:




										'''simple docstring'''



										if prompt is not None:
																if isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																						_UpperCamelCase     :   Optional[int]      =       1
																elif isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																						_UpperCamelCase     :   List[Any]      =       len(lowerCAmelCase__				)
																else:
																						raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__				)}"""				)
										else:
																_UpperCamelCase     :   Any      =       text_model_output[0].shape[0]

										_UpperCamelCase     :   Tuple      =       self._execution_device

										_UpperCamelCase     :   Optional[int]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[Any]      =       prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       self._encode_prompt(
										    lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.prior_scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       self.prior_scheduler.timesteps

										_UpperCamelCase     :   str      =       self.prior.config.embedding_dim

										_UpperCamelCase     :   Dict      =       self.prepare_latents(
										    (batch_size, embedding_dim),					text_embeddings.dtype,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					self.prior_scheduler,					)

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

																_UpperCamelCase     :   Any      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					encoder_hidden_states=lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					).predicted_image_embedding

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

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						_UpperCamelCase     :   Union[str, Any]      =       None
																else:
																						_UpperCamelCase     :   Union[str, Any]      =       prior_timesteps_tensor[i + 1]

																_UpperCamelCase     :   Dict      =       self.prior_scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					generator=lowerCAmelCase__,					prev_timestep=lowerCAmelCase__,					).prev_sample

										_UpperCamelCase     :   str      =       self.prior.post_process_latents(lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       prior_latents

										_UpperCamelCase     :   Tuple      =       self.decoder_pipe(
										    image=lowerCAmelCase__,					height=lowerCAmelCase__,					width=lowerCAmelCase__,					num_inference_steps=lowerCAmelCase__,					guidance_scale=lowerCAmelCase__,					generator=lowerCAmelCase__,					output_type=lowerCAmelCase__,					return_dict=lowerCAmelCase__,					num_images_per_prompt=lowerCAmelCase__,					eta=lowerCAmelCase__,					)
										return output










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

										with open(self.feature_extraction_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

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

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






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

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

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






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



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

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					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 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

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






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

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



