






"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


_UpperCamelCase						:    Optional[Any]		=						random.Random()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Union[str, Any]=1.0						,   __snake_case     :    Optional[int]=None						,   __snake_case     :    Dict=None       ):





  '''simple docstring'''





  if rng is None:
    lowercase				       =			global_rng

  lowercase				       =			[]
  for batch_idx in range(shape[0]       ):
    values.append([]       )
    for _ in range(shape[1]       ):
      values[-1].append(rng.random() * scale       )

  return values

@require_torch
class 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=2_0_0_0				,						_lowerCamelCase=1				,						_lowerCamelCase=0.0				,						_lowerCamelCase=2_4_0_0_0				,						_lowerCamelCase=True				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			min_seq_length
      lowercase				       =			max_seq_length
      lowercase				       =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      lowercase				       =			feature_size
      lowercase				       =			padding_value
      lowercase				       =			sampling_rate
      lowercase				       =			return_attention_mask







    def      UpperCamelCase_ (     self			):
      return {
          "feature_size": self.feature_size,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
      }







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=False				,						_lowerCamelCase=False			):
      def _flatten(_lowerCamelCase			):
        return list(itertools.chain(*_lowerCamelCase			)			)

      if equal_length:
        lowercase				       =			floats_list((self.batch_size, self.max_seq_length)			)
      else:
        # make sure that inputs increase in size
        lowercase				       =			[
            _flatten(floats_list((x, self.feature_size)			)			)
            for x in range(self.min_seq_length				,						self.max_seq_length				,						self.seq_length_diff			)
        ]

      if numpify:
        lowercase				       =			[np.asarray(_lowerCamelCase			) for x in audio_inputs]

      return audio_inputs


@require_torch
class 			a	(  a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=EncodecFeatureExtractor







    def      UpperCamelCase_ (     self			):
      lowercase				       =			EnCodecFeatureExtractionTester(self			)







    def      UpperCamelCase_ (     self			):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      lowercase				       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      lowercase				       =			[floats_list((1, x)			)[0] for x in range(8_0_0				,						1_4_0_0				,						2_0_0			)]
      lowercase				       =			[np.asarray(_lowerCamelCase			) for audio_input in audio_inputs]

      # Test not batched input
      lowercase				       =			feat_extract(audio_inputs[0]				,						return_tensors='np'			).input_values
      lowercase				       =			feat_extract(np_audio_inputs[0]				,						return_tensors='np'			).input_values
      self.assertTrue(np.allclose(_lowerCamelCase				,						_lowerCamelCase				,						atol=1e-3			)			)

      # Test batched
      lowercase				       =			feat_extract(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='np'			).input_values
      lowercase				       =			feat_extract(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='np'			).input_values
      for enc_seq_a, enc_seq_a in zip(_lowerCamelCase				,						_lowerCamelCase			):
        self.assertTrue(np.allclose(_lowerCamelCase				,						_lowerCamelCase				,						atol=1e-3			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      lowercase				       =			np.random.rand(1_0_0			).astype(np.floataa			)
      lowercase				       =			np_audio_inputs.tolist()

      for inputs in [py_audio_inputs, np_audio_inputs]:
        lowercase				       =			feature_extractor.pad([{'input_values': inputs}]				,						return_tensors='np'			)
        self.assertTrue(np_processed.input_values.dtype == np.floataa			)
        lowercase				       =			feature_extractor.pad([{'input_values': inputs}]				,						return_tensors='pt'			)
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      from datasets import load_dataset

      lowercase				       =			load_dataset('hf-internal-testing/librispeech_asr_dummy'				,						'clean'				,						split='validation'			)
      # automatic decoding with librispeech
      lowercase				       =			ds.sort('id'			).select(range(_lowerCamelCase			)			)[:num_samples]['audio']

      return [x["array"] for x in audio_samples]







    def      UpperCamelCase_ (     self			):
      # fmt: off
      lowercase				       =			torch.tensor(
          [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
           3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
           2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
           4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
           7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
           4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
      # fmt: on
      lowercase				       =			self._load_datasamples(1			)
      lowercase				       =			EncodecFeatureExtractor()
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape				,						(1, 1, 9_3_6_8_0)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]				,						_lowerCamelCase				,						atol=1e-6			)			)







    def      UpperCamelCase_ (     self			):
      # fmt: off
      lowercase				       =			torch.tensor(
          [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
           3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
           2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
           4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
           7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
           4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
      # fmt: on
      lowercase				       =			self._load_datasamples(1			)
      lowercase				       =			[np.tile(input_audio[0][None]				,						reps=(2, 1)			)]
      input_audio[0][1] *= 0.5
      lowercase				       =			EncodecFeatureExtractor(feature_size=2			)
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape				,						(1, 2, 9_3_6_8_0)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]				,						_lowerCamelCase				,						atol=1e-6			)			)
      self.assertTrue(torch.allclose(input_values[0, 1, :3_0]				,						EXPECTED_INPUT_VALUES * 0.5				,						atol=1e-6			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self._load_datasamples(2			)
      # would be easier if the stride was like
      lowercase				       =			EncodecFeatureExtractor(feature_size=1				,						chunk_length_s=1				,						overlap=0.0_1			)

      # pad and trunc raise an error ?
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Both padding and truncation were set. Make sure you only set one.$'				,						):
        lowercase				       =			feature_extractor(
            _lowerCamelCase				,						padding='max_length'				,						truncation=_lowerCamelCase				,						return_tensors='pt'			).input_values

      # truncate to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						truncation=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 7_1_5_2_0)			)  # 2 chunks

      # force truncate to max_length
      lowercase				       =			feature_extractor(
          _lowerCamelCase				,						truncation=_lowerCamelCase				,						max_length=4_8_0_0_0				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 4_8_0_0_0)			)

      # pad to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(padded_outputs.shape				,						(2, 1, 9_5_2_8_0)			)

      # pad to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 9_5_2_8_0)			)

      # force pad to max length
      lowercase				       =			feature_extractor(
          _lowerCamelCase				,						padding='max_length'				,						max_length=1_0_0_0_0_0				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 1_0_0_0_0_0)			)

      # force no pad
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'				,						):
        lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)

      # no pad if no chunk_length_s
      lowercase				       =			None
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'				,						):
        lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)

      # no pad if no overlap
      lowercase				       =			2
      lowercase				       =			None
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'				,						):
        lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)






"""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


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      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
								_A       	=      torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

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







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_A       	=      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}"""		)
														_A       	=      text_input_ids[:, : self.tokenizer.model_max_length]

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

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

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , 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
										_A       	=      torch.cat([uncond_embeddings, text_embeddings]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''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(a__     , "_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   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

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

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

										if do_classifier_free_guidance:
														_A	,		_A       	=      predicted_image_embedding.chunk(2		)
														_A       	=      predicted_image_embedding_uncond + prior_guidance_scale * (
														    predicted_image_embedding_text - predicted_image_embedding_uncond
														)

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output