


"""simple docstring"""

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class    lowerCamelCase__						(		_a    ):
      a :     Dict											=		["""vqvae"""]
      def __init__(		self	:	Any      ,       A_	:	List[Any]      ,       A_	:	int      ,       A_	:	Optional[int]      ,       A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__()
       self.register_modules(unet=A_      ,       scheduler=A_      ,       mel=A_      ,       vqvae=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       return 5_0 if isinstance(self.scheduler      ,       A_							) else 1_0_0_0
      @torch.no_grad()
      def __call__(		self	:	Dict      ,       A_	:	Tuple = 1      ,       A_	:	Any = None      ,       A_	:	Union[str, Any] = None      ,       A_	:	Union[str, Any] = 0      ,       A_	:	Any = 0      ,       A_	:	Any = None      ,       A_	:	Tuple = None      ,       A_	:	Tuple = 0      ,       A_	:	Dict = 0      ,       A_	:	str = None      ,       A_	:	List[str] = 0      ,       A_	:	int = None      ,       A_	:	Any = None      ,       A_	:	Optional[Any]=True      ,       ):






       '''simple docstring'''


       __lowercase									=	steps or self.get_default_steps()
       self.scheduler.set_timesteps(A_							)
       __lowercase									=	step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size							) == int:
        __lowercase									=	(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
        __lowercase									=	randn_tensor(
            (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size[0],
                self.unet.config.sample_size[1],
            )      ,       generator=A_      ,       device=self.device      ,       )
       __lowercase									=	noise
       __lowercase									=	None

       if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(A_      ,       A_							)
        __lowercase									=	self.mel.audio_slice_to_image(A_							)
        __lowercase									=	np.frombuffer(input_image.tobytes()      ,       dtype="""uint8"""							).reshape(
            (input_image.height, input_image.width)							)
        __lowercase									=	(input_image / 2_5_5) * 2 - 1
        __lowercase									=	torch.tensor(input_image[np.newaxis, :, :]      ,       dtype=torch.float							).to(self.device							)

        if self.vqvae is not None:
         __lowercase									=	self.vqvae.encode(torch.unsqueeze(A_      ,       0							)							).latent_dist.sample(
             generator=A_							)[0]
         __lowercase									=	self.vqvae.config.scaling_factor * input_images

        if start_step > 0:
         __lowercase									=	self.scheduler.add_noise(A_      ,       A_      ,       self.scheduler.timesteps[start_step - 1]							)

        __lowercase									=	(
            self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
        )
        __lowercase									=	int(mask_start_secs * pixels_per_second							)
        __lowercase									=	int(mask_end_secs * pixels_per_second							)
        __lowercase									=	self.scheduler.add_noise(A_      ,       A_      ,       torch.tensor(self.scheduler.timesteps[start_step:]							)							)

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
        if isinstance(self.unet      ,       A_							):
         __lowercase									=	self.unet(A_      ,       A_      ,       A_							)["""sample"""]
        else:
         __lowercase									=	self.unet(A_      ,       A_							)["""sample"""]

        if isinstance(self.scheduler      ,       A_							):
         __lowercase									=	self.scheduler.step(
             model_output=A_      ,       timestep=A_      ,       sample=A_      ,       eta=A_      ,       generator=A_      ,       )["""prev_sample"""]
        else:
         __lowercase									=	self.scheduler.step(
             model_output=A_      ,       timestep=A_      ,       sample=A_      ,       generator=A_      ,       )["""prev_sample"""]

        if mask is not None:
         if mask_start > 0:
          __lowercase									=	mask[:, step, :, :mask_start]
         if mask_end > 0:
          __lowercase									=	mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
        # 0.18215 was scaling factor used in training to ensure unit variance
        __lowercase									=	1 / self.vqvae.config.scaling_factor * images
        __lowercase									=	self.vqvae.decode(A_							)["""sample"""]

       __lowercase									=	(images / 2 + 0.5).clamp(0      ,       1							)
       __lowercase									=	images.cpu().permute(0      ,       2      ,       3      ,       1							).numpy()
       __lowercase									=	(images * 2_5_5).round().astype("""uint8"""							)
       __lowercase									=	list(
           (Image.fromarray(_[:, :, 0]							) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(A_      ,       mode="""RGB"""							).convert("""L"""							) for _ in images)							)

       __lowercase									=	[self.mel.image_to_audio(A_							) for _ in images]
       if not return_dict:
        return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(A_							)[:, np.newaxis, :]							)      ,       **ImagePipelineOutput(A_							)							)
      @torch.no_grad()
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Optional[int]      ,       A_	:	Tuple = 5_0							):






       '''simple docstring'''


       assert isinstance(self.scheduler      ,       A_							)
       self.scheduler.set_timesteps(A_							)
       __lowercase									=	np.array(
           [np.frombuffer(image.tobytes()      ,       dtype="""uint8"""							).reshape((1, image.height, image.width)							) for image in images]							)
       __lowercase									=	(sample / 2_5_5) * 2 - 1
       __lowercase									=	torch.Tensor(A_							).to(self.device							)

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,       (0,)							)							):
        __lowercase									=	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
        __lowercase									=	self.scheduler.alphas_cumprod[t]
        __lowercase									=	(
            self.scheduler.alphas_cumprod[prev_timestep]
            if prev_timestep >= 0
            else self.scheduler.final_alpha_cumprod
        )
        __lowercase									=	1 - alpha_prod_t
        __lowercase									=	self.unet(A_      ,       A_							)["""sample"""]
        __lowercase									=	(1 - alpha_prod_t_prev) ** 0.5 * model_output
        __lowercase									=	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
        __lowercase									=	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample






      @staticmethod
      def SCREAMING_SNAKE_CASE_	(		A_	:	Optional[int]      ,       A_	:	Optional[Any]      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	acos(torch.dot(torch.flatten(A_							)      ,       torch.flatten(A_							)							) / torch.norm(A_							) / torch.norm(A_							)							)
       return sin((1 - alpha) * theta							) * xa / sin(A_							) + sin(alpha * theta							) * xa / sin(A_							)




"""simple docstring"""

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer





class    lowerCamelCase__						(		_a    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	tempfile.mkdtemp()
       __lowercase									=	5

       # Realm tok
       __lowercase									=	[
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """test""",
           """question""",
           """this""",
           """is""",
           """the""",
           """first""",
           """second""",
           """third""",
           """fourth""",
           """fifth""",
           """record""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       __lowercase									=	os.path.join(self.tmpdirname      ,       """realm_tokenizer"""							)
       os.makedirs(A_      ,       exist_ok=A_							)
       __lowercase									=	os.path.join(A_      ,       VOCAB_FILES_NAMES["""vocab_file"""]							)
       with open(self.vocab_file      ,       """w"""      ,       encoding="""utf-8"""							) as vocab_writer:
        vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)

       __lowercase									=	os.path.join(self.tmpdirname      ,       """realm_block_records"""							)
       os.makedirs(A_      ,       exist_ok=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,       """realm_tokenizer"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       shutil.rmtree(self.tmpdirname							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	RealmConfig(num_block_records=self.num_block_records							)
       return config
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	Dataset.from_dict(
           {
               """id""": ["""0""", """1"""],
               """question""": ["""foo""", """bar"""],
               """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
           }							)
       return dataset
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	np.array(
           [
               B"""This is the first record""",
               B"""This is the second record""",
               B"""This is the third record""",
               B"""This is the fourth record""",
               B"""This is the fifth record""",
               B"""This is a longer longer longer record""",
           ]      ,       dtype=A_      ,       )
       return block_records
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	RealmRetriever(
           block_records=self.get_dummy_block_records()      ,       tokenizer=self.get_tokenizer()      ,       )
       return retriever
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.get_config()
       __lowercase									=	self.get_dummy_retriever()
       __lowercase									=	retriever.tokenizer

       __lowercase									=	np.array([0, 3]      ,       dtype="""long"""							)
       __lowercase									=	tokenizer(["""Test question"""]							).input_ids
       __lowercase									=	tokenizer(
           ["""the fourth"""]      ,       add_special_tokens=A_      ,       return_token_type_ids=A_      ,       return_attention_mask=A_      ,       ).input_ids
       __lowercase									=	config.reader_seq_len

       __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	retriever(
           A_      ,       A_      ,       answer_ids=A_      ,       max_length=A_      ,       return_tensors="""np"""							)

       self.assertEqual(len(A_							)      ,       2							)
       self.assertEqual(len(A_							)      ,       2							)
       self.assertEqual(len(A_							)      ,       2							)
       self.assertEqual(concat_inputs.input_ids.shape      ,       (2, 1_0)							)
       self.assertEqual(concat_inputs.attention_mask.shape      ,       (2, 1_0)							)
       self.assertEqual(concat_inputs.token_type_ids.shape      ,       (2, 1_0)							)
       self.assertEqual(concat_inputs.special_tokens_mask.shape      ,       (2, 1_0)							)
       self.assertEqual(
           tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]							)      ,       ["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""]      ,       )
       self.assertEqual(
           tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]							)      ,       ["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""]      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.get_config()
       __lowercase									=	self.get_dummy_retriever()
       __lowercase									=	retriever.tokenizer

       __lowercase									=	np.array([0, 3, 5]      ,       dtype="""long"""							)
       __lowercase									=	tokenizer(["""Test question"""]							).input_ids
       __lowercase									=	tokenizer(
           ["""the fourth""", """longer longer"""]      ,       add_special_tokens=A_      ,       return_token_type_ids=A_      ,       return_attention_mask=A_      ,       ).input_ids
       __lowercase									=	config.reader_seq_len

       __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	retriever(
           A_      ,       A_      ,       answer_ids=A_      ,       max_length=A_      ,       return_tensors="""np"""							)

       self.assertEqual([False, True, True]      ,       A_							)
       self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]      ,       A_							)
       self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]      ,       A_							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.get_dummy_retriever()
       retriever.save_pretrained(os.path.join(self.tmpdirname      ,       """realm_block_records"""							)							)

       # Test local path
       __lowercase									=	retriever.from_pretrained(os.path.join(self.tmpdirname      ,       """realm_block_records"""							)							)
       self.assertEqual(retriever.block_records[0]      ,       B"""This is the first record"""							)

       # Test mocked remote path
       with patch("""transformers.models.realm.retrieval_realm.hf_hub_download"""							) as mock_hf_hub_download:
        __lowercase									=	os.path.join(
            os.path.join(self.tmpdirname      ,       """realm_block_records"""							)      ,       _REALM_BLOCK_RECORDS_FILENAME							)
        __lowercase									=	RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa"""							)

       self.assertEqual(retriever.block_records[0]      ,       B"""This is the first record"""							)
