





"""simple docstring"""



import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
       import os

       from flax.core.frozen_dict import unfreeze
       from flax.traverse_util import flatten_dict

       from transformers import FlaxBertModel

       a_ =			"""0.12"""  # assumed parallelism: 8






@require_flax
@is_staging_test
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 @classmethod
 def 					UpperCamelCase__(    cls  ):



     '''simple docstring'''




     __A    :						str						 =     TOKEN
     HfFolder.save_token(__lowerCamelCase  )

 @classmethod
 def 					UpperCamelCase__(    cls  ):



     '''simple docstring'''




     try:
         delete_repo(token=cls._token					,						repo_id='''test-model-flax'''  )
     except HTTPError:
         pass

     try:
         delete_repo(token=cls._token					,						repo_id='''valid_org/test-model-flax-org'''  )
     except HTTPError:
         pass

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     BertConfig(
         vocab_size=99					,						hidden_size=32					,						num_hidden_layers=5					,						num_attention_heads=4					,						intermediate_size=37  )
     __A    :						Union[str, Any]						 =     FlaxBertModel(__lowerCamelCase  )
     model.push_to_hub('''test-model-flax'''					,						use_auth_token=self._token  )

     __A    :						List[Any]						 =     FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""  )

     __A    :						List[str]						 =     flatten_dict(unfreeze(model.params  )  )
     __A    :						Union[str, Any]						 =     flatten_dict(unfreeze(new_model.params  )  )

     for key in base_params.keys():
         __A    :						Tuple						 =     (base_params[key] - new_params[key]).sum().item()
         self.assertLessEqual(__lowerCamelCase					,						1e-3					,						msg=F"""{key} not identical"""  )

     # Reset repo
     delete_repo(token=self._token					,						repo_id='''test-model-flax'''  )

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
         model.save_pretrained(__lowerCamelCase					,						repo_id='''test-model-flax'''					,						push_to_hub=__lowerCamelCase					,						use_auth_token=self._token  )

     __A    :						List[str]						 =     FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""  )

     __A    :						int						 =     flatten_dict(unfreeze(model.params  )  )
     __A    :						str						 =     flatten_dict(unfreeze(new_model.params  )  )

     for key in base_params.keys():
         __A    :						Optional[int]						 =     (base_params[key] - new_params[key]).sum().item()
         self.assertLessEqual(__lowerCamelCase					,						1e-3					,						msg=F"""{key} not identical"""  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     BertConfig(
         vocab_size=99					,						hidden_size=32					,						num_hidden_layers=5					,						num_attention_heads=4					,						intermediate_size=37  )
     __A    :						List[str]						 =     FlaxBertModel(__lowerCamelCase  )
     model.push_to_hub('''valid_org/test-model-flax-org'''					,						use_auth_token=self._token  )

     __A    :						Dict						 =     FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''  )

     __A    :						Tuple						 =     flatten_dict(unfreeze(model.params  )  )
     __A    :						List[str]						 =     flatten_dict(unfreeze(new_model.params  )  )

     for key in base_params.keys():
         __A    :						Tuple						 =     (base_params[key] - new_params[key]).sum().item()
         self.assertLessEqual(__lowerCamelCase					,						1e-3					,						msg=F"""{key} not identical"""  )

     # Reset repo
     delete_repo(token=self._token					,						repo_id='''valid_org/test-model-flax-org'''  )

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
         model.save_pretrained(
             __lowerCamelCase					,						repo_id='''valid_org/test-model-flax-org'''					,						push_to_hub=__lowerCamelCase					,						use_auth_token=self._token  )

     __A    :						Any						 =     FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''  )

     __A    :						str						 =     flatten_dict(unfreeze(model.params  )  )
     __A    :						Optional[Any]						 =     flatten_dict(unfreeze(new_model.params  )  )

     for key in base_params.keys():
         __A    :						int						 =     (base_params[key] - new_params[key]).sum().item()
         self.assertLessEqual(__lowerCamelCase					,						1e-3					,						msg=F"""{key} not identical"""  )







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Dict						)   ->Optional[int]:



    '''simple docstring'''







    __A    :						List[Any]						 =     True
    __A    :						List[Any]						 =     flatten_dict(modela.params						)
    __A    :						List[Any]						 =     flatten_dict(modela.params						)
    for key in flat_params_a.keys():
        if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]						)						) > 1e-4:
            __A    :						Optional[Any]						 =     False

    return models_are_equal






@require_flax
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )
     __A    :						Optional[Any]						 =     FlaxBertModel(__lowerCamelCase  )

     __A    :						Any						 =     '''bert'''
     with tempfile.TemporaryDirectory() as tmp_dir:
         model.save_pretrained(os.path.join(__lowerCamelCase					,						__lowerCamelCase  )  )

         with self.assertRaises(__lowerCamelCase  ):
             __A    :						Dict						 =     FlaxBertModel.from_pretrained(__lowerCamelCase  )

         __A    :						List[Any]						 =     FlaxBertModel.from_pretrained(__lowerCamelCase					,						subfolder=__lowerCamelCase  )

     self.assertTrue(check_models_equal(__lowerCamelCase					,						__lowerCamelCase  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''  )
     __A    :						int						 =     FlaxBertModel(__lowerCamelCase  )

     __A    :						List[str]						 =     '''bert'''
     with tempfile.TemporaryDirectory() as tmp_dir:
         model.save_pretrained(os.path.join(__lowerCamelCase					,						__lowerCamelCase  )					,						max_shard_size='''10KB'''  )

         with self.assertRaises(__lowerCamelCase  ):
             __A    :						str						 =     FlaxBertModel.from_pretrained(__lowerCamelCase  )

         __A    :						List[Any]						 =     FlaxBertModel.from_pretrained(__lowerCamelCase					,						subfolder=__lowerCamelCase  )

     self.assertTrue(check_models_equal(__lowerCamelCase					,						__lowerCamelCase  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     '''bert'''
     __A    :						List[str]						 =     '''hf-internal-testing/tiny-random-bert-subfolder'''

     with self.assertRaises(__lowerCamelCase  ):
         __A    :						int						 =     FlaxBertModel.from_pretrained(__lowerCamelCase  )

     __A    :						Dict						 =     FlaxBertModel.from_pretrained(__lowerCamelCase					,						subfolder=__lowerCamelCase  )

     self.assertIsNotNone(__lowerCamelCase  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     '''bert'''
     __A    :						int						 =     '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
     with self.assertRaises(__lowerCamelCase  ):
         __A    :						List[Any]						 =     FlaxBertModel.from_pretrained(__lowerCamelCase  )

     __A    :						Optional[Any]						 =     FlaxBertModel.from_pretrained(__lowerCamelCase					,						subfolder=__lowerCamelCase  )

     self.assertIsNotNone(__lowerCamelCase  )











"""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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     ["""vqvae"""]

 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						mel=__lowerCamelCase					,						vqvae=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 50 if isinstance(self.scheduler					,						__lowerCamelCase  ) else 1000

 @torch.no_grad()
 def __call__(    self					,						__lowerCamelCase = 1					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase=True					,						):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     steps or self.get_default_steps()
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						Any						 =     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size  ) == int:
         __A    :						Optional[Any]						 =     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         __A    :						Dict						 =     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )					,						generator=__lowerCamelCase					,						device=self.device					,						)
     __A    :						Optional[int]						 =     noise
     __A    :						int						 =     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						int						 =     self.mel.audio_slice_to_image(__lowerCamelCase  )
         __A    :						Optional[Any]						 =     np.frombuffer(input_image.tobytes()					,						dtype='''uint8'''  ).reshape(
             (input_image.height, input_image.width)  )
         __A    :						Union[str, Any]						 =     (input_image / 255) * 2 - 1
         __A    :						Any						 =     torch.tensor(input_image[np.newaxis, :, :]					,						dtype=torch.float  ).to(self.device  )

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

         if start_step > 0:
             __A    :						Union[str, Any]						 =     self.scheduler.add_noise(__lowerCamelCase					,						__lowerCamelCase					,						self.scheduler.timesteps[start_step - 1]  )

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

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]  )  ):
         if isinstance(self.unet					,						__lowerCamelCase  ):
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         else:
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']

         if isinstance(self.scheduler					,						__lowerCamelCase  ):
             __A    :						Optional[Any]						 =     self.scheduler.step(
                 model_output=__lowerCamelCase					,						timestep=__lowerCamelCase					,						sample=__lowerCamelCase					,						eta=__lowerCamelCase					,						generator=__lowerCamelCase					,						)['''prev_sample''']
         else:
             __A    :						Optional[Any]						 =     self.scheduler.step(
                 model_output=__lowerCamelCase					,						timestep=__lowerCamelCase					,						sample=__lowerCamelCase					,						generator=__lowerCamelCase					,						)['''prev_sample''']

         if mask is not None:
             if mask_start > 0:
                 __A    :						Optional[Any]						 =     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 __A    :						Tuple						 =     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         __A    :						Union[str, Any]						 =     1 / self.vqvae.config.scaling_factor * images
         __A    :						List[str]						 =     self.vqvae.decode(__lowerCamelCase  )['''sample''']

     __A    :						Any						 =     (images / 2 + 0.5).clamp(0					,						1  )
     __A    :						Any						 =     images.cpu().permute(0					,						2					,						3					,						1  ).numpy()
     __A    :						Any						 =     (images * 255).round().astype('''uint8'''  )
     __A    :						List[Any]						 =     list(
         (Image.fromarray(_[:, :, 0]  ) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(__lowerCamelCase					,						mode='''RGB'''  ).convert('''L'''  ) for _ in images)  )

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

     return BaseOutput(**AudioPipelineOutput(np.array(__lowerCamelCase  )[:, np.newaxis, :]  )					,						**ImagePipelineOutput(__lowerCamelCase  )  )

 @torch.no_grad()
 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = 50  ):



     '''simple docstring'''




     assert isinstance(self.scheduler					,						__lowerCamelCase  )
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						str						 =     np.array(
         [np.frombuffer(image.tobytes()					,						dtype='''uint8'''  ).reshape((1, image.height, image.width)  ) for image in images]  )
     __A    :						Any						 =     (sample / 255) * 2 - 1
     __A    :						Dict						 =     torch.Tensor(__lowerCamelCase  ).to(self.device  )

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,						(0,)  )  ):
         __A    :						Tuple						 =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         __A    :						List[Any]						 =     self.scheduler.alphas_cumprod[t]
         __A    :						Optional[int]						 =     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         __A    :						List[Any]						 =     1 - alpha_prod_t
         __A    :						int						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         __A    :						Union[str, Any]						 =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         __A    :						List[Any]						 =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         __A    :						Union[str, Any]						 =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample







 @staticmethod
 def 					UpperCamelCase__(    __lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     acos(torch.dot(torch.flatten(__lowerCamelCase  )					,						torch.flatten(__lowerCamelCase  )  ) / torch.norm(__lowerCamelCase  ) / torch.norm(__lowerCamelCase  )  )
     return sin((1 - alpha) * theta  ) * xa / sin(__lowerCamelCase  ) + sin(alpha * theta  ) * xa / sin(__lowerCamelCase  )




