'''simple docstring'''

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  __a:					Optional[Any]			  =  """0.12"""  # assumed parallelism: 8


if is_torch_available():
  import torch
def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase=None	):





 if rng is None:
  lowercase__							:       int										=   random.Random()

 lowercase__							:       Union[str, Any]										=   1
 for dim in shape:
  total_dims *= dim

 lowercase__							:       str										=   []
 for _ in range(__snake_case	):
  values.append(rng.randint(0						,	vocab_size - 1	)	)

 lowercase__							:       Optional[int]										=   np.array(__snake_case						,	dtype=jnp.intaa	).reshape(__snake_case	)

 return output
def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase=None	):
 lowercase__							:       Optional[Any]										=   ids_tensor(__snake_case						,	vocab_size=2						,	rng=__snake_case	)
 # make sure that at least one token is attended to for each batch
 lowercase__							:       Dict										=   1
 return attn_mask



@require_flax
class      UpperCAmelCase    :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       None
       SCREAMING_SNAKE_CASE   						=       ()

       def _lowerCAmelCase(		self					)    ->    str:
        lowercase__					,				lowercase__							:       Optional[Any]										=   self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        lowercase__							:       Union[str, Any]										=   2
        lowercase__							:       List[str]										=   inputs['''input_ids'''].shape[-1] // 2
        lowercase__							:       Tuple										=   inputs['''input_ids'''][:max_batch_size, :sequence_length]

        lowercase__							:       Any										=   jnp.ones_like(__a					)
        lowercase__							:       Any										=   attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        lowercase__							:       Tuple										=   input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
         # hack to allow generate for models such as GPT2 as is done in `generate()`
         lowercase__							:       Optional[Any]										=   config.eos_token_id
        return config, input_ids, attention_mask, max_length

       @is_pt_flax_cross_test
       def _lowerCAmelCase(		self					)    ->    Union[str, Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       List[Any]										=   self._get_input_ids_and_config()
        lowercase__							:       int										=   False
        lowercase__							:       Optional[int]										=   max_length
        lowercase__							:       List[str]										=   0

        for model_class in self.all_generative_model_classes:
         lowercase__							:       Any										=   model_class(__a					)

         lowercase__							:       int										=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowercase__							:       Optional[Any]										=   getattr(__a      ,  __a					)
         lowercase__							:       Optional[Any]										=   pt_model_class(__a					).eval()
         lowercase__							:       List[str]										=   load_flax_weights_in_pytorch_model(__a      ,  flax_model.params					)

         lowercase__							:       str										=   flax_model.generate(__a					).sequences
         lowercase__							:       Tuple										=   pt_model.generate(torch.tensor(__a      ,  dtype=torch.long					)					)

         if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
          lowercase__							:       Dict										=   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

         self.assertListEqual(pt_generation_outputs.numpy().tolist()      ,  flax_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    Dict:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Union[str, Any]										=   self._get_input_ids_and_config()
        lowercase__							:       Dict										=   False
        lowercase__							:       Optional[int]										=   max_length

        for model_class in self.all_generative_model_classes:
         lowercase__							:       Optional[int]										=   model_class(__a					)

         lowercase__							:       Optional[Any]										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       str										=   jit(model.generate					)
         lowercase__							:       Tuple										=   jit_generate(__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Any										=   self._get_input_ids_and_config()
        lowercase__							:       str										=   True
        lowercase__							:       Optional[int]										=   max_length

        for model_class in self.all_generative_model_classes:
         lowercase__							:       int										=   model_class(__a					)

         lowercase__							:       Optional[int]										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       int										=   jit(model.generate					)
         lowercase__							:       Optional[int]										=   jit_generate(__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    List[str]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Union[str, Any]										=   self._get_input_ids_and_config()
        lowercase__							:       Optional[int]										=   False
        lowercase__							:       int										=   max_length
        lowercase__							:       Tuple										=   2

        for model_class in self.all_generative_model_classes:
         lowercase__							:       int										=   model_class(__a					)

         lowercase__							:       Optional[int]										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       int										=   jit(model.generate					)
         lowercase__							:       str										=   jit_generate(__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Optional[Any]										=   self._get_input_ids_and_config()
        lowercase__							:       Optional[Any]										=   False
        lowercase__							:       Dict										=   max_length
        lowercase__							:       List[Any]										=   2
        lowercase__							:       Optional[Any]										=   2

        for model_class in self.all_generative_model_classes:
         lowercase__							:       Optional[Any]										=   model_class(__a					)

         lowercase__							:       Union[str, Any]										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[0]      ,  input_ids.shape[0] * config.num_return_sequences					)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       int										=   self._get_input_ids_and_config()
        lowercase__							:       int										=   True
        lowercase__							:       Optional[int]										=   max_length
        lowercase__							:       Optional[int]										=   0.8
        lowercase__							:       Optional[int]										=   10
        lowercase__							:       Optional[int]										=   0.3
        lowercase__							:       Tuple										=   1
        lowercase__							:       List[str]										=   8
        lowercase__							:       int										=   9

        for model_class in self.all_generative_model_classes:
         lowercase__							:       str										=   model_class(__a					)

         lowercase__							:       str										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       Tuple										=   jit(model.generate					)
         lowercase__							:       List[str]										=   jit_generate(__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       List[str]										=   self._get_input_ids_and_config()
        lowercase__							:       Optional[Any]										=   max_length
        lowercase__							:       Optional[int]										=   1
        lowercase__							:       List[Any]										=   8
        lowercase__							:       str										=   9

        for model_class in self.all_generative_model_classes:
         lowercase__							:       Optional[Any]										=   model_class(__a					)

         lowercase__							:       str										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       Tuple										=   jit(model.generate					)
         lowercase__							:       List[Any]										=   jit_generate(__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    Optional[int]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Optional[int]										=   self._get_input_ids_and_config()
        lowercase__							:       List[Any]										=   max_length
        lowercase__							:       Dict										=   2
        lowercase__							:       int										=   1
        lowercase__							:       Optional[int]										=   8
        lowercase__							:       int										=   9

        for model_class in self.all_generative_model_classes:
         lowercase__							:       List[str]										=   model_class(__a					)

         lowercase__							:       Dict										=   model.generate(__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       Union[str, Any]										=   jit(model.generate					)
         lowercase__							:       Tuple										=   jit_generate(__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    List[Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Tuple										=   self._get_input_ids_and_config()

        # pad attention mask on the left
        lowercase__							:       int										=   attention_mask.at[(0, 0)].set(0					)

        lowercase__							:       str										=   False
        lowercase__							:       Union[str, Any]										=   max_length

        for model_class in self.all_generative_model_classes:
         lowercase__							:       Optional[int]										=   model_class(__a					)

         lowercase__							:       int										=   model.generate(__a      ,  attention_mask=__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       Any										=   jit(model.generate					)
         lowercase__							:       int										=   jit_generate(__a      ,  attention_mask=__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       str										=   self._get_input_ids_and_config()

        # pad attention mask on the left
        lowercase__							:       List[Any]										=   attention_mask.at[(0, 0)].set(0					)

        lowercase__							:       Optional[Any]										=   True
        lowercase__							:       List[str]										=   max_length

        for model_class in self.all_generative_model_classes:
         lowercase__							:       List[str]										=   model_class(__a					)

         lowercase__							:       Union[str, Any]										=   model.generate(__a      ,  attention_mask=__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       Optional[int]										=   jit(model.generate					)
         lowercase__							:       Optional[Any]										=   jit_generate(__a      ,  attention_mask=__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)


       def _lowerCAmelCase(		self					)    ->    int:
        lowercase__					,				lowercase__					,				lowercase__					,				lowercase__							:       Tuple										=   self._get_input_ids_and_config()

        # pad attention mask on the left
        lowercase__							:       str										=   attention_mask.at[(0, 0)].set(0					)

        lowercase__							:       Optional[Any]										=   2
        lowercase__							:       Union[str, Any]										=   max_length

        for model_class in self.all_generative_model_classes:
         lowercase__							:       List[Any]										=   model_class(__a					)

         lowercase__							:       str										=   model.generate(__a      ,  attention_mask=__a					).sequences
         self.assertEqual(generation_outputs.shape[-1]      ,  __a					)

         lowercase__							:       List[Any]										=   jit(model.generate					)
         lowercase__							:       Tuple										=   jit_generate(__a      ,  attention_mask=__a					).sequences

         self.assertListEqual(generation_outputs.tolist()      ,  jit_generation_outputs.tolist()					)



@require_flax
class      UpperCAmelCase    (		unittest.TestCase   ):





       '''simple docstring'''



       def _lowerCAmelCase(		self					)    ->    str:
        lowercase__							:       List[str]										=   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''					)
        lowercase__							:       str										=   FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''					)

        lowercase__							:       List[str]										=   '''Hello world'''
        lowercase__							:       Optional[Any]										=   tokenizer(__a      ,  return_tensors='''np'''					).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(__a      ,  '''do_samples'''					):
         model.generate(__a      ,  do_samples=__a					)

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(__a      ,  '''foo'''					):
         lowercase__							:       str										=   {'''foo''': '''bar'''}
         model.generate(__a      ,  **__a					)



"""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        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['vqvae']



		def __init__(   self							,						__a							,						__a							,						__a							,						__a							,						)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a							,						mel=__a							,						vqvae=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 50 if isinstance(self.scheduler							,						__a) else 10_00



		@torch.no_grad()
		def __call__(   self							,						__a = 1							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = 0							,						__a = None							,						__a = None							,						__a=True							,						)			->			Union[
		    Union[AudioPipelineOutput, ImagePipelineOutput],
		    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
		]:






							'''simple docstring'''
							_UpperCamelCase									=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size) == int:
												_UpperCamelCase									=    (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
												_UpperCamelCase									=    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							,						)
							_UpperCamelCase									=    noise
							_UpperCamelCase									=    None

							if audio_file is not None or raw_audio is not None:
												self.mel.load_audio(__a							,						__a)
												_UpperCamelCase									=    self.mel.audio_slice_to_image(__a)
												_UpperCamelCase									=    np.frombuffer(input_image.tobytes()							,						dtype='''uint8''').reshape(
												    (input_image.height, input_image.width))
												_UpperCamelCase									=    (input_image / 2_55) * 2 - 1
												_UpperCamelCase									=    torch.tensor(input_image[np.newaxis, :, :]							,						dtype=torch.float).to(self.device)

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

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

												_UpperCamelCase									=    (
												    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
												)
												_UpperCamelCase									=    int(mask_start_secs * pixels_per_second)
												_UpperCamelCase									=    int(mask_end_secs * pixels_per_second)
												_UpperCamelCase									=    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):
																	_UpperCamelCase									=    self.unet(__a							,						__a							,						__a)['''sample''']
												else:
																	_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']

												if isinstance(self.scheduler							,						__a):
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						eta=__a							,						generator=__a							,						)['''prev_sample''']
												else:
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						generator=__a							,						)['''prev_sample''']

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

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

							_UpperCamelCase									=    (images / 2 + 0.5).clamp(0							,						1)
							_UpperCamelCase									=    images.cpu().permute(0							,						2							,						3							,						1).numpy()
							_UpperCamelCase									=    (images * 2_55).round().astype('''uint8''')
							_UpperCamelCase									=    list(
							    (Image.fromarray(_[:, :, 0]) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__a							,						mode='''RGB''').convert('''L''') for _ in images))

							_UpperCamelCase									=    [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 		UpperCAmelCase				(   self							,						__a							,						__a = 50)			->			np.ndarray:






							'''simple docstring'''
							assert isinstance(self.scheduler							,						__a)
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    np.array(
							    [np.frombuffer(image.tobytes()							,						dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
							_UpperCamelCase									=    (sample / 2_55) * 2 - 1
							_UpperCamelCase									=    torch.Tensor(__a).to(self.device)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps							,						(0,))):
												_UpperCamelCase									=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
												_UpperCamelCase									=    self.scheduler.alphas_cumprod[t]
												_UpperCamelCase									=    (
												    self.scheduler.alphas_cumprod[prev_timestep]
												    if prev_timestep >= 0
												    else self.scheduler.final_alpha_cumprod
												)
												_UpperCamelCase									=    1 - alpha_prod_t
												_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']
												_UpperCamelCase									=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
												_UpperCamelCase									=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
												_UpperCamelCase									=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




		@staticmethod
		def 		UpperCAmelCase				(   __a							,						__a							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    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)






