






"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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


class      _lowercase (     lowerCAmelCase						):


      def __init__(    self				:	List[str]							,   a				:	Union[str, Any]							,   a				:	Union[str, Any]						):





            """simple docstring"""







            super().__init__()
            self.register_modules(unet=a							,   scheduler=a						)







      @torch.no_grad()
      def __call__(    self				:	Any							,   a				:	int = 1							,   a				:	int = 1_0_0							,   a				:	Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   a				:	Optional[float] = None							,   a				:	bool = True							,   ):





            """simple docstring"""







            if audio_length_in_s is None:
                  __snake_case      :    str  			=self.unet.config.sample_size / self.unet.config.sample_rate

            __snake_case      :    Any  			=audio_length_in_s * self.unet.config.sample_rate

            __snake_case      :    Optional[Any]  			=2 ** len(self.unet.up_blocks						)
            if sample_size < 3 * down_scale_factor:
                  raise ValueError(
                      f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
                      f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''						)

            __snake_case      :    Optional[Any]  			=int(a						)
            if sample_size % down_scale_factor != 0:
                  __snake_case      :    int  			=(
                      (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                  ) * down_scale_factor
                  logger.info(
                      f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
                      f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
                      ''' process.'''						)
            __snake_case      :    Any  			=int(a						)

            __snake_case      :    Union[str, Any]  			=next(iter(self.unet.parameters()						)						).dtype
            __snake_case      :    Dict  			=(batch_size, self.unet.config.in_channels, sample_size)
            if isinstance(a							,   a						) and len(a						) != batch_size:
                  raise ValueError(
                      f'''You have passed a list of generators of length {len(a						)}, but requested an effective batch'''
                      f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

            __snake_case      :    Any  			=randn_tensor(a							,   generator=a							,   device=self.device							,   dtype=a						)

            # set step values
            self.scheduler.set_timesteps(a							,   device=audio.device						)
            __snake_case      :    List[Any]  			=self.scheduler.timesteps.to(a						)

            for t in self.progress_bar(self.scheduler.timesteps						):
                  # 1. predict noise model_output
                  __snake_case      :    List[Any]  			=self.unet(a							,   a						).sample

                  # 2. compute previous image: x_t -> t_t-1
                  __snake_case      :    Tuple  			=self.scheduler.step(a							,   a							,   a						).prev_sample

            __snake_case      :    Any  			=audio.clamp(-1							,   1						).float().cpu().numpy()

            __snake_case      :    Tuple  			=audio[:, :, :original_sample_size]

            if not return_dict:
                  return (audio,)

            return AudioPipelineOutput(audios=a						)










"""simple docstring"""





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     List[str]								=   MgpstrTokenizer
      _a   :     int								=   False
      _a   :     List[str]								=   {}
      _a   :     Optional[Any]								=   False


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            super().setUp()

            # fmt: off
            __snake_case      :    Union[str, Any]  			=['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
            # fmt: on
            __snake_case      :    Optional[Any]  			=dict(zip(a							,   range(len(a						)						)						)						)

            __snake_case      :    int  			=os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES['''vocab_file''']						)
            with open(self.vocab_file							,   '''w'''							,   encoding='''utf-8'''						) as fp:
                  fp.write(json.dumps(a						) + '''\n'''						)


      def _UpperCamelCase						(    self				:	Any							,   **a				:	int						):





            """simple docstring"""







            return MgpstrTokenizer.from_pretrained(self.tmpdirname							,   **a						)


      def _UpperCamelCase						(    self				:	int							,   a				:	Any						):





            """simple docstring"""







            __snake_case      :    Dict  			='''tester'''
            __snake_case      :    str  			='''tester'''
            return input_text, output_text


      @unittest.skip('''MGP-STR always lower cases letters.'''						)
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            pass


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.get_tokenizers(do_lower_case=a						)
            for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
                        __snake_case      :    List[Any]  			='''[SPECIAL_TOKEN]'''

                        tokenizer.add_special_tokens({'''cls_token''': special_token}						)
                        __snake_case      :    Tuple  			=tokenizer.encode([special_token]							,   add_special_tokens=a						)
                        self.assertEqual(len(a						)							,   1						)

                        __snake_case      :    int  			=tokenizer.decode(a							,   skip_special_tokens=a						)
                        self.assertTrue(special_token not in decoded						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
                        __snake_case			,				__snake_case      :    Tuple  			=self.get_input_output_texts(a						)

                        __snake_case      :    int  			=tokenizer.tokenize(a						)
                        __snake_case      :    str  			=tokenizer.convert_tokens_to_ids(a						)
                        __snake_case      :    Union[str, Any]  			=tokenizer.encode(a							,   add_special_tokens=a						)
                        self.assertListEqual(a							,   a						)

                        __snake_case      :    Tuple  			=tokenizer.convert_ids_to_tokens(a						)
                        self.assertNotEqual(len(a						)							,   0						)
                        __snake_case      :    Optional[Any]  			=tokenizer.decode(a						)
                        self.assertIsInstance(a							,   a						)

                        self.assertEqual(text_a.replace(''' '''							,   ''''''						)							,   a						)


      @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''						)
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            pass







      @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''						)
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            pass


