




'''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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Tuple   		=			MgpstrTokenizer
       snake_case__    :     Optional[int]   		=			False
       snake_case__    :     Tuple   		=			{}
       snake_case__    :     str   		=			False




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            super().setUp()

            # fmt: off
            __lowerCamelCase						:       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
            __lowerCamelCase						:       Any				   =				dict(zip(A__	,    range(len(A__					)					)					)					)

            __lowerCamelCase						:       str				   =				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 					a_					(					self      : int	,    **A__      : List[Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : List[str]	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				"""tester"""
            __lowerCamelCase						:       List[str]				   =				"""tester"""
            return input_text, output_text




       @unittest.skip("""MGP-STR always lower cases letters."""					)
       def 					a_					(					self      : int					):


            """simple docstring"""







            pass




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				self.get_tokenizers(do_lower_case=A__					)
            for tokenizer in tokenizers:
                 with self.subTest(f"{tokenizer.__class__.__name__}"					):
                      __lowerCamelCase						:       int				   =				"""[SPECIAL_TOKEN]"""

                      tokenizer.add_special_tokens({"""cls_token""": special_token}					)
                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer.encode([special_token]	,    add_special_tokens=A__					)
                      self.assertEqual(len(A__					)	,    1					)

                      __lowerCamelCase						:       List[Any]				   =				tokenizer.decode(A__	,    skip_special_tokens=A__					)
                      self.assertTrue(special_token not in decoded					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.get_tokenizers()
            for tokenizer in tokenizers:
                 with self.subTest(f"{tokenizer.__class__.__name__}"					):
                      __lowerCamelCase						:       Any				   =				self.get_input_output_texts(A__					)

                      __lowerCamelCase						:       Dict				   =				tokenizer.tokenize(A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer.convert_tokens_to_ids(A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
                      self.assertListEqual(A__	,    A__					)

                      __lowerCamelCase						:       Tuple				   =				tokenizer.convert_ids_to_tokens(A__					)
                      self.assertNotEqual(len(A__					)	,    0					)
                      __lowerCamelCase						:       str				   =				tokenizer.decode(A__					)
                      self.assertIsInstance(A__	,    A__					)

                      self.assertEqual(text_a.replace(""" """	,    """"""					)	,    A__					)




       @unittest.skip("""MGP-STR tokenizer only handles one sequence."""					)
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            pass




       @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""					)
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            pass











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







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




       @torch.no_grad()
       def __call__(					self      : List[str]	,    A__      : int = 1	,    A__      : int = 100	,    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:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[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}."					)

            __lowerCamelCase						:       Any				   =				int(A__					)
            if sample_size % down_scale_factor != 0:
                 __lowerCamelCase						:       Optional[Any]				   =				(
                     (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."""					)
            __lowerCamelCase						:       Dict				   =				int(A__					)

            __lowerCamelCase						:       Optional[int]				   =				next(iter(self.unet.parameters()					)					).dtype
            __lowerCamelCase						:       Optional[Any]				   =				(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."					)

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

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

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

                 # 2. compute previous image: x_t -> t_t-1
                 __lowerCamelCase						:       Dict				   =				self.scheduler.step(A__	,    A__	,    A__					).prev_sample

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



