




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_		:					Any          =  get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase_		:					int          =  250004
UpperCAmelCase_		:					List[str]          =  250020




@require_sentencepiece
@require_tokenizers
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       MBartTokenizer
						UpperCamelCase      =       MBartTokenizerFast
						UpperCamelCase      =       True
						UpperCamelCase      =       True

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__						:						List[str]										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)
										tokenizer.save_pretrained(self.tmpdirname			)

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Any										=MBartTokenizer(__snake_case     ,			keep_accents=__snake_case			)

										__magic_name__						:						int										=tokenizer.tokenize("""This is a test"""			)
										self.assertListEqual(__snake_case     ,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__snake_case			)     ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,			)

										__magic_name__						:						Optional[int]										=tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
										self.assertListEqual(
										    __snake_case     ,			[
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """9""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """é""",
										        """.""",
										    ]     ,			)
										__magic_name__						:						Optional[Any]										=tokenizer.convert_tokens_to_ids(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]     ,			)

										__magic_name__						:						Any										=tokenizer.convert_ids_to_tokens(__snake_case			)
										self.assertListEqual(
										    __snake_case     ,			[
										        SPIECE_UNDERLINE + """I""",
										        SPIECE_UNDERLINE + """was""",
										        SPIECE_UNDERLINE + """b""",
										        """or""",
										        """n""",
										        SPIECE_UNDERLINE + """in""",
										        SPIECE_UNDERLINE + """""",
										        """<unk>""",
										        """2""",
										        """0""",
										        """0""",
										        """0""",
										        """,""",
										        SPIECE_UNDERLINE + """and""",
										        SPIECE_UNDERLINE + """this""",
										        SPIECE_UNDERLINE + """is""",
										        SPIECE_UNDERLINE + """f""",
										        """al""",
										        """s""",
										        """<unk>""",
										        """.""",
										    ]     ,			)


						def      A__    (		self :Tuple			):
										'''simple docstring'''

										if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

										__magic_name__						:						Tuple										=(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
																		__magic_name__						:						List[Any]										=self.rust_tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)
																		__magic_name__						:						List[str]										=self.tokenizer_class.from_pretrained(__snake_case     ,			**__snake_case			)

																		__magic_name__						:						Optional[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it save with the same files + the tokenizer.json file for the fast one
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)
																		__magic_name__						:						int										=tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f			)
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						List[Any]										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)
																						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=True
																		__magic_name__						:						List[str]										=tempfile.mkdtemp()

																		__magic_name__						:						Optional[int]										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						Dict										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it save with the same files
																		self.assertSequenceEqual(__snake_case     ,			__snake_case			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						Any										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						int										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)

																		shutil.rmtree(__snake_case			)

																		# Save tokenizer rust, legacy_format=False
																		__magic_name__						:						List[Any]										=tempfile.mkdtemp()

																		__magic_name__						:						Dict										=tokenizer_r.save_pretrained(__snake_case     ,			legacy_format=__snake_case			)
																		__magic_name__						:						List[str]										=tokenizer_p.save_pretrained(__snake_case			)

																		# Checks it saved the tokenizer.json file
																		self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files			)			)

																		# Checks everything loads correctly in the same way
																		__magic_name__						:						str										=tokenizer_r.from_pretrained(__snake_case			)
																		__magic_name__						:						Optional[int]										=tokenizer_p.from_pretrained(__snake_case			)

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(__snake_case     ,			__snake_case			)			)

																		shutil.rmtree(__snake_case			)




@require_torch
@require_sentencepiece
@require_tokenizers
class 		__A   (      unittest.TestCase      ):
						UpperCamelCase      =       """facebook/mbart-large-en-ro"""
						UpperCamelCase      =       [
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						UpperCamelCase      =       [
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						UpperCamelCase      =       [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]

						@classmethod
						def      A__    (		cls :str			):
										'''simple docstring'''

										__magic_name__						:						MBartTokenizer										=MBartTokenizer.from_pretrained(
										    cls.checkpoint_name     ,			src_lang="""en_XX"""     ,			tgt_lang="""ro_RO"""			)
										__magic_name__						:						Any										=1
										return cls

						def      A__    (		self :Any			):
										'''simple docstring'''

										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]     ,			25_00_01			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]     ,			25_00_04			)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]     ,			25_00_20			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										self.assertIn(__snake_case     ,			self.tokenizer.all_special_ids			)
										__magic_name__						:						Union[str, Any]										=[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
										__magic_name__						:						Optional[int]										=self.tokenizer.decode(__snake_case     ,			skip_special_tokens=__snake_case			)
										__magic_name__						:						List[str]										=self.tokenizer.decode(generated_ids[1:]     ,			skip_special_tokens=__snake_case			)
										self.assertEqual(__snake_case     ,			__snake_case			)
										self.assertNotIn(self.tokenizer.eos_token     ,			__snake_case			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						str										=["""this is gunna be a long sentence """ * 20]
										assert isinstance(src_text[0]     ,			__snake_case			)
										__magic_name__						:						Dict										=10
										__magic_name__						:						Optional[Any]										=self.tokenizer(__snake_case     ,			max_length=__snake_case     ,			truncation=__snake_case			).input_ids[0]
										self.assertEqual(ids[-2]     ,			2			)
										self.assertEqual(ids[-1]     ,			__snake_case			)
										self.assertEqual(len(__snake_case			)     ,			__snake_case			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]			)     ,			[25_00_26, 25_00_01]			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=tempfile.mkdtemp()
										__magic_name__						:						Dict										=self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__snake_case			)
										__magic_name__						:						Dict										=MBartTokenizer.from_pretrained(__snake_case			)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,			__snake_case			)

						@require_torch
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.tokenizer(self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			return_tensors="""pt"""			)
										__magic_name__						:						str										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

						@require_torch
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.tokenizer(
										    self.src_text     ,			text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=len(self.expected_src_tokens			)     ,			return_tensors="""pt"""     ,			)

										__magic_name__						:						Any										=shift_tokens_right(batch["""labels"""]     ,			self.tokenizer.pad_token_id			)

										self.assertIsInstance(__snake_case     ,			__snake_case			)

										self.assertEqual((2, 14)     ,			batch.input_ids.shape			)
										self.assertEqual((2, 14)     ,			batch.attention_mask.shape			)
										__magic_name__						:						int										=batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens     ,			__snake_case			)
										self.assertEqual(2     ,			batch.decoder_input_ids[0, -1]			)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens     ,			[]			)
										self.assertEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id, EN_CODE]			)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.tokenizer(self.src_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=3     ,			return_tensors="""pt"""			)
										__magic_name__						:						Tuple										=self.tokenizer(
										    text_target=self.tgt_text     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=10     ,			return_tensors="""pt"""			)
										__magic_name__						:						List[Any]										=targets["""input_ids"""]
										__magic_name__						:						List[str]										=shift_tokens_right(__snake_case     ,			self.tokenizer.pad_token_id			)

										self.assertEqual(batch.input_ids.shape[1]     ,			3			)
										self.assertEqual(batch.decoder_input_ids.shape[1]     ,			10			)


						@require_torch
						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.tokenizer._build_translation_inputs(
										    """A test"""     ,			return_tensors="""pt"""     ,			src_lang="""en_XX"""     ,			tgt_lang="""ar_AR"""			)

										self.assertEqual(
										    nested_simplify(__snake_case			)     ,			{
										        # A, test, EOS, en_XX
										        """input_ids""": [[62, 30_34, 2, 25_00_04]],
										        """attention_mask""": [[1, 1, 1, 1]],
										        # ar_AR
										        """forced_bos_token_id""": 25_00_01,
										    }     ,			)






"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
		from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


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

UpperCamelCase_       :    List[Any]						      =			256



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["melgan"]




			def __init__(					self    :       Dict							, _snake_case    :       SpectrogramNotesEncoder							, _snake_case    :       SpectrogramContEncoder							, _snake_case    :       TaFilmDecoder							, _snake_case    :       DDPMScheduler							, _snake_case    :       OnnxRuntimeModel if is_onnx_available() else Any							, )   ->			None:







								"""simple docstring"""
								super().__init__()

								# From MELGAN
								A_								=    math.log(1e-5						)  # Matches MelGAN training.
								A_								=    4.0  # Largest value for most examples
								A_								=    128

								self.register_modules(
								    notes_encoder=_snake_case							, continuous_encoder=_snake_case							, decoder=_snake_case							, scheduler=_snake_case							, melgan=_snake_case							, )




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       str=(-1.0, 1.0)							, _snake_case    :       int=False						)   ->			str:







								"""simple docstring"""
								A_			,     A_								=    output_range
								if clip:
													A_								=    torch.clip(_snake_case							, self.min_value							, self.max_value						)
								# Scale to [0, 1].
								A_								=    (features - self.min_value) / (self.max_value - self.min_value)
								# Scale to [min_out, max_out].
								return zero_one * (max_out - min_out) + min_out




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]=(-1.0, 1.0)							, _snake_case    :       List[str]=False						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_								=    input_range
								A_								=    torch.clip(_snake_case							, _snake_case							, _snake_case						) if clip else outputs
								# Scale to [0, 1].
								A_								=    (outputs - min_out) / (max_out - min_out)
								# Scale to [self.min_value, self.max_value].
								return zero_one * (self.max_value - self.min_value) + self.min_value




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    input_tokens > 0
								A_			,     A_								=    self.notes_encoder(
								    encoder_input_tokens=_snake_case							, encoder_inputs_mask=_snake_case						)

								A_			,     A_								=    self.continuous_encoder(
								    encoder_inputs=_snake_case							, encoder_inputs_mask=_snake_case						)

								return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       int							, _snake_case    :       Tuple						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    noise_time
								if not torch.is_tensor(_snake_case						):
													A_								=    torch.tensor([timesteps]							, dtype=torch.long							, device=input_tokens.device						)
								elif torch.is_tensor(_snake_case						) and len(timesteps.shape						) == 0:
													A_								=    timesteps[None].to(input_tokens.device						)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								A_								=    timesteps * torch.ones(input_tokens.shape[0]							, dtype=timesteps.dtype							, device=timesteps.device						)

								A_								=    self.decoder(
								    encodings_and_masks=_snake_case							, decoder_input_tokens=_snake_case							, decoder_noise_time=_snake_case						)
								return logits






			@torch.no_grad()
			def __call__(					self    :       List[Any]							, _snake_case    :       List[List[int]]							, _snake_case    :       Optional[torch.Generator] = None							, _snake_case    :       int = 100							, _snake_case    :       bool = True							, _snake_case    :       str = "numpy"							, _snake_case    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							, _snake_case    :       int = 1							, )   ->			Union[AudioPipelineOutput, Tuple]:







								"""simple docstring"""
								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_snake_case							, _snake_case						) or callback_steps <= 0)
								):
													raise ValueError(
													    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
													    F' {type(_snake_case						)}.'						)

								A_								=    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							, dtype=np.floataa						)
								A_								=    np.zeros([1, 0, self.n_dims]							, np.floataa						)
								A_								=    torch.ones((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)

								for i, encoder_input_tokens in enumerate(_snake_case						):
													if i == 0:
																		A_								=    torch.from_numpy(pred_mel[:1].copy()						).to(
																		    device=self.device							, dtype=self.decoder.dtype						)
																		# The first chunk has no previous context.
																		A_								=    torch.zeros((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)
													else:
																		# The full song pipeline does not feed in a context feature, so the mask
																		# will be all 0s after the feature converter. Because we know we're
																		# feeding in a full context chunk from the previous prediction, set it
																		# to all 1s.
																		A_								=    ones

													A_								=    self.scale_features(
													    _snake_case							, output_range=[-1.0, 1.0]							, clip=_snake_case						)

													A_								=    self.encode(
													    input_tokens=torch.IntTensor([encoder_input_tokens]						).to(device=self.device						)							, continuous_inputs=_snake_case							, continuous_mask=_snake_case							, )

													# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
													A_								=    randn_tensor(
													    shape=encoder_continuous_inputs.shape							, generator=_snake_case							, device=self.device							, dtype=self.decoder.dtype							, )

													# set step values
													self.scheduler.set_timesteps(_snake_case						)

													# Denoising diffusion loop
													for j, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																		A_								=    self.decode(
																		    encodings_and_masks=_snake_case							, input_tokens=_snake_case							, noise_time=t / self.scheduler.config.num_train_timesteps							, )

																		# Compute previous output: x_t -> x_t-1
																		A_								=    self.scheduler.step(_snake_case							, _snake_case							, _snake_case							, generator=_snake_case						).prev_sample

													A_								=    self.scale_to_features(_snake_case							, input_range=[-1.0, 1.0]						)
													A_								=    mel[:1]
													A_								=    mel.cpu().float().numpy()

													A_								=    np.concatenate([full_pred_mel, pred_mel[:1]]							, axis=1						)

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																		callback(_snake_case							, _snake_case						)

													logger.info("Generated segment"							, _snake_case						)

								if output_type == "numpy" and not is_onnx_available():
													raise ValueError(
													    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."						)
								elif output_type == "numpy" and self.melgan is None:
													raise ValueError(
													    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."						)

								if output_type == "numpy":
													A_								=    self.melgan(input_features=full_pred_mel.astype(np.floataa						)						)
								else:
													A_								=    full_pred_mel

								if not return_dict:
													return (output,)

								return AudioPipelineOutput(audios=_snake_case						)




