


"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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						=      get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
	from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__UpperCamelCase						=      256047
__UpperCamelCase						=      256145
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[Any]								=     NllbTokenizer
		SCREAMING_SNAKE_CASE_						:     Dict								=     NllbTokenizerFast
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     True
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     True
		SCREAMING_SNAKE_CASE_						:     List[str]								=     {}







		def 							__A						(	self				)     ->							List[Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								SCREAMING_SNAKE_CASE													=					NllbTokenizer(lowerCAmelCase__						,       keep_accents=lowerCAmelCase__				)
								tokenizer.save_pretrained(self.tmpdirname				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					NllbTokenizer(lowerCAmelCase__						,       keep_accents=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					tokenizer.tokenize('This is a test'				)
								self.assertListEqual(lowerCAmelCase__						,       ['▁This', '▁is', '▁a', '▁t', 'est']				)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)						,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,       )

								SCREAMING_SNAKE_CASE													=					tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        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',
								        '.',
								    ]						,       )
								SCREAMING_SNAKE_CASE													=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]						,       )

								SCREAMING_SNAKE_CASE													=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        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				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																				SCREAMING_SNAKE_CASE													=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__						,       **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					self.tokenizer_class.from_pretrained(lowerCAmelCase__						,       **lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.save_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.save_pretrained(lowerCAmelCase__				)

																				# 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				)				)
																				SCREAMING_SNAKE_CASE													=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
																				self.assertSequenceEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.from_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.from_pretrained(lowerCAmelCase__				)

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

																				shutil.rmtree(lowerCAmelCase__				)

																				# Save tokenizer rust, legacy_format=True
																				SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.save_pretrained(lowerCAmelCase__						,       legacy_format=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.save_pretrained(lowerCAmelCase__				)

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

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.from_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.from_pretrained(lowerCAmelCase__				)

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

																				shutil.rmtree(lowerCAmelCase__				)

																				# Save tokenizer rust, legacy_format=False
																				SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.save_pretrained(lowerCAmelCase__						,       legacy_format=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.save_pretrained(lowerCAmelCase__				)

																				# 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
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.from_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.from_pretrained(lowerCAmelCase__				)

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

																				shutil.rmtree(lowerCAmelCase__				)







		@require_torch
		def 							__A						(	self				)     ->							Any:
								if not self.test_seqaseq:
														return

								SCREAMING_SNAKE_CASE													=					self.get_tokenizers()
								for tokenizer in tokenizers:
														with self.subTest(F'{tokenizer.__class__.__name__}'				):
																				# Longer text that will definitely require truncation.
																				SCREAMING_SNAKE_CASE													=					[
																				    ' 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.',
																				]
																				SCREAMING_SNAKE_CASE													=					[
																				    '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.',
																				]
																				try:
																										SCREAMING_SNAKE_CASE													=					tokenizer.prepare_seqaseq_batch(
																										    src_texts=lowerCAmelCase__						,       tgt_texts=lowerCAmelCase__						,       max_length=3						,       max_target_length=10						,       return_tensors='pt'						,       src_lang='eng_Latn'						,       tgt_lang='ron_Latn'						,       )
																				except NotImplementedError:
																										return
																				self.assertEqual(batch.input_ids.shape[1]						,       3				)
																				self.assertEqual(batch.labels.shape[1]						,       10				)
																				# max_target_length will default to max_length if not specified
																				SCREAMING_SNAKE_CASE													=					tokenizer.prepare_seqaseq_batch(
																				    lowerCAmelCase__						,       tgt_texts=lowerCAmelCase__						,       max_length=3						,       return_tensors='pt'				)
																				self.assertEqual(batch.input_ids.shape[1]						,       3				)
																				self.assertEqual(batch.labels.shape[1]						,       3				)

																				SCREAMING_SNAKE_CASE													=					tokenizer.prepare_seqaseq_batch(
																				    src_texts=lowerCAmelCase__						,       max_length=3						,       max_target_length=10						,       return_tensors='pt'				)
																				self.assertEqual(batch_encoder_only.input_ids.shape[1]						,       3				)
																				self.assertEqual(batch_encoder_only.attention_mask.shape[1]						,       3				)
																				self.assertNotIn('decoder_input_ids'						,       lowerCAmelCase__				)







		@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'				)
		def 							__A						(	self				)     ->							Tuple:
								pass







		def 							__A						(	self				)     ->							str:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																				SCREAMING_SNAKE_CASE													=					[AddedToken('<special>'						,       lstrip=lowerCAmelCase__				)]

																				SCREAMING_SNAKE_CASE													=					self.rust_tokenizer_class.from_pretrained(
																				    lowerCAmelCase__						,       additional_special_tokens=lowerCAmelCase__						,       **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.encode('Hey this is a <special> token'				)

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.encode('<special>'						,       add_special_tokens=lowerCAmelCase__				)[0]

																				self.assertTrue(special_token_id in r_output				)

																				if self.test_slow_tokenizer:
																										SCREAMING_SNAKE_CASE													=					self.rust_tokenizer_class.from_pretrained(
																										    lowerCAmelCase__						,       additional_special_tokens=lowerCAmelCase__						,       **lowerCAmelCase__						,       )
																										SCREAMING_SNAKE_CASE													=					self.tokenizer_class.from_pretrained(
																										    lowerCAmelCase__						,       additional_special_tokens=lowerCAmelCase__						,       **lowerCAmelCase__				)

																										SCREAMING_SNAKE_CASE													=					tokenizer_p.encode('Hey this is a <special> token'				)

																										SCREAMING_SNAKE_CASE													=					tokenizer_cr.encode('Hey this is a <special> token'				)

																										self.assertEqual(lowerCAmelCase__						,       lowerCAmelCase__				)
																										self.assertEqual(lowerCAmelCase__						,       lowerCAmelCase__				)
																										self.assertTrue(special_token_id in p_output				)
																										self.assertTrue(special_token_id in cr_output				)





@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     """facebook/nllb-200-distilled-600M"""
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     [
		    """ 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.""",
		]
		SCREAMING_SNAKE_CASE_						:     Any								=     [
		    """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.""",
		]
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     [
		    2_5_6_0_4_7,
		    1_6_2_9_7,
		    1_3_4_4_0_8,
		    8_1_6_5,
		    2_4_8_0_6_6,
		    1_4_7_3_4,
		    9_5_0,
		    1_1_3_5,
		    1_0_5_7_2_1,
		    3_5_7_3,
		    8_3,
		    2_7_3_5_2,
		    1_0_8,
		    4_9_4_8_6,
		    2,
		]







		@classmethod
		def 							__A						(	cls				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					NllbTokenizer.from_pretrained(
								    cls.checkpoint_name						,       src_lang='eng_Latn'						,       tgt_lang='ron_Latn'				)
								SCREAMING_SNAKE_CASE													=					1
								return cls







		def 							__A						(	self				)     ->							str:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']						,       256_001				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']						,       256_002				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']						,       256_057				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
								self.assertListEqual(self.expected_src_tokens						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								self.assertIn(lowerCAmelCase__						,       self.tokenizer.all_special_ids				)
								# fmt: off
								SCREAMING_SNAKE_CASE													=					[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
								# fmt: on

								SCREAMING_SNAKE_CASE													=					self.tokenizer.decode(lowerCAmelCase__						,       skip_special_tokens=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.tokenizer.decode(generated_ids[1:]						,       skip_special_tokens=lowerCAmelCase__				)
								self.assertEqual(lowerCAmelCase__						,       lowerCAmelCase__				)
								self.assertNotIn(self.tokenizer.eos_token						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					10
								SCREAMING_SNAKE_CASE													=					self.tokenizer(lowerCAmelCase__						,       max_length=lowerCAmelCase__						,       truncation=lowerCAmelCase__				).input_ids[0]
								self.assertEqual(ids[-1]						,       2				)
								self.assertEqual(ids[0]						,       lowerCAmelCase__				)
								self.assertEqual(len(lowerCAmelCase__				)						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']				)						,       [256_203, 3]				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE													=					self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					NllbTokenizer.from_pretrained(lowerCAmelCase__				)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,       lowerCAmelCase__				)







		@require_torch
		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    self.src_text						,       text_target=self.tgt_text						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       max_length=len(self.expected_src_tokens				)						,       return_tensors='pt'						,       )
								SCREAMING_SNAKE_CASE													=					shift_tokens_right(
								    batch['labels']						,       self.tokenizer.pad_token_id						,       self.tokenizer.lang_code_to_id['ron_Latn']				)

								self.assertIsInstance(lowerCAmelCase__						,       lowerCAmelCase__				)

								self.assertEqual((2, 15)						,       batch.input_ids.shape				)
								self.assertEqual((2, 15)						,       batch.attention_mask.shape				)
								SCREAMING_SNAKE_CASE													=					batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens						,       lowerCAmelCase__				)
								self.assertEqual(lowerCAmelCase__						,       batch.decoder_input_ids[0, 0]				)  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens						,       [EN_CODE]				)
								self.assertEqual(self.tokenizer.suffix_tokens						,       [self.tokenizer.eos_token_id]				)







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					self.tokenizer(self.src_text						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       max_length=3						,       return_tensors='pt'				)
								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    text_target=self.tgt_text						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       max_length=10						,       return_tensors='pt'				)
								SCREAMING_SNAKE_CASE													=					targets['input_ids']
								SCREAMING_SNAKE_CASE													=					shift_tokens_right(
								    lowerCAmelCase__						,       self.tokenizer.pad_token_id						,       decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]						,       )

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







		@require_torch
		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					self.tokenizer._build_translation_inputs(
								    'A test'						,       return_tensors='pt'						,       src_lang='eng_Latn'						,       tgt_lang='fra_Latn'				)

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       {
								        # A, test, EOS, en_XX
								        'input_ids': [[256_047, 70, 7_356, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 256_057,
								    }						,       )







		@require_torch
		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					True
								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    'UN Chief says there is no military solution in Syria'						,       src_lang='eng_Latn'						,       tgt_lang='fra_Latn'				)
								self.assertEqual(
								    inputs.input_ids						,       [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]				)

								SCREAMING_SNAKE_CASE													=					False
								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    'UN Chief says there is no military solution in Syria'						,       src_lang='eng_Latn'						,       tgt_lang='fra_Latn'				)
								self.assertEqual(
								    inputs.input_ids						,       [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]				)






"""simple docstring"""




import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__UpperCamelCase						=      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						return torch.atana(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) / math.pi * 2


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						int:
						SCREAMING_SNAKE_CASE													=					torch.sin(t * math.pi / 2  ) ** 2
						SCREAMING_SNAKE_CASE													=					(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		pass





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							int:
								super().__init__()

								SCREAMING_SNAKE_CASE													=					DiffusionAttnUnetaD(lowerCAmelCase__						,       n_attn_layers=4				)
								SCREAMING_SNAKE_CASE													=					deepcopy(self.diffusion				)
								SCREAMING_SNAKE_CASE													=					torch.quasirandom.SobolEngine(1						,       scramble=lowerCAmelCase__				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['url']
						os.system(F'wget {url} ./'  )

						return F'./{model_name}.ckpt'


__UpperCamelCase						=      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
__UpperCamelCase						=      {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__UpperCamelCase						=      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
__UpperCamelCase						=      {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__UpperCamelCase						=      {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

__UpperCamelCase						=      {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[Any]:
						if name.startswith('skip'  ):
												return name.replace('skip' ,			RES_CONV_MAP['skip']  )

						# name has to be of format main.{digit}
						if not name.startswith('main.'  ):
												raise ValueError(F'ResConvBlock error with {name}'  )

						return name.replace(name[:6] ,			RES_CONV_MAP[name[:6]]  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						for key, value in ATTN_MAP.items():
												if name.startswith(SCREAMING_SNAKE_CASE_  ) and not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		return name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												elif name.startswith(SCREAMING_SNAKE_CASE_  ):
																		return [name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for v in value]
						raise ValueError(F'Attn error with {name}'  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple=13  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					input_string

						if string.split('.'  )[0] == "timestep_embed":
												return string.replace('timestep_embed' ,			'time_proj'  )

						SCREAMING_SNAKE_CASE													=					0
						if string.startswith('net.3.'  ):
												depth += 1
												SCREAMING_SNAKE_CASE													=					string[6:]
						elif string.startswith('net.'  ):
												SCREAMING_SNAKE_CASE													=					string[4:]

						while string.startswith('main.7.'  ):
												depth += 1
												SCREAMING_SNAKE_CASE													=					string[7:]

						if string.startswith('main.'  ):
												SCREAMING_SNAKE_CASE													=					string[5:]

						# mid block
						if string[:2].isdigit():
												SCREAMING_SNAKE_CASE													=					string[:2]
												SCREAMING_SNAKE_CASE													=					string[2:]
						else:
												SCREAMING_SNAKE_CASE													=					string[0]
												SCREAMING_SNAKE_CASE													=					string[1:]

						if depth == max_depth:
												SCREAMING_SNAKE_CASE													=					MID_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					'mid_block'
						elif depth > 0 and int(SCREAMING_SNAKE_CASE_  ) < 7:
												SCREAMING_SNAKE_CASE													=					DOWN_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'down_blocks.{depth}'
						elif depth > 0 and int(SCREAMING_SNAKE_CASE_  ) > 7:
												SCREAMING_SNAKE_CASE													=					UP_NUM_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'up_blocks.{max_depth - depth - 1}'
						elif depth == 0:
												SCREAMING_SNAKE_CASE													=					DEPTH_0_TO_LAYER[layer_num]
												SCREAMING_SNAKE_CASE													=					F'up_blocks.{max_depth - 1}' if int(SCREAMING_SNAKE_CASE_  ) > 3 else 'down_blocks.0'

						if not string_left.startswith('.'  ):
												raise ValueError(F'Naming error with {input_string} and string_left: {string_left}.'  )

						SCREAMING_SNAKE_CASE													=					string_left[1:]

						if "resnets" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_resconv_naming(SCREAMING_SNAKE_CASE_  )
						elif "attentions" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_attn_naming(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					new_string_left

						if not isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					prefix + '.' + new_layer + '.' + string_left
						else:
												SCREAMING_SNAKE_CASE													=					[prefix + '.' + new_layer + '.' + s for s in string_left]
						return new_string


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					{}
						for k, v in state_dict.items():
												if k.endswith('kernel'  ):
																		# up- and downsample layers, don't have trainable weights
																		continue

												SCREAMING_SNAKE_CASE													=					rename(SCREAMING_SNAKE_CASE_  )

												# check if we need to transform from Conv => Linear for attention
												if isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					transform_conv_attns(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												else:
																		SCREAMING_SNAKE_CASE													=					v

						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						str:
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												if len(v.shape  ) == 3:
																		# weight
																		SCREAMING_SNAKE_CASE													=					v[:, :, 0]
												else:
																		# bias
																		SCREAMING_SNAKE_CASE													=					v
						else:
												# qkv matrices
												SCREAMING_SNAKE_CASE													=					v.shape[0]
												SCREAMING_SNAKE_CASE													=					trippled_shape // 3
												for i in range(3  ):
																		if len(v.shape  ) == 3:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

						SCREAMING_SNAKE_CASE													=					args.model_path.split('/'  )[-1].split('.'  )[0]
						if not os.path.isfile(args.model_path  ):
												assert (
												    model_name == args.model_path
												), F'Make sure to provide one of the official model names {MODELS_MAP.keys()}'
												SCREAMING_SNAKE_CASE													=					download(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['sample_rate']
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['sample_size']

						SCREAMING_SNAKE_CASE													=					Object()
						SCREAMING_SNAKE_CASE													=					sample_size
						SCREAMING_SNAKE_CASE													=					sample_rate
						SCREAMING_SNAKE_CASE													=					0

						SCREAMING_SNAKE_CASE													=					UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ ,			sample_rate=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					diffusers_model.state_dict()

						SCREAMING_SNAKE_CASE													=					DiffusionUncond(SCREAMING_SNAKE_CASE_  )
						orig_model.load_state_dict(torch.load(args.model_path ,			map_location=SCREAMING_SNAKE_CASE_  )['state_dict']  )
						SCREAMING_SNAKE_CASE													=					orig_model.diffusion_ema.eval()
						SCREAMING_SNAKE_CASE													=					orig_model.state_dict()
						SCREAMING_SNAKE_CASE													=					rename_orig_weights(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					set(renamed_state_dict.keys()  ) - set(diffusers_state_dict.keys()  )
						SCREAMING_SNAKE_CASE													=					set(diffusers_state_dict.keys()  ) - set(renamed_state_dict.keys()  )

						assert len(SCREAMING_SNAKE_CASE_  ) == 0, F'Problem with {renamed_minus_diffusers}'
						assert all(k.endswith('kernel'  ) for k in list(SCREAMING_SNAKE_CASE_  )  ), F'Problem with {diffusers_minus_renamed}'

						for key, value in renamed_state_dict.items():
												assert (
												    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
												), F'Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'
												if key == "time_proj.weight":
																		SCREAMING_SNAKE_CASE													=					value.squeeze()

												SCREAMING_SNAKE_CASE													=					value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					1_00
						SCREAMING_SNAKE_CASE													=					33

						SCREAMING_SNAKE_CASE													=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.manual_seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					torch.randn([1, 2, config.sample_size] ,			generator=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.linspace(1 ,			0 ,			steps + 1 ,			device=SCREAMING_SNAKE_CASE_  )[:-1]
						SCREAMING_SNAKE_CASE													=					get_crash_schedule(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ ,			scheduler=SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.manual_seed(33  )
						SCREAMING_SNAKE_CASE													=					pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ ,			generator=SCREAMING_SNAKE_CASE_  ).audios

						SCREAMING_SNAKE_CASE													=					sampling.iplms_sample(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			{}  )
						SCREAMING_SNAKE_CASE													=					generated.clamp(-1 ,			1  )

						SCREAMING_SNAKE_CASE													=					(generated - audio).abs().sum()
						SCREAMING_SNAKE_CASE													=					(generated - audio).abs().max()

						if args.save:
												pipe.save_pretrained(args.checkpoint_path  )

						print('Diff sum' ,			SCREAMING_SNAKE_CASE_  )
						print('Diff max' ,			SCREAMING_SNAKE_CASE_  )

						assert diff_max < 1E-3, F'Diff max: {diff_max} is too much :-/'

						print(F'Conversion for {model_name} successful!'  )


if __name__ == "__main__":
	__UpperCamelCase						=      argparse.ArgumentParser()

	parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
	parser.add_argument(
	    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
	)
	parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
	__UpperCamelCase						=      parser.parse_args()

	main(args)


