

"""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		:       str						    =	{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 13_1072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
}





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							return torch.atana(UpperCamelCase__       ,							UpperCamelCase__				) / math.pi * 2





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   torch.sin(t * math.pi / 2				) ** 2
							snake_case_          =   (1 - sigma**2) ** 0.5
							return alpha_sigma_to_t(UpperCamelCase__       ,							UpperCamelCase__				)







class 			lowercase    (	lowercase_      ):
				pass







class 			lowercase    (	nn.Module      ):




				def __init__( self				,       snake_case	):
											super().__init__()

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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   MODELS_MAP[model_name]['url']
							os.system(F'''wget {url} ./'''				)

							return F'''./{model_name}.ckpt'''


_UpperCAmelCase		:       Optional[Any]						    =	{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
_UpperCAmelCase		:       int						    =	{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_UpperCAmelCase		:       Dict						    =	{
    """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		:       List[str]						    =	{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

_UpperCAmelCase		:       str						    =	{
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

_UpperCAmelCase		:       Dict						    =	{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							for key, value in ATTN_MAP.items():
														if name.startswith(UpperCamelCase__				) and not isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
																					return name.replace(UpperCamelCase__       ,							UpperCamelCase__				)
														elif name.startswith(UpperCamelCase__				):
																					return [name.replace(UpperCamelCase__       ,							UpperCamelCase__				) for v in value]
							raise ValueError(F'''Attn error with {name}'''				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=13				):







							'''simple docstring'''






							snake_case_          =   input_string

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

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

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

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

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

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

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

							snake_case_          =   string_left[1:]

							if "resnets" in new_layer:
														snake_case_          =   convert_resconv_naming(UpperCamelCase__				)
							elif "attentions" in new_layer:
														snake_case_          =   convert_attn_naming(UpperCamelCase__				)
														snake_case_          =   new_string_left

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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {}
							for k, v in state_dict.items():
														if k.endswith('kernel'				):
																					# up- and downsample layers, don't have trainable weights
																					continue

														snake_case_          =   rename(UpperCamelCase__				)

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

							return new_state_dict





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if len(UpperCamelCase__				) == 1:
														if len(v.shape				) == 3:
																					# weight
																					snake_case_          =   v[:, :, 0]
														else:
																					# bias
																					snake_case_          =   v
							else:
														# qkv matrices
														snake_case_          =   v.shape[0]
														snake_case_          =   trippled_shape // 3
														for i in range(3				):
																					if len(v.shape				) == 3:
																												snake_case_          =   v[i * single_shape : (i + 1) * single_shape, :, 0]
																					else:
																												snake_case_          =   v[i * single_shape : (i + 1) * single_shape]
							return new_state_dict





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)

							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()}'''
														snake_case_          =   download(UpperCamelCase__				)

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

							snake_case_          =   Object()
							snake_case_          =   sample_size
							snake_case_          =   sample_rate
							snake_case_          =   0

							snake_case_          =   UNetaDModel(sample_size=UpperCamelCase__       ,							sample_rate=UpperCamelCase__				)
							snake_case_          =   diffusers_model.state_dict()

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

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

							assert len(UpperCamelCase__				) == 0, F'''Problem with {renamed_minus_diffusers}'''
							assert all(k.endswith('kernel'				) for k in list(UpperCamelCase__				)				), 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":
																					snake_case_          =   value.squeeze()

														snake_case_          =   value

							diffusers_model.load_state_dict(UpperCamelCase__				)

							snake_case_          =   100
							snake_case_          =   33

							snake_case_          =   IPNDMScheduler(num_train_timesteps=UpperCamelCase__				)

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

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

							snake_case_          =   DanceDiffusionPipeline(unet=UpperCamelCase__       ,							scheduler=UpperCamelCase__				)

							snake_case_          =   torch.manual_seed(33				)
							snake_case_          =   pipe(num_inference_steps=UpperCamelCase__       ,							generator=UpperCamelCase__				).audios

							snake_case_          =   sampling.iplms_sample(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							{}				)
							snake_case_          =   generated.clamp(-1       ,							1				)

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

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

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

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

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


if __name__ == "__main__":
					_UpperCAmelCase		:       str						    =	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		:       List[Any]						    =	parser.parse_args()

					main(args)







import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			str	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      True




				def    a							( self	):
											super().setUp()

											snake_case_          =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=snake_case	)
											snake_case_          =   tokenizer




				def    a							( self	):
											snake_case_          =   '<pad>'
											snake_case_          =   1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case	)				,       snake_case	)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]				,       '<s>'	)
											self.assertEqual(vocab_keys[1]				,       '<pad>'	)
											self.assertEqual(vocab_keys[-1]				,       '<mask>'	)
											self.assertEqual(len(snake_case	)				,       10_1122	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       10_1122	)




				@require_torch
				def    a							( self	):
											snake_case_          =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   self.tokenizer(
											    snake_case				,       max_length=len(snake_case	)				,       padding=snake_case				,       truncation=snake_case				,       return_tensors='pt'	)
											self.assertIsInstance(snake_case				,       snake_case	)

											self.assertEqual((2, 6)				,       batch.input_ids.shape	)
											self.assertEqual((2, 6)				,       batch.attention_mask.shape	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




				def    a							( self	):
											if not self.test_rust_tokenizer:
																		return

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

											snake_case_          =   'I was born in 92000, and this is falsé.'

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											snake_case_          =   [
											    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
											    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
											    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
											    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
											    'telles que la traduction et la synthèse de texte.',
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=snake_case				,       model_name='moussaKam/mbarthez'				,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'				,       sequences=snake_case				,       )




