

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


lowerCAmelCase_			      =      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 13_10_72,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
}


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						return torch.atana(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			) / math.pi * 2


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





class       snake_case_  (						__A   ):







			'''simple docstring'''

			pass






class       snake_case_  (						nn.Module   ):







			'''simple docstring'''

			def __init__(	self					:							List[Any]				,  _UpperCamelCase					:							int				) ->Optional[int]:
									super().__init__()

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






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

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


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

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

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=13			):
						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(SCREAMING_SNAKE_CASE__			) < 7:
												snake_case_  =      DOWN_NUM_TO_LAYER[layer_num]
												snake_case_  =      F'''down_blocks.{depth}'''
						elif depth > 0 and int(SCREAMING_SNAKE_CASE__			) > 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(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}.'''			)

						snake_case_  =      string_left[1:]

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

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						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(SCREAMING_SNAKE_CASE__			)

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

						return new_state_dict


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						if len(SCREAMING_SNAKE_CASE__			) == 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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						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(SCREAMING_SNAKE_CASE__			)

						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=SCREAMING_SNAKE_CASE__    ,						sample_rate=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      diffusers_model.state_dict()

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

						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(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":
																		snake_case_  =      value.squeeze()

												snake_case_  =      value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__			)

						snake_case_  =      100
						snake_case_  =      33

						snake_case_  =      IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__			)

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

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

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

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

						snake_case_  =      sampling.iplms_sample(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						{}			)
						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'''    ,						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__":
					lowerCAmelCase_			      =      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.''')
					lowerCAmelCase_			      =      parser.parse_args()

					main(args)
'''simple docstring'''


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__snake_case						  =		"""src/transformers"""
__snake_case						  =		"""docs/source/en"""
__snake_case						  =		"""."""

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  with open(SCREAMING_SNAKE_CASE_ ,	"""r""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
    lowercase_     =       f.readlines()
  # Find the start prompt.
  lowercase_     =       0
  while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_						):
    start_index += 1
  start_index += 1

  lowercase_     =       start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_						):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]						) <= 1:
    start_index += 1
  while len(lines[end_index]						) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__snake_case						  =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__snake_case						  =		re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__snake_case						  =		re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case						  =		re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
__snake_case						  =		direct_transformers_import(TRANSFORMERS_PATH)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" ,	SCREAMING_SNAKE_CASE_						)
  return [m.group(0						) for m in matches]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       (width - text_length) // 2
  lowercase_     =       width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   A_      (       )			->Tuple:
  lowercase_     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  lowercase_     =       {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  lowercase_     =       {name: config.replace("""Config""" ,	""""""						) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)

  # Let's lookup through all transformers object (once).
  for attr_name in dir(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       None
    if attr_name.endswith("""Tokenizer"""						):
      lowercase_     =       slow_tokenizers
      lowercase_     =       attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""						):
      lowercase_     =       fast_tokenizers
      lowercase_     =       attr_name[:-13]
    elif _re_tf_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       tf_models
      lowercase_     =       _re_tf_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_flax_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       flax_models
      lowercase_     =       _re_flax_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_pt_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       pt_models
      lowercase_     =       _re_pt_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]

    if lookup_dict is not None:
      while len(SCREAMING_SNAKE_CASE_						) > 0:
        if attr_name in model_name_to_prefix.values():
          lowercase_     =       True
          break
        # Try again after removing the last word in the name
        lowercase_     =       """""".join(camel_case_split(SCREAMING_SNAKE_CASE_						)[:-1]						)

    # Let's build that table!
  lowercase_     =       list(model_name_to_config.keys()						)
  model_names.sort(key=str.lower						)
  lowercase_     =       ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  lowercase_     =       [len(SCREAMING_SNAKE_CASE_						) + 2 for c in columns]
  lowercase_     =       max([len(SCREAMING_SNAKE_CASE_						) for name in model_names]						) + 2

  # Build the table per se
  lowercase_     =       """|""" + """|""".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for c, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]						) + "|\n"

  lowercase_     =       {True: """✅""", False: """❌"""}
  for name in model_names:
    lowercase_     =       model_name_to_prefix[name]
    lowercase_     =       [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for l, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + "|\n"
  return table

def   A_      (       SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       _find_text_in_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	start_prompt="""<!--This table is updated automatically from the auto modules""" ,	end_prompt="""<!-- End table-->""" ,	)
  lowercase_     =       get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	"""w""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       __snake_case						  =		parser.parse_args()

       check_model_table(args.fix_and_overwrite)

