





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_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 1_3_1_0_7_2,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase )		->						Any:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  torch.sin(t * math.pi / 2 ) ** 2
				snake_case_						:    List[Any]				=  (1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(_UpperCamelCase ,				_UpperCamelCase )



class 					__lowerCAmelCase       (			_a     ):
			pass




class 					__lowerCAmelCase       (			nn.Module     ):






			def __init__(self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							super().__init__()

							snake_case_						:    Dict				=  DiffusionAttnUnetaD(__magic_name__					,							n_attn_layers=4    )
							snake_case_						:    Union[str, Any]				=  deepcopy(self.diffusion    )
							snake_case_						:    Tuple				=  torch.quasirandom.SobolEngine(1					,							scramble=__magic_name__    )






def      lowerCamelCase_						(				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:    Any				=  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      lowerCamelCase_						(				_UpperCamelCase )		->						Union[str, Any]:






				"""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 )		->						Optional[Any]:






				"""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 )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:    Optional[Any]				=  input_string

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

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

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

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

				# mid block
				if string[:2].isdigit():
								snake_case_						:    int				=  string[:2]
								snake_case_						:    Optional[Any]				=  string[2:]
				else:
								snake_case_						:    Tuple				=  string[0]
								snake_case_						:    Union[str, Any]				=  string[1:]

				if depth == max_depth:
								snake_case_						:    str				=  MID_NUM_TO_LAYER[layer_num]
								snake_case_						:    Optional[int]				=  '''mid_block'''
				elif depth > 0 and int(_UpperCamelCase ) < 7:
								snake_case_						:    str				=  DOWN_NUM_TO_LAYER[layer_num]
								snake_case_						:    Dict				=  f'''down_blocks.{depth}'''
				elif depth > 0 and int(_UpperCamelCase ) > 7:
								snake_case_						:    Optional[int]				=  UP_NUM_TO_LAYER[layer_num]
								snake_case_						:    int				=  f'''up_blocks.{max_depth - depth - 1}'''
				elif depth == 0:
								snake_case_						:    Any				=  DEPTH_0_TO_LAYER[layer_num]
								snake_case_						:    Optional[int]				=  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_						:    Optional[int]				=  string_left[1:]

				if "resnets" in new_layer:
								snake_case_						:    Tuple				=  convert_resconv_naming(_UpperCamelCase )
				elif "attentions" in new_layer:
								snake_case_						:    Dict				=  convert_attn_naming(_UpperCamelCase )
								snake_case_						:    List[str]				=  new_string_left

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






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


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

								snake_case_						:    Dict				=  rename(_UpperCamelCase )

								# check if we need to transform from Conv => Linear for attention
								if isinstance(_UpperCamelCase ,				_UpperCamelCase ):
												snake_case_						:    List[str]				=  transform_conv_attns(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )
								else:
												snake_case_						:    Dict				=  v

				return new_state_dict






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


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

				snake_case_						:    List[Any]				=  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_						:    Tuple				=  download(_UpperCamelCase )

				snake_case_						:    List[str]				=  MODELS_MAP[model_name]['''sample_rate''']
				snake_case_						:    List[Any]				=  MODELS_MAP[model_name]['''sample_size''']

				snake_case_						:    Optional[Any]				=  Object()
				snake_case_						:    Union[str, Any]				=  sample_size
				snake_case_						:    Dict				=  sample_rate
				snake_case_						:    Tuple				=  0

				snake_case_						:    Optional[Any]				=  UNetaDModel(sample_size=_UpperCamelCase ,				sample_rate=_UpperCamelCase )
				snake_case_						:    Optional[Any]				=  diffusers_model.state_dict()

				snake_case_						:    int				=  DiffusionUncond(_UpperCamelCase )
				orig_model.load_state_dict(torch.load(args.model_path ,				map_location=_UpperCamelCase )['''state_dict'''] )
				snake_case_						:    List[Any]				=  orig_model.diffusion_ema.eval()
				snake_case_						:    Any				=  orig_model.state_dict()
				snake_case_						:    Optional[Any]				=  rename_orig_weights(_UpperCamelCase )

				snake_case_						:    List[Any]				=  set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
				snake_case_						:    List[str]				=  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_						:    Dict				=  value.squeeze()

								snake_case_						:    List[Any]				=  value

				diffusers_model.load_state_dict(_UpperCamelCase )

				snake_case_						:    Dict				=  100
				snake_case_						:    List[str]				=  33

				snake_case_						:    int				=  IPNDMScheduler(num_train_timesteps=_UpperCamelCase )

				snake_case_						:    Optional[Any]				=  torch.manual_seed(_UpperCamelCase )
				snake_case_						:    Optional[Any]				=  torch.randn([1, 2, config.sample_size] ,				generator=_UpperCamelCase ).to(_UpperCamelCase )

				snake_case_						:    Union[str, Any]				=  torch.linspace(1 ,				0 ,				steps + 1 ,				device=_UpperCamelCase )[:-1]
				snake_case_						:    Dict				=  get_crash_schedule(_UpperCamelCase )

				snake_case_						:    int				=  DanceDiffusionPipeline(unet=_UpperCamelCase ,				scheduler=_UpperCamelCase )

				snake_case_						:    Any				=  torch.manual_seed(33 )
				snake_case_						:    Optional[Any]				=  pipe(num_inference_steps=_UpperCamelCase ,				generator=_UpperCamelCase ).audios

				snake_case_						:    Tuple				=  sampling.iplms_sample(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				{} )
				snake_case_						:    Any				=  generated.clamp(-1 ,				1 )

				snake_case_						:    Optional[Any]				=  (generated - audio).abs().sum()
				snake_case_						:    List[str]				=  (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__":
	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)







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase_    =				logging.getLogger(__name__)



@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     str
			lowerCamelCase_     :     List[str]
			lowerCamelCase_     :     Optional[List[str]]




@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     Optional[List[int]]       			=      None
			lowerCamelCase_     :     Optional[List[int]]       			=      None




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     str       			=      '''train'''
			lowerCamelCase_     :     List[str]       			=      '''dev'''
			lowerCamelCase_     :     List[Any]       			=      '''test'''




class 					__lowerCAmelCase       :






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__    )     ->   List[InputExample]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=False					,							__magic_name__="[CLS]"					,							__magic_name__=1					,							__magic_name__="[SEP]"					,							__magic_name__=False					,							__magic_name__=False					,							__magic_name__=0					,							__magic_name__=0					,							__magic_name__=-100					,							__magic_name__=0					,							__magic_name__=True					,							)     ->   List[InputFeatures]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {label: i for i, label in enumerate(__magic_name__    )}

							snake_case_						:    Dict				=  []
							for ex_index, example in enumerate(__magic_name__    ):
											if ex_index % 1_0000 == 0:
															logger.info('''Writing example %d of %d'''					,							__magic_name__					,							len(__magic_name__    )    )

											snake_case_						:    List[str]				=  []
											snake_case_						:    List[str]				=  []
											for word, label in zip(example.words					,							example.labels    ):
															snake_case_						:    Optional[Any]				=  tokenizer.tokenize(__magic_name__    )

															# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
															if len(__magic_name__    ) > 0:
																			tokens.extend(__magic_name__    )
																			# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																			label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__magic_name__    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											snake_case_						:    Union[str, Any]				=  tokenizer.num_special_tokens_to_add()
											if len(__magic_name__    ) > max_seq_length - special_tokens_count:
															snake_case_						:    str				=  tokens[: (max_seq_length - special_tokens_count)]
															snake_case_						:    Any				=  label_ids[: (max_seq_length - special_tokens_count)]

											# The convention in BERT is:
											# (a) For sequence pairs:
											#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
											#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
											# (b) For single sequences:
											#  tokens:   [CLS] the dog is hairy . [SEP]
											#  type_ids:   0   0   0   0  0     0   0
											#
											# Where "type_ids" are used to indicate whether this is the first
											# sequence or the second sequence. The embedding vectors for `type=0` and
											# `type=1` were learned during pre-training and are added to the wordpiece
											# embedding vector (and position vector). This is not *strictly* necessary
											# since the [SEP] token unambiguously separates the sequences, but it makes
											# it easier for the model to learn the concept of sequences.
											#
											# For classification tasks, the first vector (corresponding to [CLS]) is
											# used as the "sentence vector". Note that this only makes sense because
											# the entire model is fine-tuned.
											tokens += [sep_token]
											label_ids += [pad_token_label_id]
											if sep_token_extra:
															# roberta uses an extra separator b/w pairs of sentences
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
											snake_case_						:    Union[str, Any]				=  [sequence_a_segment_id] * len(__magic_name__    )

											if cls_token_at_end:
															tokens += [cls_token]
															label_ids += [pad_token_label_id]
															segment_ids += [cls_token_segment_id]
											else:
															snake_case_						:    Union[str, Any]				=  [cls_token] + tokens
															snake_case_						:    List[Any]				=  [pad_token_label_id] + label_ids
															snake_case_						:    Optional[Any]				=  [cls_token_segment_id] + segment_ids

											snake_case_						:    Optional[Any]				=  tokenizer.convert_tokens_to_ids(__magic_name__    )

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											snake_case_						:    int				=  [1 if mask_padding_with_zero else 0] * len(__magic_name__    )

											# Zero-pad up to the sequence length.
											snake_case_						:    Optional[int]				=  max_seq_length - len(__magic_name__    )
											if pad_on_left:
															snake_case_						:    Optional[Any]				=  ([pad_token] * padding_length) + input_ids
															snake_case_						:    Optional[int]				=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
															snake_case_						:    Optional[Any]				=  ([pad_token_segment_id] * padding_length) + segment_ids
															snake_case_						:    Dict				=  ([pad_token_label_id] * padding_length) + label_ids
											else:
															input_ids += [pad_token] * padding_length
															input_mask += [0 if mask_padding_with_zero else 1] * padding_length
															segment_ids += [pad_token_segment_id] * padding_length
															label_ids += [pad_token_label_id] * padding_length

											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length

											if ex_index < 5:
															logger.info('''*** Example ***'''    )
															logger.info('''guid: %s'''					,							example.guid    )
															logger.info('''tokens: %s'''					,							''' '''.join([str(__magic_name__    ) for x in tokens]    )    )
															logger.info('''input_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in input_ids]    )    )
															logger.info('''input_mask: %s'''					,							''' '''.join([str(__magic_name__    ) for x in input_mask]    )    )
															logger.info('''segment_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in segment_ids]    )    )
															logger.info('''label_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in label_ids]    )    )

											if "token_type_ids" not in tokenizer.model_input_names:
															snake_case_						:    int				=  None

											features.append(
											    InputFeatures(
											        input_ids=__magic_name__					,							attention_mask=__magic_name__					,							token_type_ids=__magic_name__					,							label_ids=__magic_name__    )    )
							return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset



	class 					__lowerCAmelCase       (			_a     ):
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      nn.CrossEntropyLoss().ignore_index






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Union[str, Any]:


								'''simple docstring'''



								snake_case_						:    List[str]				=  os.path.join(
								    __magic_name__					,							'''cached_{}_{}_{}'''.format(mode.value					,							tokenizer.__class__.__name__					,							str(__magic_name__    )    )					,							)

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_						:    Dict				=  cached_features_file + '''.lock'''
								with FileLock(__magic_name__    ):
												if os.path.exists(__magic_name__    ) and not overwrite_cache:
																logger.info(F'''Loading features from cached file {cached_features_file}'''    )
																snake_case_						:    Dict				=  torch.load(__magic_name__    )
												else:
																logger.info(F'''Creating features from dataset file at {data_dir}'''    )
																snake_case_						:    Any				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
																# TODO clean up all this to leverage built-in features of tokenizers
																snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
																    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							cls_token_at_end=bool(model_type in ['''xlnet''']    )					,							cls_token=tokenizer.cls_token					,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,							sep_token=tokenizer.sep_token					,							sep_token_extra=__magic_name__					,							pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,							pad_token=tokenizer.pad_token_id					,							pad_token_segment_id=tokenizer.pad_token_type_id					,							pad_token_label_id=self.pad_token_label_id					,							)
																logger.info(F'''Saving features into cached file {cached_features_file}'''    )
																torch.save(self.features					,							__magic_name__    )






				def __len__(self    )     ->   Optional[Any]:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]


if is_tf_available():
	import tensorflow as tf



	class 					__lowerCAmelCase       :
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      -100






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Optional[int]:


								'''simple docstring'''



								snake_case_						:    Optional[int]				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
								# TODO clean up all this to leverage built-in features of tokenizers
								snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
								    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							cls_token_at_end=bool(model_type in ['''xlnet''']    )					,							cls_token=tokenizer.cls_token					,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,							sep_token=tokenizer.sep_token					,							sep_token_extra=__magic_name__					,							pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,							pad_token=tokenizer.pad_token_id					,							pad_token_segment_id=tokenizer.pad_token_type_id					,							pad_token_label_id=self.pad_token_label_id					,							)

								def gen():
												for ex in self.features:
																if ex.token_type_ids is None:
																				yield (
																				    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																				    ex.label_ids,
																				)
																else:
																				yield (
																				    {
																				        "input_ids": ex.input_ids,
																				        "attention_mask": ex.attention_mask,
																				        "token_type_ids": ex.token_type_ids,
																				    },
																				    ex.label_ids,
																				)

								if "token_type_ids" not in tokenizer.model_input_names:
												snake_case_						:    Optional[Any]				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,							(
												        {'''input_ids''': tf.TensorShape([None]    ), '''attention_mask''': tf.TensorShape([None]    )},
												        tf.TensorShape([None]    ),
												    )					,							)
								else:
												snake_case_						:    int				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,							(
												        {
												            '''input_ids''': tf.TensorShape([None]    ),
												            '''attention_mask''': tf.TensorShape([None]    ),
												            '''token_type_ids''': tf.TensorShape([None]    ),
												        },
												        tf.TensorShape([None]    ),
												    )					,							)






				def 			lowerCamelCase  (self    )     ->   List[Any]:


								'''simple docstring'''



								snake_case_						:    Optional[Any]				=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

								return self.dataset






				def __len__(self    )     ->   str:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]
