




from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_lowerCamelCase					:     List[Any]					  =  {
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

_lowerCamelCase					:     Optional[Any]					  =  datasets.load_dataset("xnli", split="test+validation")

_lowerCamelCase					:     Dict					  =  0
_lowerCamelCase					:     Union[str, Any]					  =  0
_lowerCamelCase					:     Dict					  =  0
_lowerCamelCase					:     int					  =  0




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Union[str, Any]					,			UpperCamelCase_       :       Tuple				):
				'''simple docstring'''
				if spm_diff == list(reversed(UpperCamelCase_				)				):
								# AAA -> AA+A vs A+AA case.
								return True
				elif len(UpperCamelCase_				) == len(UpperCamelCase_				) and fast.decode(UpperCamelCase_				) == fast.decode(UpperCamelCase_				):
								# Second order OK
								# Barrich -> Barr + ich vs Bar + rich
								return True
				_lowerCAmelCase			:			Any        =					slow.encode(slow.decode(UpperCamelCase_				)				)
				_lowerCAmelCase			:			Dict        =					fast.encode(fast.decode(UpperCamelCase_				)				)
				if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
								# Type 3 error.
								# Snehagatha ->
								#       Sne, h, aga, th, a
								#       Sne, ha, gat, ha
								# Encoding the wrong with sp does not even recover what spm gave us
								# It fits tokenizer however...
								return True
				return False




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]					,			UpperCamelCase_       :       Any				):
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					fast.encode_plus(UpperCamelCase_				)[0]
				_lowerCAmelCase			:			Tuple        =					enc.offsets
				_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					offsets[idx], offsets[idx - 1]
				if curr is not None and line[curr[0] : curr[1]] == "\u200f":
								return True
				if prev is not None and line[prev[0] : prev[1]] == "\u200f":
								return True




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Any				):
				'''simple docstring'''
				# Encoding can be the same with same result AAA -> A + AA vs AA + A
				# We can check that we use at least exactly the same number of tokens.
				for i, (spm_id, tok_id) in enumerate(zip(UpperCamelCase_					,			UpperCamelCase_				)				):
								if spm_id != tok_id:
												break
				_lowerCAmelCase			:			str        =					i
				for i, (spm_id, tok_id) in enumerate(zip(reversed(UpperCamelCase_				)					,			reversed(UpperCamelCase_				)				)				):
								if spm_id != tok_id:
												break
				_lowerCAmelCase			:			int        =					len(UpperCamelCase_				) - i

				_lowerCAmelCase			:			Any        =					spm_ids[first:last]
				_lowerCAmelCase			:			Optional[Any]        =					tok_ids[first:last]

				if check_diff(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				):
								return True

				if check_LTR_mark(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				):
								return True

				if last - first > 5:
								# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
								_lowerCAmelCase			:			Optional[Any]        =					Counter(spm_ids[first:last]				)
								_lowerCAmelCase			:			Dict        =					Counter(tok_ids[first:last]				)

								_lowerCAmelCase			:			int        =					{spm_ for (spm_, si) in spms.items() if toks.get(spm_					,			0				) == si}
								_lowerCAmelCase			:			Optional[Any]        =					3
								for i in range(last - first - min_width				):
												if all(spm_ids[first + i + j] in removable_tokens for j in range(UpperCamelCase_				)				):
																_lowerCAmelCase			:			List[str]        =					[
																    k
																    for k in range(last - first - min_width				)
																    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																]
																for j in possible_matches:
																				if check_diff(spm_ids[first : first + i]					,			tok_ids[first : first + j]					,			UpperCamelCase_					,			UpperCamelCase_				) and check_details(
																				    UpperCamelCase_					,			spm_ids[first + i : last]					,			tok_ids[first + j : last]					,			UpperCamelCase_					,			UpperCamelCase_					,			):
																								return True

				print(F"Spm: {[fast.decode([spm_ids[i]]				) for i in range(UpperCamelCase_					,			UpperCamelCase_				)]}"				)
				try:
								print(F"Tok: {[fast.decode([tok_ids[i]]				) for i in range(UpperCamelCase_					,			UpperCamelCase_				)]}"				)
				except Exception:
								pass

				_lowerCAmelCase			:			Any        =					fast.decode(spm_ids[:first]				)
				_lowerCAmelCase			:			List[Any]        =					fast.decode(spm_ids[last:]				)
				_lowerCAmelCase			:			Dict        =					fast.decode(spm_ids[first:last]				)
				print()
				print(UpperCamelCase_				)
				return False




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[str]					,			UpperCamelCase_       :       Any				):
				'''simple docstring'''
				global perfect
				global imperfect
				global wrong
				global total

				_lowerCAmelCase			:			Optional[Any]        =					slow.encode(UpperCamelCase_				)
				_lowerCAmelCase			:			List[str]        =					fast.encode(UpperCamelCase_				)

				_lowerCAmelCase			:			int        =					False
				total += 1

				if slow_ids != fast_ids:
								if check_details(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				):
												_lowerCAmelCase			:			str        =					True
												imperfect += 1
								else:
												wrong += 1
				else:
								perfect += 1

				if total % 10000 == 0:
								print(F"({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"				)

				if skip_assert:
								return

				assert (
				    slow_ids == fast_ids
				), F"line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(UpperCamelCase_				)}\n{fast.tokenize(UpperCamelCase_				)}"




def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				global batch_total
				for i in range(len(UpperCamelCase_				)				):
								# premise, all languages
								for text in dataset[i]["premise"].values():
												test_string(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)

								# hypothesis, all languages
								for text in dataset[i]["hypothesis"]["translation"]:
												test_string(UpperCamelCase_					,			UpperCamelCase_					,			UpperCamelCase_				)


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		_lowerCamelCase					:     Optional[int]					  =  list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			_lowerCamelCase					:     Dict					  =  0
			_lowerCamelCase					:     str					  =  0
			_lowerCamelCase					:     Union[str, Any]					  =  0
			_lowerCamelCase					:     List[Any]					  =  0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			_lowerCamelCase					:     List[Any]					  =  slow_class.from_pretrained(checkpoint, force_download=True)
			_lowerCamelCase					:     Optional[int]					  =  fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 1_0_0 / total:.2f}''')







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	_lowerCamelCase					:     Union[str, Any]					  =  True
	_lowerCamelCase					:     Optional[Any]					  =  ""
except Exception as e:
	_lowerCamelCase					:     int					  =  False
	_lowerCamelCase					:     List[str]					  =  (
	    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
	)


from PIL import Image  # noqa: E402
class 				__snake_case     (_a  ,		_a						):
				lowerCAmelCase__			=   "mel_config.json"



				@register_to_config
				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 2_2050						,		_UpperCAmelCase					:						int = 2048						,		_UpperCAmelCase					:						int = 512						,		_UpperCAmelCase					:						int = 80						,		_UpperCAmelCase					:						int = 32						,		)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					hop_length
								_lowerCAmelCase			:			Optional[Any]        =					sample_rate
								_lowerCAmelCase			:			Optional[Any]        =					n_fft
								_lowerCAmelCase			:			Dict        =					top_db
								_lowerCAmelCase			:			List[Any]        =					n_iter
								self.set_resolution(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					None

								if not _librosa_can_be_imported:
												raise ValueError(_import_error					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					x_res
								_lowerCAmelCase			:			Union[str, Any]        =					y_res
								_lowerCAmelCase			:			Any        =					self.y_res
								_lowerCAmelCase			:			Optional[Any]        =					self.x_res * self.hop_length - 1



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						str = None						,		_UpperCAmelCase					:						np.ndarray = None					)     ->			List[str]:





								'''simple docstring'''




								if audio_file is not None:
												_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					librosa.load(_UpperCAmelCase						,		mono=_UpperCAmelCase						,		sr=self.sr					)
								else:
												_lowerCAmelCase			:			Any        =					raw_audio

								# Pad with silence if necessary.
								if len(self.audio					) < self.x_res * self.hop_length:
												_lowerCAmelCase			:			Optional[Any]        =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								return len(self.audio					) // self.slice_size



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						int = 0					)     ->			np.ndarray:





								'''simple docstring'''




								return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			int:





								'''simple docstring'''




								return self.sr



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						int					)     ->			Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					librosa.feature.melspectrogram(
								    y=self.get_audio_slice(_UpperCAmelCase					)						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_mels=self.n_mels					)
								_lowerCAmelCase			:			List[Any]        =					librosa.power_to_db(_UpperCAmelCase						,		ref=np.max						,		top_db=self.top_db					)
								_lowerCAmelCase			:			List[Any]        =					(((log_S + self.top_db) * 255 / self.top_db).clip(0						,		255					) + 0.5).astype(np.uinta					)
								_lowerCAmelCase			:			Optional[Any]        =					Image.fromarray(_UpperCAmelCase					)
								return image



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						Image.Image					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					np.frombuffer(image.tobytes()						,		dtype="""uint8"""					).reshape((image.height, image.width)					)
								_lowerCAmelCase			:			Optional[int]        =					bytedata.astype("""float"""					) * self.top_db / 255 - self.top_db
								_lowerCAmelCase			:			List[Any]        =					librosa.db_to_power(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					librosa.feature.inverse.mel_to_audio(
								    _UpperCAmelCase						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_iter=self.n_iter					)
								return audio

