
"""simple docstring"""







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__     =						{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

lowerCamelCase__     =						datasets.load_dataset("""xnli""", split="""test+validation""")

lowerCamelCase__     =						0
lowerCamelCase__     =						0
lowerCamelCase__     =						0
lowerCamelCase__     =						0
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	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     :		Optional[Any]		     = slow.encode(slow.decode(_UpperCamelCase						)						)
	__lowerCAmelCase     :		Any		     = 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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		List[str]		     = fast.encode_plus(_UpperCamelCase						)[0]
	__lowerCAmelCase     :		List[Any]		     = enc.offsets
	__lowerCAmelCase     ,  __lowerCAmelCase     :		List[Any]		     = 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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	# 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     :		Dict		     = i
	for i, (spm_id, tok_id) in enumerate(zip(reversed(_UpperCamelCase						)			,    reversed(_UpperCamelCase						)						)						):
		if spm_id != tok_id:
			break
	__lowerCAmelCase     :		Union[str, Any]		     = len(_UpperCamelCase						) - i

	__lowerCAmelCase     :		Optional[int]		     = spm_ids[first:last]
	__lowerCAmelCase     :		str		     = 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     :		Tuple		     = Counter(spm_ids[first:last]						)
		__lowerCAmelCase     :		Optional[Any]		     = Counter(tok_ids[first:last]						)

		__lowerCAmelCase     :		Dict		     = {spm_ for (spm_, si) in spms.items() if toks.get(spm_			,    0						) == si}
		__lowerCAmelCase     :		List[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     :		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     :		str		     = fast.decode(spm_ids[:first]						)
	__lowerCAmelCase     :		List[str]		     = fast.decode(spm_ids[last:]						)
	__lowerCAmelCase     :		Tuple		     = fast.decode(spm_ids[first:last]						)
	print()
	print(_UpperCamelCase						)
	return False
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	global perfect
	global imperfect
	global wrong
	global total

	__lowerCAmelCase     :		List[Any]		     = slow.encode(_UpperCamelCase						)
	__lowerCAmelCase     :		Any		     = fast.encode(_UpperCamelCase						)

	__lowerCAmelCase     :		int		     = False
	total += 1

	if slow_ids != fast_ids:
		if check_details(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
			__lowerCAmelCase     :		Dict		     = True
			imperfect += 1
		else:
			wrong += 1
	else:
		perfect += 1

	if total % 1_0000 == 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        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	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__     =						list(slow_class.max_model_input_sizes.keys())
														for checkpoint in checkpoint_names:
																					lowerCamelCase__     =						0
																					lowerCamelCase__     =						0
																					lowerCamelCase__     =						0
																					lowerCamelCase__     =						0

																					print(f'========================== Checking {name}: {checkpoint} ==========================')
																					lowerCamelCase__     =						slow_class.from_pretrained(checkpoint, force_download=True)
																					lowerCamelCase__     =						fast_class.from_pretrained(checkpoint, force_download=True)
																					test_tokenizer(slow, fast)
																					print(f'Accuracy {perfect * 100 / total:.2f}')

"""simple docstring"""







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_pt_objects import *  # noqa F403
else:
							from .consistency_models import ConsistencyModelPipeline
							from .dance_diffusion import DanceDiffusionPipeline
							from .ddim import DDIMPipeline
							from .ddpm import DDPMPipeline
							from .dit import DiTPipeline
							from .latent_diffusion import LDMSuperResolutionPipeline
							from .latent_diffusion_uncond import LDMPipeline
							from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
							from .pndm import PNDMPipeline
							from .repaint import RePaintPipeline
							from .score_sde_ve import ScoreSdeVePipeline
							from .stochastic_karras_ve import KarrasVePipeline

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
							from .audioldm import AudioLDMPipeline
							from .controlnet import (
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							)
							from .deepfloyd_if import (
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							)
							from .kandinsky import (
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							)
							from .kandinskya_a import (
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							)
							from .latent_diffusion import LDMTextToImagePipeline
							from .paint_by_example import PaintByExamplePipeline
							from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
							from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
							from .stable_diffusion import (
							    CycleDiffusionPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							)
							from .stable_diffusion_safe import StableDiffusionPipelineSafe
							from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
							from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
							from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
							from .versatile_diffusion import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
							from .vq_diffusion import VQDiffusionPipeline


try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_onnx_objects import *  # noqa F403
else:
							from .onnx_utils import OnnxRuntimeModel

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .stable_diffusion import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_objects import *  # noqa F403
else:
							from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .controlnet import FlaxStableDiffusionControlNetPipeline
							from .stable_diffusion import (
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)
try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline