

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      :    Optional[int]						       =    {
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

_lowerCAmelCase      :    List[str]						       =    datasets.load_dataset("xnli", split="test+validation")

_lowerCAmelCase      :    str						       =    0
_lowerCAmelCase      :    Optional[int]						       =    0
_lowerCAmelCase      :    str						       =    0
_lowerCAmelCase      :    int						       =    0






def  UpperCamelCase_(							_snake_case     :						List[Any] ,  _snake_case     :						int ,  _snake_case     :						Optional[Any] ,  _snake_case     :						Tuple			):




			"""simple docstring"""






			if spm_diff == list(reversed(_snake_case			)			):
						# AAA -> AA+A vs A+AA case.
						return True
			elif len(_snake_case			) == len(_snake_case			) and fast.decode(_snake_case			) == fast.decode(_snake_case			):
						# Second order OK
						# Barrich -> Barr + ich vs Bar + rich
						return True
			__a						   =slow.encode(slow.decode(_snake_case			)			)
			__a						   =fast.encode(fast.decode(_snake_case			)			)
			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_(							_snake_case     :						List[str] ,  _snake_case     :						Tuple ,  _snake_case     :						str			):




			"""simple docstring"""






			__a						   =fast.encode_plus(_snake_case			)[0]
			__a						   =enc.offsets
			__a   ,					__a						   =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_(							_snake_case     :						Optional[int] ,  _snake_case     :						List[Any] ,  _snake_case     :						int ,  _snake_case     :						Union[str, Any] ,  _snake_case     :						int			):




			"""simple docstring"""






			for i, (spm_id, tok_id) in enumerate(zip(_snake_case ,  _snake_case			)			):
						if spm_id != tok_id:
									break
			__a						   =i
			for i, (spm_id, tok_id) in enumerate(zip(reversed(_snake_case			) ,  reversed(_snake_case			)			)			):
						if spm_id != tok_id:
									break
			__a						   =len(_snake_case			) - i

			__a						   =spm_ids[first:last]
			__a						   =tok_ids[first:last]

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

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

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

						__a						   ={spm_ for (spm_, si) in spms.items() if toks.get(spm_ ,  0			) == si}
						__a						   =3
						for i in range(last - first - min_width			):
									if all(spm_ids[first + i + j] in removable_tokens for j in range(_snake_case			)			):
												__a						   =[
												    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] ,  _snake_case ,  _snake_case			) and check_details(
															    _snake_case ,  spm_ids[first + i : last] ,  tok_ids[first + j : last] ,  _snake_case ,  _snake_case ,  ):
																		return True

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

			__a						   =fast.decode(spm_ids[:first]			)
			__a						   =fast.decode(spm_ids[last:]			)
			__a						   =fast.decode(spm_ids[first:last]			)
			print()
			print(_snake_case			)
			return False






def  UpperCamelCase_(							_snake_case     :						Union[str, Any] ,  _snake_case     :						Union[str, Any] ,  _snake_case     :						Any			):




			"""simple docstring"""






			global perfect
			global imperfect
			global wrong
			global total

			__a						   =slow.encode(_snake_case			)
			__a						   =fast.encode(_snake_case			)

			__a						   =False
			total += 1

			if slow_ids != fast_ids:
						if check_details(_snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _snake_case			):
									__a						   =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(_snake_case			)}\n{fast.tokenize(_snake_case			)}'






def  UpperCamelCase_(							_snake_case     :						Tuple ,  _snake_case     :						Tuple			):




			"""simple docstring"""






			global batch_total
			for i in range(len(_snake_case			)			):
						# premise, all languages
						for text in dataset[i]["premise"].values():
									test_string(_snake_case ,  _snake_case ,  _snake_case			)

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


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

																		print(f'''========================== Checking {name}: {checkpoint} ==========================''')
																		_lowerCAmelCase      :    List[str]						       =    slow_class.from_pretrained(checkpoint, force_download=True)
																		_lowerCAmelCase      :    Union[str, Any]						       =    fast_class.from_pretrained(checkpoint, force_download=True)
																		test_tokenizer(slow, fast)
																		print(f'''Accuracy {perfect * 100 / total:.2f}''')









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase      :    List[str]						       =    False






@nightly
@require_torch_gpu
class 			__magic_name__					(    unittest.TestCase   ):





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




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__snake_case      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"





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




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   ='cyberpunk 2077'
						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=50      ,						output_type='numpy'      ,						).images

						__a						   =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a						   =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






