




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 random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  _UpperCAmelCase       (			lowerCAmelCase,       unittest.TestCase   ):



 '''simple docstring'''




 __A           = KandinskyVaaPriorEmbaEmbPipeline
 __A           = ['''prompt''', '''image''']
 __A           = ['''prompt''', '''image''']
 __A           = [
     '''num_images_per_prompt''',
     '''strength''',
     '''generator''',
     '''num_inference_steps''',
     '''latents''',
     '''negative_prompt''',
     '''guidance_scale''',
     '''output_type''',
     '''return_dict''',
 ]
 __A           = False







 @property
 def 						__UpperCAmelCase							(						self							:					List[str])		->     List[str]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Union[str, Any]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					Any)		->     Optional[int]:


   """simple docstring"""







   return self.time_input_dim







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     int:


   """simple docstring"""







   return self.time_input_dim * 4







 @property
 def 						__UpperCAmelCase							(						self							:					int)		->     str:


   """simple docstring"""







   return 100







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
   return tokenizer







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Optional[int]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  CLIPTextConfig(
       bos_token_id=0				,      eos_token_id=2				,      hidden_size=self.text_embedder_hidden_size				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      layer_norm_eps=1e-0_5				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
   return CLIPTextModelWithProjection(lowercase_)







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     List[str]:


   """simple docstring"""







   torch.manual_seed(0)

   _UpperCamelCase				=  {
       "num_attention_heads": 2,
       "attention_head_dim": 12,
       "embedding_dim": self.text_embedder_hidden_size,
       "num_layers": 1,
   }

   _UpperCamelCase				=  PriorTransformer(**lowercase_)
   # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
   _UpperCamelCase				=  nn.Parameter(torch.ones(model.clip_std.shape))
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					str)		->     Union[str, Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  CLIPVisionConfig(
       hidden_size=self.text_embedder_hidden_size				,      image_size=224				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      num_attention_heads=4				,      num_channels=3				,      num_hidden_layers=5				,      patch_size=14				,      )

   _UpperCamelCase				=  CLIPVisionModelWithProjection(lowercase_)
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  CLIPImageProcessor(
       crop_size=224				,      do_center_crop=lowercase_				,      do_normalize=lowercase_				,      do_resize=lowercase_				,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,      resample=3				,      size=224				,      )

   return image_processor







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Optional[Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.dummy_prior
   _UpperCamelCase				=  self.dummy_image_encoder
   _UpperCamelCase				=  self.dummy_text_encoder
   _UpperCamelCase				=  self.dummy_tokenizer
   _UpperCamelCase				=  self.dummy_image_processor

   _UpperCamelCase				=  UnCLIPScheduler(
       variance_type="fixed_small_log"				,      prediction_type="sample"				,      num_train_timesteps=1000				,      clip_sample=lowercase_				,      clip_sample_range=10.0				,      )

   _UpperCamelCase				=  {
       "prior": prior,
       "image_encoder": image_encoder,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "scheduler": scheduler,
       "image_processor": image_processor,
   }

   return components







 def 						__UpperCAmelCase							(						self							:					List[str]				,      lowercase_							:					Optional[int]				,      lowercase_							:					Union[str, Any]=0)		->     Tuple:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase				=  torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase				=  torch.Generator(device=lowercase_).manual_seed(lowercase_)

   _UpperCamelCase				=  floats_tensor((1, 3, 64, 64)				,      rng=random.Random(lowercase_)).to(lowercase_)
   _UpperCamelCase				=  image.cpu().permute(0				,      2				,      3				,      1)[0]
   _UpperCamelCase				=  Image.fromarray(np.uinta(lowercase_)).convert("RGB").resize((256, 256))

   _UpperCamelCase				=  {
       "prompt": "horse",
       "image": init_image,
       "strength": 0.5,
       "generator": generator,
       "guidance_scale": 4.0,
       "num_inference_steps": 2,
       "output_type": "np",
   }
   return inputs







 def 						__UpperCAmelCase							(						self							:					List[str])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  "cpu"

   _UpperCamelCase				=  self.get_dummy_components()

   _UpperCamelCase				=  self.pipeline_class(**lowercase_)
   _UpperCamelCase				=  pipe.to(lowercase_)

   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase				=  pipe(**self.get_dummy_inputs(lowercase_))
   _UpperCamelCase				=  output.image_embeds

   _UpperCamelCase				=  pipe(
       **self.get_dummy_inputs(lowercase_)				,      return_dict=lowercase_				,      )[0]

   _UpperCamelCase				=  image[0, -10:]
   _UpperCamelCase				=  image_from_tuple[0, -10:]

   assert image.shape == (1, 32)

   _UpperCamelCase				=  np.array(
       [
           0.1_07_12_84,
           1.3_33_02_71,
           0.61_26_02_23,
           -0.6_69_10_65,
           -0.3_84_68_52,
           -1.0_30_36_61,
           0.22_71_61_11,
           0.03_34_89_01,
           0.30_04_06_75,
           -0.24_80_50_29,
       ])

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







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  True
   _UpperCamelCase				=  False

   self._test_inference_batch_single_identical(
       test_max_difference=lowercase_				,      relax_max_difference=lowercase_				,      test_mean_pixel_difference=lowercase_				,      )







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  False

   self._test_attention_slicing_forward_pass(
       test_max_difference=lowercase_				,      test_mean_pixel_difference=lowercase_				,      )




