"""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()

_SCREAMING_SNAKE_CASE	:		Tuple   =   {
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

_SCREAMING_SNAKE_CASE	:		Any   =   datasets.load_dataset('''xnli''', split='''test+validation''')

_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0
_SCREAMING_SNAKE_CASE	:		int   =   0
_SCREAMING_SNAKE_CASE	:		List[Any]   =   0
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0



def       A__    (	lowercase_     :	Optional[int]      , lowercase_     :	Optional[Any]      , lowercase_     :	List[Any]      , lowercase_     :	Dict						)  ->							List[Any]:
   if spm_diff == list(reversed(lowercase_						)						):
      # AAA -> AA+A vs A+AA case.
      return True
   elif len(lowercase_						) == len(lowercase_						) and fast.decode(lowercase_						) == fast.decode(lowercase_						):
      # Second order OK
      # Barrich -> Barr + ich vs Bar + rich
      return True
   __lowerCamelCase    		=					slow.encode(slow.decode(lowercase_						)						)
   __lowerCamelCase    		=					fast.encode(fast.decode(lowercase_						)						)
   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       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	Union[str, Any]						)  ->							Dict:
   __lowerCamelCase    		=					fast.encode_plus(lowercase_						)[0]
   __lowerCamelCase    		=					enc.offsets
   __lowerCamelCase		,__lowerCamelCase    		=					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       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]      , lowercase_     :	Union[str, Any]      , lowercase_     :	Any						)  ->							Any:
   # 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(lowercase_      , lowercase_						)						):
      if spm_id != tok_id:
         break
   __lowerCamelCase    		=					i
   for i, (spm_id, tok_id) in enumerate(zip(reversed(lowercase_						)      , reversed(lowercase_						)						)						):
      if spm_id != tok_id:
         break
   __lowerCamelCase    		=					len(lowercase_						) - i

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

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

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

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

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

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

   __lowerCamelCase    		=					fast.decode(spm_ids[:first]						)
   __lowerCamelCase    		=					fast.decode(spm_ids[last:]						)
   __lowerCamelCase    		=					fast.decode(spm_ids[first:last]						)
   print()
   print(lowercase_						)
   return False



def       A__    (	lowercase_     :	str      , lowercase_     :	Dict      , lowercase_     :	Union[str, Any]						)  ->							Any:
   global perfect
   global imperfect
   global wrong
   global total

   __lowerCamelCase    		=					slow.encode(lowercase_						)
   __lowerCamelCase    		=					fast.encode(lowercase_						)

   __lowerCamelCase    		=					False
   total += 1

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



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Tuple						)  ->							Tuple:
   global batch_total
   for i in range(len(lowercase_						)						):
      # premise, all languages
      for text in dataset[i]["premise"].values():
         test_string(lowercase_      , lowercase_      , lowercase_						)

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


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

               print(F"========================== Checking {name}: {checkpoint} ==========================")
               _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   slow_class.from_pretrained(checkpoint, force_download=True)
               _SCREAMING_SNAKE_CASE	:		Dict   =   fast_class.from_pretrained(checkpoint, force_download=True)
               test_tokenizer(slow, fast)
               print(F"Accuracy {perfect * 1_0_0 / total:.2f}")







"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class      UpperCamelCase__:
						lowerCAmelCase__	:    CommonSchedulerState

						# setable values
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    Optional[int]    				=		None

						# running values
						lowerCAmelCase__	:    Optional[jnp.ndarray]    				=		None





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[Any]:
													return cls(common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase  )






@dataclass
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    LMSDiscreteSchedulerState






class      UpperCamelCase__(      __A		,    __A			):
						lowerCAmelCase__	:    List[str]    				=		[e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase__	:    jnp.dtype





						@property
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return True





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = 0.0_0_0_1		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = "linear"		,__UpperCAmelCase = None		,__UpperCAmelCase = "epsilon"		,__UpperCAmelCase = jnp.floataa		,)       ->					str:
													A__						  =				dtype





						def 					snake_case__						(							self		,__UpperCAmelCase = None  )       ->					LMSDiscreteSchedulerState:
													if common is None:
																				A__						  =				CommonSchedulerState.create(self  )

													A__						  =				jnp.arange(0		,self.config.num_train_timesteps  ).round()[::-1]
													A__						  =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													A__						  =				sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					jnp.ndarray:
													((A__)  ,	)						  =				jnp.where(state.timesteps == timestep		,size=1  )
													A__						  =				step_index[0]

													A__						  =				state.sigmas[step_index]
													A__						  =				sample / ((sigma**2 + 1) ** 0.5)
													return sample





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[int]:

													def lms_derivative(__UpperCAmelCase  ):
																				A__						  =				1.0
																				for k in range(__UpperCAmelCase  ):
																											if current_order == k:
																																		continue
																											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																				return prod

													A__						  =				integrate.quad(__UpperCAmelCase		,state.sigmas[t]		,state.sigmas[t + 1]		,epsrel=1e-4  )[0]

													return integrated_coeff





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = ()  )       ->					LMSDiscreteSchedulerState:
													A__						  =				jnp.linspace(self.config.num_train_timesteps - 1		,0		,__UpperCAmelCase		,dtype=self.dtype  )

													A__						  =				jnp.floor(__UpperCAmelCase  ).astype(jnp.intaa  )
													A__						  =				jnp.ceil(__UpperCAmelCase  ).astype(jnp.intaa  )

													A__						  =				jnp.mod(__UpperCAmelCase		,1.0  )

													A__						  =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													A__						  =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													A__						  =				jnp.concatenate([sigmas, jnp.array([0.0]		,dtype=self.dtype  )]  )

													A__						  =				timesteps.astype(jnp.intaa  )

													# initial running values
													A__						  =				jnp.zeros((0,) + shape		,dtype=self.dtype  )

													return state.replace(
													    timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,num_inference_steps=__UpperCAmelCase		,derivatives=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 4		,__UpperCAmelCase = True		,)       ->					Union[FlaxLMSSchedulerOutput, Tuple]:
													if state.num_inference_steps is None:
																				raise ValueError(
																				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

													A__						  =				state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				A__						  =				sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				A__						  =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																				raise ValueError(
																				    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

													# 2. Convert to an ODE derivative
													A__						  =				(sample - pred_original_sample) / sigma
													A__						  =				state.replace(derivatives=jnp.append(state.derivatives		,__UpperCAmelCase  )  )
													if len(state.derivatives  ) > order:
																				A__						  =				state.replace(derivatives=jnp.delete(state.derivatives		,0  )  )

													# 3. Compute linear multistep coefficients
													A__						  =				min(timestep + 1		,__UpperCAmelCase  )
													A__						  =				[self.get_lms_coefficient(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ) for curr_order in range(__UpperCAmelCase  )]

													# 4. Compute previous sample based on the derivatives path
													A__						  =				sample + sum(
													    coeff * derivative for coeff, derivative in zip(__UpperCAmelCase		,reversed(state.derivatives  )  )  )

													if not return_dict:
																				return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase		,state=__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					jnp.ndarray:
													A__						  =				state.sigmas[timesteps].flatten()
													A__						  =				broadcast_to_shape_from_left(__UpperCAmelCase		,noise.shape  )

													A__						  =				original_samples + noise * sigma

													return noisy_samples





						def __len__(							self  )       ->					Optional[Any]:
													return self.config.num_train_timesteps





