





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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:	CommonSchedulerState

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






						@classmethod
						def UpperCAmelCase_ (					cls					:					Union[str, Any]						,      _A					:					Tuple						,      _A					:					List[str]						,      _A					:					Union[str, Any]		)							->			str:







							"""simple docstring"""


							return cls(common=_A						,      init_noise_sigma=_A						,      timesteps=_A		)
@dataclass
class SCREAMING_SNAKE_CASE_  (    snake_case_    ):
						__magic_name__:	DDPMSchedulerState
class SCREAMING_SNAKE_CASE_  (    snake_case_				, snake_case_    ):
						__magic_name__:	Dict       					=				[e.name for e in FlaxKarrasDiffusionSchedulers]

						__magic_name__:	jnp.dtype






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







							"""simple docstring"""


							return True






						@register_to_config
						def __init__(					self					:					Tuple						,      _A					:					Dict = 1000						,      _A					:					Tuple = 0.0_0_0_1						,      _A					:					int = 0.0_2						,      _A					:					List[Any] = "linear"						,      _A					:					str = None						,      _A					:					List[Any] = "fixed_small"						,      _A					:					Any = True						,      _A					:					Tuple = "epsilon"						,      _A					:					int = jnp.floataa						,      )							->			Union[str, Any]:







							"""simple docstring"""


							snake_case_						:	str      =		dtype






						def UpperCAmelCase_ (					self					:					List[Any]						,      _A					:					str = None		)							->			DDPMSchedulerState:







							"""simple docstring"""


							if common is None:
								snake_case_						:	Optional[int]      =		CommonSchedulerState.create(self		)

							# standard deviation of the initial noise distribution
							snake_case_						:	Any      =		jnp.array(1.0						,      dtype=self.dtype		)

							snake_case_						:	int      =		jnp.arange(0						,      self.config.num_train_timesteps		).round()[::-1]

							return DDPMSchedulerState.create(
							    common=_A						,      init_noise_sigma=_A						,      timesteps=_A						,      )






						def UpperCAmelCase_ (					self					:					Optional[Any]						,      _A					:					Tuple						,      _A					:					int						,      _A					:					int = None		)							->			jnp.ndarray:







							"""simple docstring"""


							return sample






						def UpperCAmelCase_ (					self					:					Optional[int]						,      _A					:					Dict						,      _A					:					Dict						,      _A					:					Dict = ()		)							->			DDPMSchedulerState:







							"""simple docstring"""


							snake_case_						:	Dict      =		self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							snake_case_						:	int      =		(jnp.arange(0						,      _A		) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=_A						,      timesteps=_A						,      )






						def UpperCAmelCase_ (					self					:					Any						,      _A					:					Union[str, Any]						,      _A					:					Optional[Any]						,      _A					:					Optional[int]=None						,      _A					:					List[str]=None		)							->			int:







							"""simple docstring"""


							snake_case_						:	int      =		state.common.alphas_cumprod[t]
							snake_case_						:	Dict      =		jnp.where(t > 0						,      state.common.alphas_cumprod[t - 1]						,      jnp.array(1.0						,      dtype=self.dtype		)		)

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							snake_case_						:	int      =		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
								snake_case_						:	int      =		self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
								snake_case_						:	List[str]      =		jnp.clip(_A						,      a_min=1E-20		)
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
								snake_case_						:	Optional[Any]      =		jnp.log(jnp.clip(_A						,      a_min=1E-20		)		)
							elif variance_type == "fixed_large":
								snake_case_						:	List[str]      =		state.common.betas[t]
							elif variance_type == "fixed_large_log":
								# Glide max_log
								snake_case_						:	List[Any]      =		jnp.log(state.common.betas[t]		)
							elif variance_type == "learned":
								return predicted_variance
							elif variance_type == "learned_range":
								snake_case_						:	Dict      =		variance
								snake_case_						:	Dict      =		state.common.betas[t]
								snake_case_						:	Any      =		(predicted_variance + 1) / 2
								snake_case_						:	str      =		frac * max_log + (1 - frac) * min_log

							return variance






						def UpperCAmelCase_ (					self					:					Optional[Any]						,      _A					:					Optional[Any]						,      _A					:					Optional[Any]						,      _A					:					List[str]						,      _A					:					str						,      _A					:					Optional[int] = None						,      _A					:					Tuple = True						,      )							->			Union[FlaxDDPMSchedulerOutput, Tuple]:







							"""simple docstring"""


							snake_case_						:	Dict      =		timestep

							if key is None:
								snake_case_						:	Union[str, Any]      =		jax.random.PRNGKey(0		)

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
								snake_case_  ,snake_case_						:	List[str]      =		jnp.split(_A						,      sample.shape[1]						,      axis=1		)
							else:
								snake_case_						:	Any      =		None

							# 1. compute alphas, betas
							snake_case_						:	str      =		state.common.alphas_cumprod[t]
							snake_case_						:	Optional[Any]      =		jnp.where(t > 0						,      state.common.alphas_cumprod[t - 1]						,      jnp.array(1.0						,      dtype=self.dtype		)		)
							snake_case_						:	Tuple      =		1 - alpha_prod_t
							snake_case_						:	Optional[Any]      =		1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
								snake_case_						:	List[str]      =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
								snake_case_						:	str      =		model_output
							elif self.config.prediction_type == "v_prediction":
								snake_case_						:	int      =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
								raise ValueError(
								    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
								    ' for the FlaxDDPMScheduler.'		)

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
								snake_case_						:	str      =		jnp.clip(_A						,      -1						,      1		)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:	List[str]      =		(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:	str      =		state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:	Any      =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
								snake_case_						:	List[str]      =		jax.random.split(_A						,      num=1		)
								snake_case_						:	List[Any]      =		jax.random.normal(_A						,      shape=model_output.shape						,      dtype=self.dtype		)
								return (self._get_variance(_A						,      _A						,      predicted_variance=_A		) ** 0.5) * noise

							snake_case_						:	Optional[int]      =		jnp.where(t > 0						,      random_variance()						,      jnp.zeros(model_output.shape						,      dtype=self.dtype		)		)

							snake_case_						:	Optional[int]      =		pred_prev_sample + variance

							if not return_dict:
								return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=_A						,      state=_A		)






						def UpperCAmelCase_ (					self					:					Optional[int]						,      _A					:					List[str]						,      _A					:					Optional[Any]						,      _A					:					str						,      _A					:					int						,      )							->			jnp.ndarray:







							"""simple docstring"""


							return add_noise_common(state.common						,      _A						,      _A						,      _A		)






						def UpperCAmelCase_ (					self					:					Optional[int]						,      _A					:					List[str]						,      _A					:					Any						,      _A					:					Optional[int]						,      _A					:					List[Any]						,      )							->			jnp.ndarray:







							"""simple docstring"""


							return get_velocity_common(state.common						,      _A						,      _A						,      _A		)






						def __len__(					self					:					str		)							->			Tuple:







							"""simple docstring"""


							return self.config.num_train_timesteps






'''simple docstring'''





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  "▁"

lowercase_		       =  {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_		       =  {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase_		       =  {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowercase_		       =  {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Dict          =       ['input_ids', 'attention_mask']

 __lowerCamelCase    :       List[int]          =       []
 __lowerCamelCase    :       List[int]          =       []



 def __init__(self ,   A ,   A ,   A=None ,   A=None ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<pad>" ,   A="<unk>" ,   A="m2m100" ,   A = None ,   A=8 ,   **A ,   )    ->     None:


      """simple docstring"""



      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

      _a      =  language_codes
      _a      =  FAIRSEQ_LANGUAGE_CODES[language_codes]
      _a      =  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

      _a      =  kwargs.get('''additional_special_tokens''' ,   []       )
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(A       )
          for lang_code in fairseq_language_code
          if self.get_lang_token(A       ) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=A ,   tgt_lang=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   unk_token=A ,   pad_token=A ,   language_codes=A ,   sp_model_kwargs=self.sp_model_kwargs ,   num_madeup_words=A ,   **A ,   )

      _a      =  vocab_file
      _a      =  load_json(A       )
      _a      =  {v: k for k, v in self.encoder.items()}
      _a      =  spm_file
      _a      =  load_spm(A ,   self.sp_model_kwargs       )

      _a      =  len(self.encoder       )

      _a      =  {
          self.get_lang_token(A       ): self.encoder_size + i for i, lang_code in enumerate(A       )
      }
      _a      =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(A       )}
      _a      =  {v: k for k, v in self.lang_token_to_id.items()}

      _a      =  src_lang if src_lang is not None else '''en'''
      _a      =  tgt_lang
      _a      =  self.get_lang_id(self._src_lang       )
      self.set_src_lang_special_tokens(self._src_lang       )

      _a      =  num_madeup_words



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return len(self.encoder       ) + len(self.lang_token_to_id       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang       )



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      return self.sp_model.encode(A ,   out_type=A       )



 def 					a__  (self ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
      return self.encoder.get(A ,   self.encoder[self.unk_token]       )



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
      return self.decoder.get(A ,   self.unk_token       )



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  []
      _a      =  ''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A       ) + token
                _a      =  []
           else:
                current_sub_tokens.append(A       )
      out_string += self.sp_model.decode(A       )
      return out_string.strip()



 def 					a__  (self ,   A ,   A = None ,   A = False       )    ->     List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A ,   token_ids_a=A ,   already_has_special_tokens=A       )

      _a      =  [1] * len(self.prefix_tokens       )
      _a      =  [1] * len(self.suffix_tokens       )
      if token_ids_a is None:
           return prefix_ones + ([0] * len(A       )) + suffix_ones
      return prefix_ones + ([0] * len(A       )) + ([0] * len(A       )) + suffix_ones



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  {self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab



 def __getstate__(self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      return state



 def __setstate__(self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''       ):
           _a      =  {}

      _a      =  load_spm(self.spm_file ,   self.sp_model_kwargs       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  Path(A       )
      if not save_dir.is_dir():
           raise OSError(f'''{save_directory} should be a directory'''       )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
      )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
      )

      save_json(self.encoder ,   A       )

      if os.path.abspath(self.spm_file       ) != os.path.abspath(A       ) and os.path.isfile(self.spm_file       ):
           copyfile(self.spm_file ,   A       )
      elif not os.path.isfile(self.spm_file       ):
           with open(A ,   '''wb'''       ) as fi:
                _a      =  self.sp_model.serialized_model_proto()
                fi.write(A       )

      return (str(A       ), str(A       ))



 def 					a__  (self ,   A ,   A = "en" ,   A = None ,   A = "ro" ,   **A ,   )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  src_lang
      _a      =  tgt_lang
      self.set_src_lang_special_tokens(self.src_lang       )
      return super().prepare_seqaseq_batch(A ,   A ,   **A       )



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Union[str, Any]:


      """simple docstring"""



      if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
      _a      =  src_lang
      _a      =  self(A ,   add_special_tokens=A ,   **A       )
      _a      =  self.get_lang_id(A       )
      _a      =  tgt_lang_id
      return inputs



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


      """simple docstring"""



      self.set_src_lang_special_tokens(self.src_lang       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      self.set_tgt_lang_special_tokens(self.tgt_lang       )



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      return self.lang_code_to_token[lang]



 def 					a__  (self ,   A       )    ->     int:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      return self.lang_token_to_id[lang_token]





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  sentencepiece.SentencePieceProcessor(**__A)
     spm.Load(str(__A))
     return spm




def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(__A  ,	'''r''') as f:
          return json.load(__A)




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     with open(__A  ,	'''w''') as f:
          json.dump(__A  ,	__A  ,	indent=2)



