

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     lowercase_       :
      UpperCamelCase_     :						CommonSchedulerState

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

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



      @classmethod
      def 		UpperCamelCase_						(     cls :							Dict     ,   A__ :							int     ,   A__ :							Union[str, Any]     ,   A__ :							str     ,   A__ :							List[Any]						)       ->	Optional[Any]:
        return cls(common=__A     ,   init_noise_sigma=__A     ,   timesteps=__A     ,   sigmas=__A						)



@dataclass
class     lowercase_       (    A__	):
      UpperCamelCase_     :						LMSDiscreteSchedulerState



class     lowercase_       (    A__  ,				A__	):
      UpperCamelCase_     :						Optional[Any]		 =							[e.name for e in FlaxKarrasDiffusionSchedulers]

      UpperCamelCase_     :						jnp.dtype



      @property
      def 		UpperCamelCase_						(     self :							Tuple						)       ->	Dict:
        return True



      @register_to_config
      def __init__(     self :							Optional[Any]     ,   A__ :							List[Any] = 1000     ,   A__ :							Union[str, Any] = 0.0001     ,   A__ :							int = 0.02     ,   A__ :							Dict = "linear"     ,   A__ :							int = None     ,   A__ :							Any = "epsilon"     ,   A__ :							Tuple = jnp.floataa     ,   )       ->	Any:
        _snake_case		=				dtype



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							str = None						)       ->	LMSDiscreteSchedulerState:
        if common is None:
          _snake_case		=				CommonSchedulerState.create(self						)

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

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

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



      def 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							Any     ,   A__ :							Optional[int]     ,   A__ :							Tuple						)       ->	jnp.ndarray:
        (_snake_case			)		=				jnp.where(state.timesteps == timestep     ,   size=1						)
        _snake_case		=				step_index[0]

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



      def 		UpperCamelCase_						(     self :							int     ,   A__ :							Union[str, Any]     ,   A__ :							Dict     ,   A__ :							Dict     ,   A__ :							Any						)       ->	int:

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

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

        return integrated_coeff



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Any     ,   A__ :							Union[str, Any]     ,   A__ :							List[str] = ()						)       ->	LMSDiscreteSchedulerState:
        _snake_case		=				jnp.linspace(self.config.num_train_timesteps - 1     ,   0     ,   __A     ,   dtype=self.dtype						)

        _snake_case		=				jnp.floor(__A						).astype(jnp.intaa						)
        _snake_case		=				jnp.ceil(__A						).astype(jnp.intaa						)

        _snake_case		=				jnp.mod(__A     ,   1.0						)

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

        _snake_case		=				timesteps.astype(jnp.intaa						)

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

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



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							List[str]     ,   A__ :							Union[str, Any]     ,   A__ :							List[Any]     ,   A__ :							Dict     ,   A__ :							Dict = 4     ,   A__ :							str = 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'''						)

        _snake_case		=				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          _snake_case		=				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          _snake_case		=				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
        _snake_case		=				(sample - pred_original_sample) / sigma
        _snake_case		=				state.replace(derivatives=jnp.append(state.derivatives     ,   __A						)						)
        if len(state.derivatives						) > order:
          _snake_case		=				state.replace(derivatives=jnp.delete(state.derivatives     ,   0						)						)

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

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

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=__A     ,   state=__A						)



      def 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							Any     ,   A__ :							int     ,   A__ :							Union[str, Any]     ,   A__ :							Tuple     ,   )       ->	jnp.ndarray:
        _snake_case		=				state.sigmas[timesteps].flatten()
        _snake_case		=				broadcast_to_shape_from_left(__A     ,   noise.shape						)

        _snake_case		=				original_samples + noise * sigma

        return noisy_samples







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



"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      _SCREAMING_SNAKE_CASE   (     A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		GPTNeoXJapaneseTokenizer
			UpperCAmelCase_    :List[str]												=		False
			UpperCAmelCase_    :int												=		{"do_clean_text": False, "add_prefix_space": False}






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						super().setUp()

						lowerCAmelCase_       :int     	=    [
						    """こん""",
						    """こんに""",
						    """にちは""",
						    """ばんは""",
						    """世界,㔺界""",
						    """、""",
						    """。""",
						    """<BR>""",
						    """<SP>""",
						    """<TAB>""",
						    """<URL>""",
						    """<EMAIL>""",
						    """<TEL>""",
						    """<DATE>""",
						    """<PRICE>""",
						    """<BLOCK>""",
						    """<KIGOU>""",
						    """<U2000U2BFF>""",
						    """<|emoji1|>""",
						    """<unk>""",
						    """<|startoftext|>""",
						    """<|endoftext|>""",
						]
						lowerCAmelCase_       :str     	=    {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
						lowerCAmelCase_       :Dict     	=    {"""unk_token""": """<unk>"""}

						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""vocab_file"""]			)
						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""emoji_file"""]			)
						with open(self.vocab_file			,  """w"""			,  encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
						with open(self.emoji_file			,  """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__A			)			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	str:
						kwargs.update(self.special_tokens_map			)
						return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    """こんにちは、世界。 \nこんばんは、㔺界。😀"""
						lowerCAmelCase_       :List[Any]     	=    """こんにちは、世界。 \nこんばんは、世界。😀"""
						return input_text, output_text






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :Optional[int]     	=    self.get_input_output_texts(__A			)
						lowerCAmelCase_       :List[Any]     	=    tokenizer.encode(__A			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Optional[Any]     	=    tokenizer.decode(__A			,  clean_up_tokenization_spaces=__A			)
						return text, ids






			def 					__lowerCAmelCase					(   self			)     ->	str:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :int     	=    self.get_tokenizer()

						# Testing tokenization
						lowerCAmelCase_       :List[str]     	=    """こんにちは、世界。　こんばんは、㔺界。"""
						lowerCAmelCase_       :int     	=    ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
						lowerCAmelCase_       :List[Any]     	=    tokenizer.tokenize(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids without special tokens
						lowerCAmelCase_       :List[str]     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						lowerCAmelCase_       :Dict     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids with special tokens
						lowerCAmelCase_       :Tuple     	=    tokens + [tokenizer.unk_token]
						lowerCAmelCase_       :str     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						lowerCAmelCase_       :Any     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :str     	=    self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.encode("""ありがとう。"""			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Dict     	=    tokenizer.encode("""どういたしまして。"""			,  add_special_tokens=__A			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.build_inputs_with_special_tokens(__A			)
						lowerCAmelCase_       :Optional[int]     	=    tokenizer.build_inputs_with_special_tokens(__A			,  __A			)

						assert encoded_sentence == ids_a
						assert encoded_pair == ids_a + ids_a






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						# tokenizer has no padding token
						pass






