





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   A							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  =MgpstrTokenizer
      _snake_case				  =False
      _snake_case				  ={}
      _snake_case				  =False



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     List[str]:
         '''simple docstring'''

         super().setUp()

         # fmt: off
         UpperCAmelCase_           =["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
         # fmt: on
         UpperCAmelCase_           =dict(zip(_lowerCAmelCase		,	range(len(_lowerCAmelCase							)							)							)							)

         UpperCAmelCase_           =os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES["vocab_file"]							)
         with open(self.vocab_file		,	"w"		,	encoding="utf-8"							) as fp:
            fp.write(json.dumps(_lowerCAmelCase							) + "\n"							)



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	**_lowerCAmelCase:    Tuple							)    ->     str:
         '''simple docstring'''

         return MgpstrTokenizer.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    Tuple							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           ="tester"
         UpperCAmelCase_           ="tester"
         return input_text, output_text



      @unittest.skip("MGP-STR always lower cases letters."							)
      def 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         pass



      def 						lowerCAmelCase__     (						self:    int							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers(do_lower_case=_lowerCAmelCase							)
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               UpperCAmelCase_           ="[SPECIAL_TOKEN]"

               tokenizer.add_special_tokens({"cls_token": special_token}							)
               UpperCAmelCase_           =tokenizer.encode([special_token]		,	add_special_tokens=_lowerCAmelCase							)
               self.assertEqual(len(_lowerCAmelCase							)		,	1							)

               UpperCAmelCase_           =tokenizer.decode(_lowerCAmelCase		,	skip_special_tokens=_lowerCAmelCase							)
               self.assertTrue(special_token not in decoded							)



      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(F'{tokenizer.__class__.__name__}'							):
               UpperCAmelCase_    ,							UpperCAmelCase_           =self.get_input_output_texts(_lowerCAmelCase							)

               UpperCAmelCase_           =tokenizer.tokenize(_lowerCAmelCase							)
               UpperCAmelCase_           =tokenizer.convert_tokens_to_ids(_lowerCAmelCase							)
               UpperCAmelCase_           =tokenizer.encode(_lowerCAmelCase		,	add_special_tokens=_lowerCAmelCase							)
               self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)

               UpperCAmelCase_           =tokenizer.convert_ids_to_tokens(_lowerCAmelCase							)
               self.assertNotEqual(len(_lowerCAmelCase							)		,	0							)
               UpperCAmelCase_           =tokenizer.decode(_lowerCAmelCase							)
               self.assertIsInstance(_lowerCAmelCase		,	_lowerCAmelCase							)

               self.assertEqual(text_a.replace(" "		,	""							)		,	_lowerCAmelCase							)



      @unittest.skip("MGP-STR tokenizer only handles one sequence."							)
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     List[Any]:
         '''simple docstring'''

         pass






      @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"							)
      def 						lowerCAmelCase__     (						self:    str							)    ->     Union[str, Any]:
         '''simple docstring'''

         pass







'''simple docstring'''

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class    snake_case     :






				"""simple docstring"""
				# setable values
				_a    	=     None
				_a    	=     None
				_a    	=     None  # sigma(t_i)



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											return cls()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42
				_a    	=     42






class    snake_case     (      lowercase_,						lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							int:
											return True



				@register_to_config
				def __init__(    self,     _lowercase = 0.02,     _lowercase = 100,     _lowercase = 1.007,     _lowercase = 80,     _lowercase = 0.05,     _lowercase = 50,     )     ->							List[Any]:
											pass



				def   a__					(    self       )     ->							List[Any]:
											return KarrasVeSchedulerState.create()



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = ()       )     ->							KarrasVeSchedulerState:
											SCREAMING_SNAKE_CASE_				=				jnp.arange(0,     _lowercase       )[::-1].copy()
											SCREAMING_SNAKE_CASE_				=				[
											    (
											        self.config.sigma_max**2
											        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
											    )
											    for i in timesteps
											]

											return state.replace(
											    num_inference_steps=_lowercase,     schedule=jnp.array(_lowercase,     dtype=jnp.floataa       ),     timesteps=_lowercase,     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple[jnp.ndarray, float]:
											if self.config.s_min <= sigma <= self.config.s_max:
																		SCREAMING_SNAKE_CASE_				=				min(self.config.s_churn / state.num_inference_steps,     2**0.5 - 1       )
											else:
																		SCREAMING_SNAKE_CASE_				=				0

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE_				=				random.split(_lowercase,     num=1       )
											SCREAMING_SNAKE_CASE_				=				self.config.s_noise * random.normal(key=_lowercase,     shape=sample.shape       )
											SCREAMING_SNAKE_CASE_				=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE_				=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * derivative

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											raise NotImplementedError()





