





import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_    =    get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')



@require_sentencepiece
@require_tokenizers
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				GPTSwaTokenizer
			lowerCamelCase_					:					str	      =				False
			lowerCamelCase_					:					str	      =				True
			lowerCamelCase_					:					List[Any]	      =				False






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:   Union[str, Any]           =  GPTSwaTokenizer(__magic_name__ ,							eos_token='''<unk>''' ,							bos_token='''<unk>''' ,							pad_token='''<unk>'''   )

							tokenizer.save_pretrained(self.tmpdirname   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  '''This is a test'''
							snake_case_						:   str           =  '''This is a test'''
							return input_text, output_text






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  '''<s>'''
							snake_case_						:   Any           =  1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__   ) ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0] ,							'''<unk>'''   )
							self.assertEqual(vocab_keys[1] ,							'''<s>'''   )
							self.assertEqual(vocab_keys[-1] ,							'''j'''   )
							self.assertEqual(len(__magic_name__   ) ,							2000   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size ,							2000   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   str           =  GPTSwaTokenizer(__magic_name__   )

							snake_case_						:   List[str]           =  tokenizer.tokenize('''This is a test'''   )
							self.assertListEqual(__magic_name__ ,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   ) ,							[465, 287, 265, 631, 842]   )

							snake_case_						:   Optional[int]           =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''] ,							)
							# fmt: on

							snake_case_						:   int           =  tokenizer.convert_tokens_to_ids(__magic_name__   )
							self.assertListEqual(
							    __magic_name__ ,							[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,							)

							snake_case_						:   Dict           =  tokenizer.convert_ids_to_tokens(__magic_name__   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']   )
							# fmt: on






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


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  GPTSwaTokenizer(__magic_name__   )
							snake_case_						:   Tuple           =  ['''This is a test''', '''I was born in 92000, and this is falsé.''']
							snake_case_						:   Optional[int]           =  [
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertListEqual(tokenizer.encode_fast(__magic_name__   ) ,							__magic_name__   )

							# Test that decode_fast returns the input text
							for text, token_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertEqual(tokenizer.decode_fast(__magic_name__   ) ,							__magic_name__   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  [
							    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
							    '''Hey there, how are you doing this fine day?''',
							    '''This is a text with a trailing spaces followed by a dot     .''',
							    '''Häj sväjs lillebrör! =)''',
							    '''Det är inget fel på Mr. Cool''',
							]

							# fmt: off
							snake_case_						:   str           =  {'''input_ids''': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=__magic_name__ ,							model_name='''AI-Sweden/gpt-sw3-126m''' ,							sequences=__magic_name__ ,							)







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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  __lowerCAmelCase	:
			lowerCamelCase_					:					CommonSchedulerState

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






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							return cls(common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__   )




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



							if common is None:
											snake_case_						:   Any           =  CommonSchedulerState.create(self   )

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

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

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  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_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  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_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = True ,							)					->       Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[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_						:   Any           =  jnp.split(__magic_name__ ,							sample.shape[1] ,							axis=1   )
							else:
											snake_case_						:   Optional[Any]           =  None

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   str           =  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_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[Any]           =  (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_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-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_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  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_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



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






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
