


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_snake_case     :		Optional[int]						=				"""scheduler_config.json"""

class 				a		(lowercase_      ):




							"""simple docstring"""


							__UpperCAmelCase    :							List[str]       =  1
							__UpperCAmelCase    :							Any       =  2
							__UpperCAmelCase    :							Optional[Any]       =  3
							__UpperCAmelCase    :							Optional[Any]       =  4
							__UpperCAmelCase    :							Optional[int]       =  5

@dataclass
class 				a		(lowercase_      ):




							"""simple docstring"""


							__UpperCAmelCase    :							jnp.ndarray

class 				a		:




							"""simple docstring"""


							__UpperCAmelCase    :							Dict       =  SCHEDULER_CONFIG_NAME
							__UpperCAmelCase    :							Tuple       =  ['''dtype''']
							__UpperCAmelCase    :							Any       =  []
							__UpperCAmelCase    :							int       =  True

							@classmethod
							def     __snake_case					(							cls       :  Union[str, Any]    ,	lowerCamelCase       :  Optional[Any] = None    ,	lowerCamelCase       :  Tuple = None    ,	lowerCamelCase       :  Optional[Any]=False    ,	**lowerCamelCase       :  Optional[int]    ,	)	->    Dict:
														__snake_case ,			__snake_case		:			Union[str, Any]							 =   cls.load_config(
														    pretrained_model_name_or_path=lowerCamelCase    ,	subfolder=lowerCamelCase    ,	return_unused_kwargs=lowerCamelCase    ,	**lowerCamelCase    ,	)
														__snake_case ,			__snake_case		:			List[Any]							 =   cls.from_config(lowerCamelCase    ,	return_unused_kwargs=lowerCamelCase    ,	**lowerCamelCase				)

														if hasattr(lowerCamelCase    ,	"create_state"				) and getattr(lowerCamelCase    ,	"has_state"    ,	lowerCamelCase				):
																					__snake_case		:			Any							 =   scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state

							def     __snake_case					(							self       :  Dict    ,	lowerCamelCase       :  Tuple    ,	lowerCamelCase       :  int = False    ,	**lowerCamelCase       :  Tuple				)	->    str:
														self.save_config(save_directory=lowerCamelCase    ,	push_to_hub=lowerCamelCase    ,	**lowerCamelCase				)

							@property
							def     __snake_case					(							self       :  Optional[Any]				)	->    int:
														return self._get_compatibles()






							@classmethod
							def     __snake_case					(							cls       :  int				)	->    Tuple:
														__snake_case		:			Tuple							 =   list(set([cls.__name__] + cls._compatibles				)				)
														__snake_case		:			int							 =   importlib.import_module(__name__.split("."				)[0]				)
														__snake_case		:			List[str]							 =   [
														    getattr(lowerCamelCase    ,	lowerCamelCase				) for c in compatible_classes_str if hasattr(lowerCamelCase    ,	lowerCamelCase				)
														]
														return compatible_classes







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							assert len(UpperCamelCase__	) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase__	) - x.ndim)	)							,	UpperCamelCase__	)







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase=0.9_9_9							,	__lowerCamelCase=jnp.floataa	):






							def alpha_bar(__lowerCamelCase	):
														return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

							__snake_case		:			List[str]							 =   []
							for i in range(UpperCamelCase__	):
														__snake_case		:			Tuple							 =   i / num_diffusion_timesteps
														__snake_case		:			int							 =   (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(UpperCamelCase__	) / alpha_bar(UpperCamelCase__	)							,	UpperCamelCase__	)	)
							return jnp.array(UpperCamelCase__							,	dtype=UpperCamelCase__	)

@flax.struct.dataclass
class 				a		:




							"""simple docstring"""


							__UpperCAmelCase    :							jnp.ndarray
							__UpperCAmelCase    :							jnp.ndarray
							__UpperCAmelCase    :							jnp.ndarray

							@classmethod
							def     __snake_case					(							cls       :  int    ,	lowerCamelCase       :  Optional[int]				)	->    Union[str, Any]:
														__snake_case		:			Tuple							 =   scheduler.config

														if config.trained_betas is not None:
																					__snake_case		:			Tuple							 =   jnp.asarray(config.trained_betas    ,	dtype=scheduler.dtype				)
														elif config.beta_schedule == "linear":
																					__snake_case		:			Optional[int]							 =   jnp.linspace(config.beta_start    ,	config.beta_end    ,	config.num_train_timesteps    ,	dtype=scheduler.dtype				)
														elif config.beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					__snake_case		:			Optional[Any]							 =   (
																					    jnp.linspace(
																					        config.beta_start**0.5    ,	config.beta_end**0.5    ,	config.num_train_timesteps    ,	dtype=scheduler.dtype				)
																					    ** 2
																					)
														elif config.beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					__snake_case		:			int							 =   betas_for_alpha_bar(config.num_train_timesteps    ,	dtype=scheduler.dtype				)
														else:
																					raise NotImplementedError(
																					    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'				)

														__snake_case		:			str							 =   1.0 - betas

														__snake_case		:			List[str]							 =   jnp.cumprod(lowerCamelCase    ,	axis=0				)

														return cls(
														    alphas=lowerCamelCase    ,	betas=lowerCamelCase    ,	alphas_cumprod=lowerCamelCase    ,	)







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			List[Any]							 =   state.alphas_cumprod

							__snake_case		:			Optional[Any]							 =   alphas_cumprod[timesteps] ** 0.5
							__snake_case		:			Optional[int]							 =   sqrt_alpha_prod.flatten()
							__snake_case		:			List[str]							 =   broadcast_to_shape_from_left(UpperCamelCase__							,	original_samples.shape	)

							__snake_case		:			Optional[int]							 =   (1 - alphas_cumprod[timesteps]) ** 0.5
							__snake_case		:			Optional[int]							 =   sqrt_one_minus_alpha_prod.flatten()
							__snake_case		:			Union[str, Any]							 =   broadcast_to_shape_from_left(UpperCamelCase__							,	original_samples.shape	)

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case ,			__snake_case		:			Any							 =   get_sqrt_alpha_prod(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__	)
							__snake_case		:			Tuple							 =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case ,			__snake_case		:			str							 =   get_sqrt_alpha_prod(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__	)
							__snake_case		:			Optional[Any]							 =   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




				def     a							(					self      ,   **snake_case       ):
											kwargs.update(self.special_tokens_map       )
											return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

											snake_case_							=    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											snake_case_							=    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case=6       ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									snake_case_							=    self.rust_tokenizer_class.from_pretrained(snake_case      ,   **snake_case       )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									snake_case_							=    'This is a simple input'
																									snake_case_							=    ['This is a simple input 1', 'This is a simple input 2']
																									snake_case_							=    ('This is a simple input', 'This is a pair')
																									snake_case_							=    [
																									    ('This is a simple input 1', 'This is a simple input 2'),
																									    ('This is a simple pair 1', 'This is a simple pair 2'),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode_plus(snake_case      ,   max_length=snake_case       )

																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.encode(snake_case      ,   max_length=snake_case       )
																																tokenizer_r.batch_encode_plus(snake_case      ,   max_length=snake_case       )
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding'       )

																									snake_case_							=    None  # Hotfixing padding = None
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Simple input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(snake_case      ,   tokenizer_r.encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'       )

																									# Pair input
																									self.assertRaises(
																									    snake_case      ,   tokenizer_r.batch_encode_plus      ,   snake_case      ,   max_length=snake_case      ,   padding='max_length'      ,   )




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
											# any sequence length constraints. This test of the parent class will fail since it relies on the
											# maximum sequence length of the positoonal embeddings.
											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )      ,   1       )
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )      ,   1       )




