

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 _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =None
			lowerCamelCase__           =BloomTokenizerFast
			lowerCamelCase__           =BloomTokenizerFast
			lowerCamelCase__           =True
			lowerCamelCase__           =False
			lowerCamelCase__           ='tokenizer_file'
			lowerCamelCase__           ={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}
			def 							__UpperCamelCase (			self							: Tuple	)							->       Tuple:

				"""simple docstring"""

				super().setUp()
				SCREAMING_SNAKE_CASE   :      Any				     =       BloomTokenizerFast.from_pretrained("bigscience/tokenizer"	)
				tokenizer.save_pretrained(self.tmpdirname	)
			def 							__UpperCamelCase (			self							: Any      ,      **a							: Optional[Any]	)							->       Tuple:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map	)
				return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,      **a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_rust_tokenizer()

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

				SCREAMING_SNAKE_CASE   :      int				     =       tokenizer.batch_encode_plus(a	)["input_ids"]
				self.assertListEqual(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer.batch_decode(a	)
				self.assertListEqual(a      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Optional[int]=6	)							->       Dict:

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.rust_tokenizer_class.from_pretrained(a      ,      **a	)
						# tokenizer_r.pad_token = None # Hotfixing padding = None
						# Simple input
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "This is a simple input"
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ["This is a simple input 1", "This is a simple input 2"]
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("This is a simple input", "This is a pair")
						SCREAMING_SNAKE_CASE   :      str				     =       [
						    ("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(a      ,      max_length=a	)
							tokenizer_r.encode_plus(a      ,      max_length=a	)

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

						SCREAMING_SNAKE_CASE   :      List[str]				     =       None  # Hotfixing padding = None
						self.assertRaises(a      ,      tokenizer_r.encode      ,      a      ,      max_length=a      ,      padding="max_length"	)

						# Simple input
						self.assertRaises(a      ,      tokenizer_r.encode_plus      ,      a      ,      max_length=a      ,      padding="max_length"	)

						# Simple input
						self.assertRaises(
						    a      ,      tokenizer_r.batch_encode_plus      ,      a      ,      max_length=a      ,      padding="max_length"      ,      )

						# Pair input
						self.assertRaises(a      ,      tokenizer_r.encode      ,      a      ,      max_length=a      ,      padding="max_length"	)

						# Pair input
						self.assertRaises(a      ,      tokenizer_r.encode_plus      ,      a      ,      max_length=a      ,      padding="max_length"	)

						# Pair input
						self.assertRaises(
						    a      ,      tokenizer_r.batch_encode_plus      ,      a      ,      max_length=a      ,      padding="max_length"      ,      )
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.get_rust_tokenizer()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       load_dataset("xnli"      ,      "all_languages"      ,      split="test"      ,      streaming=a	)

				SCREAMING_SNAKE_CASE   :      str				     =       next(iter(a	)	)["premise"]  # pick up one data
				SCREAMING_SNAKE_CASE   :      str				     =       list(sample_data.values()	)

				SCREAMING_SNAKE_CASE   :      int				     =       list(map(tokenizer.encode      ,      a	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [tokenizer.decode(a      ,      clean_up_tokenization_spaces=a	) for x in output_tokens]
				self.assertListEqual(a      ,      a	)






			def 							__UpperCamelCase (			self							: int	)							->       List[Any]:

				"""simple docstring"""

				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	)


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 _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None  # sigma(t_i)
			@classmethod
			def 							__UpperCamelCase (			cls							: Tuple	)							->       int:

				"""simple docstring"""

				return cls()






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(			self							: List[str]      ,      a							: float = 0.02      ,      a							: float = 100      ,      a							: float = 1.007      ,      a							: float = 80      ,      a							: float = 0.05      ,      a							: float = 50      ,      )							->       Optional[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				return KarrasVeSchedulerState.create()
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: KarrasVeSchedulerState      ,      a							: int      ,      a							: Tuple = ()	)							->       KarrasVeSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       jnp.arange(0      ,      a	)[::-1].copy()
				SCREAMING_SNAKE_CASE   :      int				     =       [
				    (
				        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=a      ,      schedule=jnp.array(a      ,      dtype=jnp.floataa	)      ,      timesteps=a      ,      )
			def 							__UpperCamelCase (			self							: str      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: random.KeyArray      ,      )							->       Tuple[jnp.ndarray, float]:

				"""simple docstring"""

				if self.config.s_min <= sigma <= self.config.s_max:
					SCREAMING_SNAKE_CASE   :      Dict				     =       min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1	)
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       0

				# sample eps ~ N(0, S_noise^2 * I)
				SCREAMING_SNAKE_CASE   :      Any				     =       random.split(a      ,      num=1	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.config.s_noise * random.normal(key=a      ,      shape=sample.shape	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sigma + gamma * sigma
				SCREAMING_SNAKE_CASE   :      str				     =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       sample_hat + sigma_hat * model_output
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (sample_hat - pred_original_sample) / sigma_hat
				SCREAMING_SNAKE_CASE   :      str				     =       sample_hat + (sigma_prev - sigma_hat) * derivative

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

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       sample_prev + sigma_prev * model_output
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample_prev - pred_original_sample) / sigma_prev
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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=a      ,      derivative=a      ,      state=a	)






			def 							__UpperCamelCase (			self							: int      ,      a							: KarrasVeSchedulerState      ,      a							: Optional[Any]      ,      a							: int      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				raise NotImplementedError()