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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCAmelCase   :					Any    =			logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 							lowercase   (						_lowerCAmelCase  ):
		__lowercase			:			Optional[Any]      	=							42
		__lowercase			:			Dict      	=							None






def  A			(   lowercase       ,			lowercase=0.9_9_9       ,			lowercase="cosine"       ,			)  ->    Optional[Any]:
		'''simple docstring'''

		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase					):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowercase					):
						return math.exp(t * -1_2.0					)

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''					)

		UpperCamelCase            =	[]
		for i in range(_lowercase					):
				UpperCamelCase            =	i / num_diffusion_timesteps
				UpperCamelCase            =	(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_lowercase					) / alpha_bar_fn(_lowercase					)       ,			_lowercase					)					)
		return torch.tensor(_lowercase       ,			dtype=torch.floataa					)


class 							lowercase   (						_lowerCAmelCase    ,  _lowerCAmelCase  ):
		__lowercase			:			str      	=							[e.name for e in KarrasDiffusionSchedulers]
		__lowercase			:			List[Any]      	=							1



		@register_to_config
		def __init__(					self							, A_ = 1_000							, A_ = 0.0001							, A_ = 0.02							, A_ = "linear"							, A_ = None							, A_ = "epsilon"							, A_ = "linear"							, A_ = False							, A_ = "linspace"							, A_ = 0							, )    ->   Tuple:
				"""simple docstring"""



				if trained_betas is not None:
						UpperCamelCase            =	torch.tensor(__UpperCAmelCase							, dtype=torch.floataa			)
				elif beta_schedule == "linear":
						UpperCamelCase            =	torch.linspace(__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, dtype=torch.floataa			)
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase            =	(
						    torch.linspace(beta_start**0.5							, beta_end**0.5							, __UpperCAmelCase							, dtype=torch.floataa			) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase            =	betas_for_alpha_bar(__UpperCAmelCase			)
				else:
						raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''			)

				UpperCamelCase            =	1.0 - self.betas
				UpperCamelCase            =	torch.cumprod(self.alphas							, dim=0			)

				UpperCamelCase            =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
				UpperCamelCase            =	np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
				UpperCamelCase            =	torch.from_numpy(__UpperCAmelCase			)

				# setable values
				UpperCamelCase            =	None
				UpperCamelCase            =	np.linspace(0							, num_train_timesteps - 1							, __UpperCAmelCase							, dtype=__UpperCAmelCase			)[::-1].copy()
				UpperCamelCase            =	torch.from_numpy(__UpperCAmelCase			)
				UpperCamelCase            =	False
				UpperCamelCase            =	use_karras_sigmas



		@property
		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""



				if self.config.timestep_spacing in ["linspace", "trailing"]:
						return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   torch.FloatTensor:
				"""simple docstring"""



				if isinstance(__UpperCAmelCase							, torch.Tensor			):
						UpperCamelCase            =	timestep.to(self.timesteps.device			)
				UpperCamelCase            =	(self.timesteps == timestep).nonzero().item()
				UpperCamelCase            =	self.sigmas[step_index]

				UpperCamelCase            =	sample / ((sigma**2 + 1) ** 0.5)

				UpperCamelCase            =	True
				return sample



		def __UpperCamelCase					(					self							, A_							, A_ = None			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	num_inference_steps

				# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
				if self.config.timestep_spacing == "linspace":
						UpperCamelCase            =	np.linspace(0							, self.config.num_train_timesteps - 1							, __UpperCAmelCase							, dtype=__UpperCAmelCase			)[
						    ::-1
						].copy()
				elif self.config.timestep_spacing == "leading":
						UpperCamelCase            =	self.config.num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						UpperCamelCase            =	(np.arange(0							, __UpperCAmelCase			) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase			)
						timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
						UpperCamelCase            =	self.config.num_train_timesteps / self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						UpperCamelCase            =	(np.arange(self.config.num_train_timesteps							, 0							, -step_ratio			)).round().copy().astype(__UpperCAmelCase			)
						timesteps -= 1
				else:
						raise ValueError(
						    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

				UpperCamelCase            =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
				UpperCamelCase            =	np.log(__UpperCAmelCase			)

				if self.config.interpolation_type == "linear":
						UpperCamelCase            =	np.interp(__UpperCAmelCase							, np.arange(0							, len(__UpperCAmelCase			)			)							, __UpperCAmelCase			)
				elif self.config.interpolation_type == "log_linear":
						UpperCamelCase            =	torch.linspace(np.log(sigmas[-1]			)							, np.log(sigmas[0]			)							, num_inference_steps + 1			).exp()
				else:
						raise ValueError(
						    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
						    ' \'linear\' or \'log_linear\''			)

				if self.use_karras_sigmas:
						UpperCamelCase            =	self._convert_to_karras(in_sigmas=__UpperCAmelCase							, num_inference_steps=self.num_inference_steps			)
						UpperCamelCase            =	np.array([self._sigma_to_t(__UpperCAmelCase							, __UpperCAmelCase			) for sigma in sigmas]			)

				UpperCamelCase            =	np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
				UpperCamelCase            =	torch.from_numpy(__UpperCAmelCase			).to(device=__UpperCAmelCase			)
				if str(__UpperCAmelCase			).startswith('mps'			):
						# mps does not support float64
						UpperCamelCase            =	torch.from_numpy(__UpperCAmelCase			).to(__UpperCAmelCase							, dtype=torch.floataa			)
				else:
						UpperCamelCase            =	torch.from_numpy(__UpperCAmelCase			).to(device=__UpperCAmelCase			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	np.log(__UpperCAmelCase			)

				# get distribution
				UpperCamelCase            =	log_sigma - log_sigmas[:, np.newaxis]

				# get sigmas range
				UpperCamelCase            =	np.cumsum((dists >= 0)							, axis=0			).argmax(axis=0			).clip(max=log_sigmas.shape[0] - 2			)
				UpperCamelCase            =	low_idx + 1

				UpperCamelCase            =	log_sigmas[low_idx]
				UpperCamelCase            =	log_sigmas[high_idx]

				# interpolate sigmas
				UpperCamelCase            =	(low - log_sigma) / (low - high)
				UpperCamelCase            =	np.clip(__UpperCAmelCase							, 0							, 1			)

				# transform interpolation to time range
				UpperCamelCase            =	(1 - w) * low_idx + w * high_idx
				UpperCamelCase            =	t.reshape(sigma.shape			)
				return t



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase            =	in_sigmas[-1].item()
				UpperCamelCase            =	in_sigmas[0].item()

				UpperCamelCase            =	7.0  # 7.0 is the value used in the paper
				UpperCamelCase            =	np.linspace(0							, 1							, __UpperCAmelCase			)
				UpperCamelCase            =	sigma_min ** (1 / rho)
				UpperCamelCase            =	sigma_max ** (1 / rho)
				UpperCamelCase            =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_ = 0.0							, A_ = 0.0							, A_ = float('inf'			)							, A_ = 1.0							, A_ = None							, A_ = True							, )    ->   Union[EulerDiscreteSchedulerOutput, Tuple]:
				"""simple docstring"""



				if (
				    isinstance(__UpperCAmelCase							, __UpperCAmelCase			)
				    or isinstance(__UpperCAmelCase							, torch.IntTensor			)
				    or isinstance(__UpperCAmelCase							, torch.LongTensor			)
				):
						raise ValueError(
						    (
						        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
						        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
						        ' one of the `scheduler.timesteps` as a timestep.'
						    )							, )

				if not self.is_scale_input_called:
						logger.warning(
						    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
						    'See `StableDiffusionPipeline` for a usage example.'			)

				if isinstance(__UpperCAmelCase							, torch.Tensor			):
						UpperCamelCase            =	timestep.to(self.timesteps.device			)

				UpperCamelCase            =	(self.timesteps == timestep).nonzero().item()
				UpperCamelCase            =	self.sigmas[step_index]

				UpperCamelCase            =	min(s_churn / (len(self.sigmas			) - 1)							, 2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0

				UpperCamelCase            =	randn_tensor(
				    model_output.shape							, dtype=model_output.dtype							, device=model_output.device							, generator=__UpperCAmelCase			)

				UpperCamelCase            =	noise * s_noise
				UpperCamelCase            =	sigma * (gamma + 1)

				if gamma > 0:
						UpperCamelCase            =	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				# NOTE: "original_sample" should not be an expected prediction_type but is left in for
				# backwards compatibility
				if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
						UpperCamelCase            =	model_output
				elif self.config.prediction_type == "epsilon":
						UpperCamelCase            =	sample - sigma_hat * model_output
				elif self.config.prediction_type == "v_prediction":
						# * c_out + input * c_skip
						UpperCamelCase            =	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
				UpperCamelCase            =	(sample - pred_original_sample) / sigma_hat

				UpperCamelCase            =	self.sigmas[step_index + 1] - sigma_hat

				UpperCamelCase            =	sample + derivative * dt

				if not return_dict:
						return (prev_sample,)

				return EulerDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase							, pred_original_sample=__UpperCAmelCase			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, )    ->   torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase            =	self.sigmas.to(device=original_samples.device							, dtype=original_samples.dtype			)
				if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase			):
						# mps does not support float64
						UpperCamelCase            =	self.timesteps.to(original_samples.device							, dtype=torch.floataa			)
						UpperCamelCase            =	timesteps.to(original_samples.device							, dtype=torch.floataa			)
				else:
						UpperCamelCase            =	self.timesteps.to(original_samples.device			)
						UpperCamelCase            =	timesteps.to(original_samples.device			)

				UpperCamelCase            =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

				UpperCamelCase            =	sigmas[step_indices].flatten()
				while len(sigma.shape			) < len(original_samples.shape			):
						UpperCamelCase            =	sigma.unsqueeze(-1			)

				UpperCamelCase            =	original_samples + noise * sigma
				return noisy_samples



		def __len__(					self			)    ->   Any:
				"""simple docstring"""
				return self.config.num_train_timesteps

"""simple docstring"""




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
					from tokenizers import Tokenizer
					from tokenizers.models import WordPiece

class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					list(tokenizer_class.max_model_input_sizes.keys()  )
									for model_name in sa_models[:1]:
														lowerCamelCase__		:   Tuple  				=					tokenizer_class.from_pretrained(__UpperCAmelCase  )
														self.assertIsNotNone(__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )

														for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
																			lowerCamelCase__		:   str  				=					tokenizer.convert_tokens_to_ids(__UpperCAmelCase  )
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :BatchEncoding      ,__UpperCAmelCase    :Optional[Callable] = None  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					pickle.dumps(__UpperCAmelCase  )
									self.assertIsNotNone(__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					pickle.loads(__UpperCAmelCase  )

									# Ensure is_fast is correctly restored
									self.assertEqual(be_restored.is_fast      ,be_original.is_fast  )

									# Ensure encodings are potentially correctly restored
									if be_original.is_fast:
														self.assertIsNotNone(be_restored.encodings  )
									else:
														self.assertIsNone(be_restored.encodings  )

									# Ensure the keys are the same
									for original_v, restored_v in zip(be_original.values()      ,be_restored.values()  ):
														if equal_op:
																			self.assertTrue(equal_op(__UpperCAmelCase      ,__UpperCAmelCase  )  )
														else:
																			self.assertEqual(__UpperCAmelCase      ,__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__UpperCAmelCase  )







				def   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



									self.assertEqual(TensorType('''tf'''  )      ,TensorType.TENSORFLOW  )
									self.assertEqual(TensorType('''pt'''  )      ,TensorType.PYTORCH  )
									self.assertEqual(TensorType('''np'''  )      ,TensorType.NUMPY  )







				@require_tokenizers
				def   lowercase_		(    self    :List[Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									import numpy as np

									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Optional[int]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									# Python no tensor
									with self.subTest('''BatchEncoding (Python, return_tensors=None)'''  ):
														self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''  )  )

									with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode'''      ,return_tensors=TensorType.NUMPY  )      ,np.array_equal  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''  ):
														self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''  )  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.NUMPY  )      ,np.array_equal  )







				@require_tf
				@require_tokenizers
				def   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :List[Any]  ):
														return tf.reduce_all(tf.equal(__UpperCAmelCase      ,__UpperCAmelCase  )  )

									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[Any]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode'''      ,return_tensors=TensorType.TENSORFLOW  )      ,__UpperCAmelCase  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.TENSORFLOW  )      ,__UpperCAmelCase  )







				@require_torch
				@require_tokenizers
				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Optional[int]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[str]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_p('''Small example to encode'''      ,return_tensors=TensorType.PYTORCH  )      ,torch.equal  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.PYTORCH  )      ,torch.equal  )







				@require_tokenizers
				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Any  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )

									with self.subTest('''Python Tokenizer'''  ):
														self.assertFalse(tokenizer_p('''Small example to_encode'''  ).is_fast  )

									with self.subTest('''Rust Tokenizer'''  ):
														self.assertTrue(tokenizer_r('''Small example to_encode'''  ).is_fast  )







				@require_tokenizers
				def   lowercase_		(    self    :List[str]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   str  				=					tokenizer_r(['''Test''', '''\xad''', '''test''']      ,is_split_into_words=__UpperCAmelCase  )

									self.assertEqual(encoded.word_to_tokens(0  )      ,TokenSpan(start=1      ,end=2  )  )
									self.assertEqual(encoded.word_to_tokens(1  )      ,__UpperCAmelCase  )
									self.assertEqual(encoded.word_to_tokens(2  )      ,TokenSpan(start=2      ,end=3  )  )







				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Tuple  				=					batch.convert_to_tensors(tensor_type='''np'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   List[str]  				=					batch.convert_to_tensors(tensor_type='''np'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Union[str, Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''np'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_torch
				def   lowercase_		(    self    :Optional[int]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Any  				=					batch.convert_to_tensors(tensor_type='''pt'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   Optional[int]  				=					batch.convert_to_tensors(tensor_type='''pt'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''pt'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_tf
				def   lowercase_		(    self    :Optional[Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Union[str, Any]  				=					batch.convert_to_tensors(tensor_type='''tf'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''tf'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   int  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''tf'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_flax
				def   lowercase_		(    self    :Optional[int]  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   str  				=					batch.convert_to_tensors(tensor_type='''jax'''  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(2, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(2,)  )
									# test converting the converted
									with CaptureStderr() as cs:
														lowerCamelCase__		:   Any  				=					batch.convert_to_tensors(tensor_type='''jax'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Tuple  				=					batch.convert_to_tensors(tensor_type='''jax'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







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




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					[{'''input_ids''': np.array([0, 1, 2]  )}, {'''input_ids''': np.array([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Dict  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,np.ndarray  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
									lowerCamelCase__		:   Optional[Any]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''np'''  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,np.ndarray  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )







				@require_torch
				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Union[str, Any]  				=					[{'''input_ids''': torch.tensor([0, 1, 2]  )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,torch.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
									lowerCamelCase__		:   Optional[int]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''pt'''  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,torch.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )







				@require_tf
				def   lowercase_		(    self    :int  )      ->					Optional[int]:




									"""simple docstring"""



									import tensorflow as tf

									lowerCamelCase__		:   List[Any]  				=					[{'''input_ids''': tf.constant([0, 1, 2]  )}, {'''input_ids''': tf.constant([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,tf.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].numpy().tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )
									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,return_tensors='''tf'''  )
									self.assertTrue(isinstance(batch['''input_ids''']      ,tf.Tensor  )  )
									self.assertEqual(batch['''input_ids'''].numpy().tolist()      ,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]  )







				@require_tokenizers
				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase  )







				@require_tokenizers
				def   lowercase_		(    self    :str  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									with tempfile.TemporaryDirectory() as tmpdirname:
														bert_tokenizer.save(os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )
														PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )






