




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[int]=0.9_9_9  ,							lowerCamelCase_ :     Optional[Any]="cosine"  ,							)       ->  str:







			"""simple docstring"""





			if alpha_transform_type == "cosine":

						def alpha_bar_fn(lowerCamelCase_ :     Any  ):
									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(lowerCamelCase_ :     Tuple  ):
									return math.exp(t * -12.0  )

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

			SCREAMING_SNAKE_CASE_   : List[Any]					  =      []
			for i in range(__UpperCAmelCase  ):
						SCREAMING_SNAKE_CASE_   : int					  =      i / num_diffusion_timesteps
						SCREAMING_SNAKE_CASE_   : List[str]					  =      (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase  ) / alpha_bar_fn(__UpperCAmelCase  )  ,							__UpperCAmelCase  )  )
			return torch.tensor(__UpperCAmelCase  ,							dtype=torch.floataa  )







class 				lowerCAmelCase_       (					_lowerCamelCase						,     _lowerCamelCase							):
				__a     :       Tuple						=     [e.name for e in KarrasDiffusionSchedulers]
				__a     :       Optional[Any]						=     2






				@register_to_config
				def __init__(			self      ,snake_case__ = 1000      ,snake_case__ = 0.00085      ,snake_case__ = 0.012      ,snake_case__ = "linear"      ,snake_case__ = None      ,snake_case__ = "epsilon"      ,snake_case__ = "linspace"      ,snake_case__ = 0      ,):
							if trained_betas is not None:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.tensor(lowercase_      ,dtype=torch.floataa )
							elif beta_schedule == "linear":
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.linspace(lowercase_      ,lowercase_      ,lowercase_      ,dtype=torch.floataa )
							elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										SCREAMING_SNAKE_CASE_   : Any					  =      (
										    torch.linspace(beta_start**0.5      ,beta_end**0.5      ,lowercase_      ,dtype=torch.floataa ) ** 2
										)
							elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										SCREAMING_SNAKE_CASE_   : Any					  =      betas_for_alpha_bar(lowercase_ )
							else:
										raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      1.0 - self.betas
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.cumprod(self.alphas      ,dim=0 )

							#  set all values
							self.set_timesteps(lowercase_      ,lowercase_      ,lowercase_ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None ):
							if schedule_timesteps is None:
										SCREAMING_SNAKE_CASE_   : int					  =      self.timesteps

							SCREAMING_SNAKE_CASE_   : Dict					  =      (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter ) == 0:
										SCREAMING_SNAKE_CASE_   : str					  =      1 if len(lowercase_ ) > 1 else 0
							else:
										SCREAMING_SNAKE_CASE_   : int					  =      timestep.cpu().item() if torch.is_tensor(lowercase_ ) else timestep
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self._index_counter[timestep_int]

							return indices[pos].item()






				@property
				def 		snake_case	(			self ):
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
										return self.sigmas.max()

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.index_for_timestep(lowercase_ )

							if self.state_in_first_order:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.sigmas[step_index]
							else:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.sigmas_interpol[step_index]

							SCREAMING_SNAKE_CASE_   : Any					  =      sample / ((sigma**2 + 1) ** 0.5)
							return sample






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = None      ,):
							SCREAMING_SNAKE_CASE_   : Any					  =      num_inference_steps

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      np.linspace(0      ,num_train_timesteps - 1      ,lowercase_      ,dtype=lowercase_ )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      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
										SCREAMING_SNAKE_CASE_   : List[str]					  =      (np.arange(0      ,lowercase_ ) * step_ratio).round()[::-1].copy().astype(lowercase_ )
										timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
										SCREAMING_SNAKE_CASE_   : str					  =      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
										SCREAMING_SNAKE_CASE_   : Any					  =      (np.arange(lowercase_      ,0      ,-step_ratio )).round().copy().astype(lowercase_ )
										timesteps -= 1
							else:
										raise ValueError(
										    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.from_numpy(np.log(lowercase_ ) ).to(lowercase_ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      np.interp(lowercase_      ,np.arange(0      ,len(lowercase_ ) )      ,lowercase_ )
							SCREAMING_SNAKE_CASE_   : Any					  =      np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.from_numpy(lowercase_ ).to(device=lowercase_ )

							# interpolate sigmas
							SCREAMING_SNAKE_CASE_   : int					  =      sigmas.log().lerp(sigmas.roll(1 ).log()      ,0.5 ).exp()

							SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

							if str(lowercase_ ).startswith('mps' ):
										# mps does not support float64
										SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.from_numpy(lowercase_ ).to(lowercase_      ,dtype=torch.floataa )
							else:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.from_numpy(lowercase_ ).to(lowercase_ )

							# interpolate timesteps
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.sigma_to_t(lowercase_ ).to(lowercase_      ,dtype=timesteps.dtype )
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])      ,dim=-1 ).flatten()

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.cat([timesteps[:1], interleaved_timesteps] )

							SCREAMING_SNAKE_CASE_   : Any					  =      None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      defaultdict(lowercase_ )






				def 		snake_case	(			self      ,snake_case__ ):
							# get log sigma
							SCREAMING_SNAKE_CASE_   : int					  =      sigma.log()

							# get distribution
							SCREAMING_SNAKE_CASE_   : List[str]					  =      log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
							SCREAMING_SNAKE_CASE_   : int					  =      low_idx + 1

							SCREAMING_SNAKE_CASE_   : Any					  =      self.log_sigmas[low_idx]
							SCREAMING_SNAKE_CASE_   : int					  =      self.log_sigmas[high_idx]

							# interpolate sigmas
							SCREAMING_SNAKE_CASE_   : Any					  =      (low - log_sigma) / (low - high)
							SCREAMING_SNAKE_CASE_   : List[str]					  =      w.clamp(0      ,1 )

							# transform interpolation to time range
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (1 - w) * low_idx + w * high_idx
							SCREAMING_SNAKE_CASE_   : Tuple					  =      t.view(sigma.shape )
							return t






				@property
				def 		snake_case	(			self ):
							return self.sample is None






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ = True      ,):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.index_for_timestep(lowercase_ )

							# advance index counter by 1
							SCREAMING_SNAKE_CASE_   : Any					  =      timestep.cpu().item() if torch.is_tensor(lowercase_ ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.sigmas[step_index]
										SCREAMING_SNAKE_CASE_   : str					  =      self.sigmas_interpol[step_index + 1]
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.sigmas[step_index + 1]
							else:
										# 2nd order / KDPM2's method
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.sigmas[step_index - 1]
										SCREAMING_SNAKE_CASE_   : Tuple					  =      self.sigmas_interpol[step_index]
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							SCREAMING_SNAKE_CASE_   : str					  =      0
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
										SCREAMING_SNAKE_CASE_   : str					  =      sigma_hat if self.state_in_first_order else sigma_interpol
										SCREAMING_SNAKE_CASE_   : int					  =      sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      sigma_hat if self.state_in_first_order else sigma_interpol
										SCREAMING_SNAKE_CASE_   : Tuple					  =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
										    sample / (sigma_input**2 + 1)
										)
							elif self.config.prediction_type == "sample":
										raise NotImplementedError('prediction_type not implemented yet: sample' )
							else:
										raise ValueError(
										    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )

							if self.state_in_first_order:
										# 2. Convert to an ODE derivative for 1st order
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      (sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										SCREAMING_SNAKE_CASE_   : str					  =      sigma_interpol - sigma_hat

										# store for 2nd order step
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      sample
							else:
										# DPM-Solver-2
										# 2. Convert to an ODE derivative for 2nd order
										SCREAMING_SNAKE_CASE_   : Dict					  =      (sample - pred_original_sample) / sigma_interpol

										# 3. delta timestep
										SCREAMING_SNAKE_CASE_   : List[str]					  =      sigma_next - sigma_hat

										SCREAMING_SNAKE_CASE_   : str					  =      self.sample
										SCREAMING_SNAKE_CASE_   : Dict					  =      None

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      sample + derivative * dt

							if not return_dict:
										return (prev_sample,)

							return SchedulerOutput(prev_sample=lowercase_ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.sigmas.to(device=original_samples.device      ,dtype=original_samples.dtype )
							if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_ ):
										# mps does not support float64
										SCREAMING_SNAKE_CASE_   : Tuple					  =      self.timesteps.to(original_samples.device      ,dtype=torch.floataa )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      timesteps.to(original_samples.device      ,dtype=torch.floataa )
							else:
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.timesteps.to(original_samples.device )
										SCREAMING_SNAKE_CASE_   : Any					  =      timesteps.to(original_samples.device )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.index_for_timestep(lowercase_      ,lowercase_ ) for t in timesteps]

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

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      original_samples + noise * sigma
							return noisy_samples






				def __len__(			self ):
							return self.config.num_train_timesteps









'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import (
							    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    FlaubertConfig,
							    TFFlaubertForMultipleChoice,
							    TFFlaubertForQuestionAnsweringSimple,
							    TFFlaubertForSequenceClassification,
							    TFFlaubertForTokenClassification,
							    TFFlaubertModel,
							    TFFlaubertWithLMHeadModel,
							)






class 		a							:


				def __init__(						self       :   Tuple			,							lowercase_       :   Any			,							):
									snake_case_		     =							parent
									snake_case_		     =							13
									snake_case_		     =							7
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							False
									snake_case_		     =							False
									snake_case_		     =							False
									snake_case_		     =							2
									snake_case_		     =							99
									snake_case_		     =							0
									snake_case_		     =							32
									snake_case_		     =							2
									snake_case_		     =							4
									snake_case_		     =							0.1
									snake_case_		     =							0.1
									snake_case_		     =							512
									snake_case_		     =							16
									snake_case_		     =							2
									snake_case_		     =							0.02
									snake_case_		     =							3
									snake_case_		     =							4
									snake_case_		     =							'''last'''
									snake_case_		     =							True
									snake_case_		     =							None
									snake_case_		     =							0


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )
									snake_case_		     =							random_attention_mask([self.batch_size, self.seq_length]			,							dtype=tf.floataa   )

									snake_case_		     =							None
									if self.use_input_lengths:
														snake_case_		     =							(
														    ids_tensor([self.batch_size]			,							vocab_size=2   ) + self.seq_length - 2
														)  # small variation of seq_length

									snake_case_		     =							None
									if self.use_token_type_ids:
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.n_langs   )

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							2			,							dtype=tf.floataa   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

									snake_case_		     =							FlaubertConfig(
									    vocab_size=self.vocab_size			,							n_special=self.n_special			,							emb_dim=self.hidden_size			,							n_layers=self.num_hidden_layers			,							n_heads=self.num_attention_heads			,							dropout=self.hidden_dropout_prob			,							attention_dropout=self.attention_probs_dropout_prob			,							gelu_activation=self.gelu_activation			,							sinusoidal_embeddings=self.sinusoidal_embeddings			,							asm=self.asm			,							causal=self.causal			,							n_langs=self.n_langs			,							max_position_embeddings=self.max_position_embeddings			,							initializer_range=self.initializer_range			,							summary_type=self.summary_type			,							use_proj=self.use_proj			,							bos_token_id=self.bos_token_id			,							)

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)


				def     A_					(						self       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   Optional[int]			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   str			,							lowercase_       :   int			,							):
									snake_case_		     =							TFFlaubertModel(config=lowercase_   )
									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
									snake_case_		     =							model(lowercase_   )

									snake_case_		     =							[input_ids, input_mask]
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)   )


				def     A_					(						self       :   List[str]			,							lowercase_       :   int			,							lowercase_       :   List[str]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   List[Any]			,							):
									snake_case_		     =							TFFlaubertWithLMHeadModel(lowercase_   )

									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
									snake_case_		     =							model(lowercase_   )

									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.vocab_size)   )


				def     A_					(						self       :   str			,							lowercase_       :   Dict			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict			,							lowercase_       :   Any			,							lowercase_       :   str			,							lowercase_       :   List[str]			,							lowercase_       :   List[Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   str			,							):
									snake_case_		     =							TFFlaubertForQuestionAnsweringSimple(lowercase_   )

									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths}

									snake_case_		     =							model(lowercase_   )

									self.parent.assertEqual(result.start_logits.shape			,							(self.batch_size, self.seq_length)   )
									self.parent.assertEqual(result.end_logits.shape			,							(self.batch_size, self.seq_length)   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Any			,							lowercase_       :   int			,							lowercase_       :   List[Any]			,							lowercase_       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Dict			,							):
									snake_case_		     =							TFFlaubertForSequenceClassification(lowercase_   )

									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths}

									snake_case_		     =							model(lowercase_   )

									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)   )


				def     A_					(						self       :   List[Any]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Any			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Union[str, Any]			,							):
									snake_case_		     =							self.num_labels
									snake_case_		     =							TFFlaubertForTokenClassification(config=lowercase_   )
									snake_case_		     =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.num_labels)   )


				def     A_					(						self       :   Dict			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   List[str]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any			,							):
									snake_case_		     =							self.num_choices
									snake_case_		     =							TFFlaubertForMultipleChoice(config=lowercase_   )
									snake_case_		     =							tf.tile(tf.expand_dims(lowercase_			,							1   )			,							(1, self.num_choices, 1)   )
									snake_case_		     =							tf.tile(tf.expand_dims(lowercase_			,							1   )			,							(1, self.num_choices, 1)   )
									snake_case_		     =							tf.tile(tf.expand_dims(lowercase_			,							1   )			,							(1, self.num_choices, 1)   )
									snake_case_		     =							{
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									    '''token_type_ids''': multiple_choice_token_type_ids,
									}
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_choices)   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,
									)		     =							config_and_inputs
									snake_case_		     =							{
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''langs''': token_type_ids,
									    '''lengths''': input_lengths,
									}
									return config, inputs_dict







@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        TFFlaubertModel,
				        TFFlaubertWithLMHeadModel,
				        TFFlaubertForSequenceClassification,
				        TFFlaubertForQuestionAnsweringSimple,
				        TFFlaubertForTokenClassification,
				        TFFlaubertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_           =						(
				    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				snake_case_           =						(
				    {
				        "feature-extraction": TFFlaubertModel,
				        "fill-mask": TFFlaubertWithLMHeadModel,
				        "question-answering": TFFlaubertForQuestionAnsweringSimple,
				        "text-classification": TFFlaubertForSequenceClassification,
				        "token-classification": TFFlaubertForTokenClassification,
				        "zero-shot": TFFlaubertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   str			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Optional[Any]   ):
									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith('''Fast'''   )
									):
														# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
														# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
														# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
														return True

									return False


				def     A_					(						self       :   int   ):
									snake_case_		     =							TFFlaubertModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							emb_dim=37   )


				def     A_					(						self       :   List[str]   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_   )


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase_   )


				@slow
				def     A_					(						self       :   Optional[int]   ):
									for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFFlaubertModel.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







@require_tf
@require_sentencepiece
@require_tokenizers
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   int   ):
									snake_case_		     =							TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''   )

									snake_case_		     =							tf.convert_to_tensor(
									    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]			,							dtype=tf.intaa			,							)  # "J'aime flaubert !"

									snake_case_		     =							model(lowercase_   )[0]
									snake_case_		     =							tf.TensorShape((1, 8, 512)   )
									self.assertEqual(output.shape			,							lowercase_   )
									# compare the actual values for a slice.
									snake_case_		     =							tf.convert_to_tensor(
									    [
									        [
									            [-1.876_8773, -1.56_6555, 0.2707_2418],
									            [-1.692_0038, -0.587_3505, 1.932_9599],
									            [-2.956_3985, -1.699_3835, 1.797_2052],
									        ]
									    ]			,							dtype=tf.floataa			,							)

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,							expected_slice.numpy()			,							atol=1e-4   )   )

