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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=0.999  ,      SCREAMING_SNAKE_CASE="cosine"  ,      ):
				'''simple docstring'''







				if alpha_transform_type == "cosine":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE     ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE     ):
												return math.exp(t * -12.0     )

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

				__UpperCamelCase					:Tuple        =		[]
				for i in range(SCREAMING_SNAKE_CASE     ):
								__UpperCamelCase					:Optional[Any]        =		i / num_diffusion_timesteps
								__UpperCamelCase					:Optional[int]        =		(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE     ) / alpha_bar_fn(SCREAMING_SNAKE_CASE     )  ,      SCREAMING_SNAKE_CASE     )     )
				return torch.tensor(SCREAMING_SNAKE_CASE  ,      dtype=torch.floataa     )


class      lowerCamelCase_					(       UpperCAmelCase_						,     UpperCAmelCase_			):






				'''simple docstring'''






				a__     :						Optional[Any]      =				[e.name for e in KarrasDiffusionSchedulers]
				a__     :						str      =				2







				@register_to_config
				def __init__(     self			,			__lowercase = 1_000			,			__lowercase = 0.0_00_85			,			__lowercase = 0.0_12			,			__lowercase = "linear"			,			__lowercase = None			,			__lowercase = "epsilon"			,			__lowercase = False			,			__lowercase = False			,			__lowercase = 1.0			,			__lowercase = "linspace"			,			__lowercase = 0			,			)	->     str:
								if trained_betas is not None:
												__UpperCamelCase					:str        =		torch.tensor(__lowercase			,			dtype=torch.floataa)
								elif beta_schedule == "linear":
												__UpperCamelCase					:Any        =		torch.linspace(__lowercase			,			__lowercase			,			__lowercase			,			dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												__UpperCamelCase					:Optional[int]        =		(
												    torch.linspace(beta_start**0.5			,			beta_end**0.5			,			__lowercase			,			dtype=torch.floataa) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												__UpperCamelCase					:List[str]        =		betas_for_alpha_bar(__lowercase			,			alpha_transform_type='''cosine''')
								elif beta_schedule == "exp":
												__UpperCamelCase					:str        =		betas_for_alpha_bar(__lowercase			,			alpha_transform_type='''exp''')
								else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

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

								#  set all values
								self.set_timesteps(__lowercase			,			__lowercase			,			__lowercase)
								__UpperCamelCase					:Dict        =		use_karras_sigmas







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=None)	->     Optional[int]:
								if schedule_timesteps is None:
												__UpperCamelCase					:Any        =		self.timesteps

								__UpperCamelCase					:Optional[int]        =		(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:
												__UpperCamelCase					:Optional[int]        =		1 if len(__lowercase) > 1 else 0
								else:
												__UpperCamelCase					:Union[str, Any]        =		timestep.cpu().item() if torch.is_tensor(__lowercase) else timestep
												__UpperCamelCase					:Tuple        =		self._index_counter[timestep_int]

								return indices[pos].item()







				@property
				def       UpperCamelCase__       (     self)	->     str:
								# 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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			)	->     torch.FloatTensor:
								__UpperCamelCase					:Optional[int]        =		self.index_for_timestep(__lowercase)

								__UpperCamelCase					:Optional[Any]        =		self.sigmas[step_index]
								__UpperCamelCase					:Dict        =		sample / ((sigma**2 + 1) ** 0.5)
								return sample







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = None			,			)	->     Union[str, Any]:
								__UpperCamelCase					:List[str]        =		num_inference_steps

								__UpperCamelCase					: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":
												__UpperCamelCase					:Tuple        =		np.linspace(0			,			num_train_timesteps - 1			,			__lowercase			,			dtype=__lowercase)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
												__UpperCamelCase					:Optional[int]        =		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					:Optional[int]        =		(np.arange(0			,			__lowercase) * step_ratio).round()[::-1].copy().astype(__lowercase)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												__UpperCamelCase					:Tuple        =		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					:Optional[int]        =		(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'.""")

								__UpperCamelCase					:Any        =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								__UpperCamelCase					:Union[str, Any]        =		np.log(__lowercase)
								__UpperCamelCase					:List[Any]        =		np.interp(__lowercase			,			np.arange(0			,			len(__lowercase))			,			__lowercase)

								if self.config.use_karras_sigmas:
												__UpperCamelCase					:Optional[int]        =		self._convert_to_karras(in_sigmas=__lowercase			,			num_inference_steps=self.num_inference_steps)
												__UpperCamelCase					:Any        =		np.array([self._sigma_to_t(__lowercase			,			__lowercase) for sigma in sigmas])

								__UpperCamelCase					:List[Any]        =		np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								__UpperCamelCase					:List[str]        =		torch.from_numpy(__lowercase).to(device=__lowercase)
								__UpperCamelCase					:List[str]        =		torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])

								__UpperCamelCase					:Optional[Any]        =		torch.from_numpy(__lowercase)
								__UpperCamelCase					:Dict        =		torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])

								if str(__lowercase).startswith('''mps'''):
												# mps does not support float64
												__UpperCamelCase					:Dict        =		timesteps.to(__lowercase			,			dtype=torch.floataa)
								else:
												__UpperCamelCase					:Any        =		timesteps.to(device=__lowercase)

								# empty dt and derivative
								__UpperCamelCase					:List[Any]        =		None
								__UpperCamelCase					:List[str]        =		None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								__UpperCamelCase					:List[Any]        =		defaultdict(__lowercase)







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Union[str, Any]:
								# get log sigma
								__UpperCamelCase					:Optional[Any]        =		np.log(__lowercase)

								# get distribution
								__UpperCamelCase					:Optional[int]        =		log_sigma - log_sigmas[:, np.newaxis]

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

								__UpperCamelCase					:Union[str, Any]        =		log_sigmas[low_idx]
								__UpperCamelCase					:Optional[int]        =		log_sigmas[high_idx]

								# interpolate sigmas
								__UpperCamelCase					:Optional[Any]        =		(low - log_sigma) / (low - high)
								__UpperCamelCase					:Any        =		np.clip(__lowercase			,			0			,			1)

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







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     torch.FloatTensor:
								__UpperCamelCase					:float        =		in_sigmas[-1].item()
								__UpperCamelCase					:float        =		in_sigmas[0].item()

								__UpperCamelCase					:int        =		7.0  # 7.0 is the value used in the paper
								__UpperCamelCase					:str        =		np.linspace(0			,			1			,			__lowercase)
								__UpperCamelCase					:List[Any]        =		sigma_min ** (1 / rho)
								__UpperCamelCase					:List[str]        =		sigma_max ** (1 / rho)
								__UpperCamelCase					:List[str]        =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas







				@property
				def       UpperCamelCase__       (     self)	->     List[str]:
								return self.dt is None







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = True			,			)	->     Union[SchedulerOutput, Tuple]:
								__UpperCamelCase					:Any        =		self.index_for_timestep(__lowercase)

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

								if self.state_in_first_order:
												__UpperCamelCase					:Tuple        =		self.sigmas[step_index]
												__UpperCamelCase					:List[str]        =		self.sigmas[step_index + 1]
								else:
												# 2nd order / Heun's method
												__UpperCamelCase					:int        =		self.sigmas[step_index - 1]
												__UpperCamelCase					:Tuple        =		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
								__UpperCamelCase					:List[str]        =		0
								__UpperCamelCase					:Any        =		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":
												__UpperCamelCase					:List[str]        =		sigma_hat if self.state_in_first_order else sigma_next
												__UpperCamelCase					:str        =		sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
												__UpperCamelCase					:Optional[Any]        =		sigma_hat if self.state_in_first_order else sigma_next
												__UpperCamelCase					:Any        =		model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
												    sample / (sigma_input**2 + 1)
												)
								elif self.config.prediction_type == "sample":
												__UpperCamelCase					:Optional[int]        =		model_output
								else:
												raise ValueError(
												    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

								if self.config.clip_sample:
												__UpperCamelCase					:int        =		pred_original_sample.clamp(
												    -self.config.clip_sample_range			,			self.config.clip_sample_range)

								if self.state_in_first_order:
												# 2. Convert to an ODE derivative for 1st order
												__UpperCamelCase					:Dict        =		(sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												__UpperCamelCase					:Optional[int]        =		sigma_next - sigma_hat

												# store for 2nd order step
												__UpperCamelCase					:Dict        =		derivative
												__UpperCamelCase					:Tuple        =		dt
												__UpperCamelCase					:List[Any]        =		sample
								else:
												# 2. 2nd order / Heun's method
												__UpperCamelCase					:int        =		(sample - pred_original_sample) / sigma_next
												__UpperCamelCase					:Dict        =		(self.prev_derivative + derivative) / 2

												# 3. take prev timestep & sample
												__UpperCamelCase					:str        =		self.dt
												__UpperCamelCase					:List[Any]        =		self.sample

												# free dt and derivative
												# Note, this puts the scheduler in "first order mode"
												__UpperCamelCase					:Any        =		None
												__UpperCamelCase					:Dict        =		None
												__UpperCamelCase					:Tuple        =		None

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

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=__lowercase)







				def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								__UpperCamelCase					:Any        =		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
												__UpperCamelCase					:Tuple        =		self.timesteps.to(original_samples.device			,			dtype=torch.floataa)
												__UpperCamelCase					:Optional[int]        =		timesteps.to(original_samples.device			,			dtype=torch.floataa)
								else:
												__UpperCamelCase					:Dict        =		self.timesteps.to(original_samples.device)
												__UpperCamelCase					:Union[str, Any]        =		timesteps.to(original_samples.device)

								__UpperCamelCase					:int        =		[self.index_for_timestep(__lowercase			,			__lowercase) for t in timesteps]

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

								__UpperCamelCase					:Tuple        =		original_samples + noise * sigma
								return noisy_samples







				def __len__(     self)	->     List[str]:
								return self.config.num_train_timesteps






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      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			)	->     Optional[Any]:
        __UpperCamelCase					:Optional[int]        =		parent
        __UpperCamelCase					:Optional[int]        =		13
        __UpperCamelCase					:Dict        =		7
        __UpperCamelCase					:Optional[int]        =		True
        __UpperCamelCase					:List[str]        =		True
        __UpperCamelCase					:List[Any]        =		True
        __UpperCamelCase					:Union[str, Any]        =		True
        __UpperCamelCase					:Any        =		True
        __UpperCamelCase					:Optional[int]        =		False
        __UpperCamelCase					:Any        =		False
        __UpperCamelCase					:str        =		False
        __UpperCamelCase					:Optional[Any]        =		2
        __UpperCamelCase					:Optional[int]        =		99
        __UpperCamelCase					:Any        =		0
        __UpperCamelCase					:List[Any]        =		32
        __UpperCamelCase					:int        =		2
        __UpperCamelCase					:Optional[Any]        =		4
        __UpperCamelCase					:Dict        =		0.1
        __UpperCamelCase					:Optional[Any]        =		0.1
        __UpperCamelCase					:str        =		512
        __UpperCamelCase					:Any        =		16
        __UpperCamelCase					:str        =		2
        __UpperCamelCase					:Dict        =		0.02
        __UpperCamelCase					:List[Any]        =		3
        __UpperCamelCase					:Optional[int]        =		4
        __UpperCamelCase					:Tuple        =		'''last'''
        __UpperCamelCase					:Any        =		True
        __UpperCamelCase					:Union[str, Any]        =		None
        __UpperCamelCase					:Tuple        =		0







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[str]        =		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size)
        __UpperCamelCase					:Any        =		random_attention_mask([self.batch_size, self.seq_length]			,			dtype=tf.floataa)

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

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

        __UpperCamelCase					:Dict        =		None
        __UpperCamelCase					:List[str]        =		None
        __UpperCamelCase					:Union[str, Any]        =		None
        if self.use_labels:
            __UpperCamelCase					:Dict        =		ids_tensor([self.batch_size]			,			self.type_sequence_label_size)
            __UpperCamelCase					:int        =		ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels)
            __UpperCamelCase					:List[str]        =		ids_tensor([self.batch_size]			,			2			,			dtype=tf.floataa)
            __UpperCamelCase					:Optional[Any]        =		ids_tensor([self.batch_size]			,			self.num_choices)

        __UpperCamelCase					:Optional[int]        =		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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Optional[Any]:
        __UpperCamelCase					:str        =		TFFlaubertModel(config=__lowercase)
        __UpperCamelCase					:List[str]        =		{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
        __UpperCamelCase					:Optional[int]        =		model(__lowercase)

        __UpperCamelCase					:Optional[int]        =		[input_ids, input_mask]
        __UpperCamelCase					:Union[str, Any]        =		model(__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     int:
        __UpperCamelCase					:str        =		TFFlaubertWithLMHeadModel(__lowercase)

        __UpperCamelCase					:List[str]        =		{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
        __UpperCamelCase					:Optional[int]        =		model(__lowercase)

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Any:
        __UpperCamelCase					:Any        =		TFFlaubertForQuestionAnsweringSimple(__lowercase)

        __UpperCamelCase					:str        =		{'''input_ids''': input_ids, '''lengths''': input_lengths}

        __UpperCamelCase					:int        =		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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Any:
        __UpperCamelCase					:int        =		TFFlaubertForSequenceClassification(__lowercase)

        __UpperCamelCase					:List[Any]        =		{'''input_ids''': input_ids, '''lengths''': input_lengths}

        __UpperCamelCase					:Any        =		model(__lowercase)

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:Optional[int]        =		self.num_labels
        __UpperCamelCase					:int        =		TFFlaubertForTokenClassification(config=__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
        __UpperCamelCase					:Optional[Any]        =		model(__lowercase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     List[Any]:
        __UpperCamelCase					:Any        =		self.num_choices
        __UpperCamelCase					:Any        =		TFFlaubertForMultipleChoice(config=__lowercase)
        __UpperCamelCase					:List[str]        =		tf.tile(tf.expand_dims(__lowercase			,			1)			,			(1, self.num_choices, 1))
        __UpperCamelCase					:Optional[Any]        =		tf.tile(tf.expand_dims(__lowercase			,			1)			,			(1, self.num_choices, 1))
        __UpperCamelCase					:Tuple        =		tf.tile(tf.expand_dims(__lowercase			,			1)			,			(1, self.num_choices, 1))
        __UpperCamelCase					:Any        =		{
            '''input_ids''': multiple_choice_inputs_ids,
            '''attention_mask''': multiple_choice_input_mask,
            '''token_type_ids''': multiple_choice_token_type_ids,
        }
        __UpperCamelCase					:List[Any]        =		model(__lowercase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_choices))







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Optional[int]        =		self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	
        )					:Any        =		config_and_inputs
        __UpperCamelCase					:int        =		{
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''langs''': token_type_ids,
            '''lengths''': input_lengths,
        }
        return config, inputs_dict


@require_tf
class      lowerCamelCase_					(       UpperCAmelCase_						,     UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						List[Any]      =				(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    a__     :						Tuple      =				(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    a__     :						Tuple      =				(
        {
            """feature-extraction""": TFFlaubertModel,
            """fill-mask""": TFFlaubertWithLMHeadModel,
            """question-answering""": TFFlaubertForQuestionAnsweringSimple,
            """text-classification""": TFFlaubertForSequenceClassification,
            """token-classification""": TFFlaubertForTokenClassification,
            """zero-shot""": TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    a__     :						Tuple      =				False
    a__     :						Any      =				False







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     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       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:int        =		TFFlaubertModelTester(self)
        __UpperCamelCase					:Dict        =		ConfigTester(self			,			config_class=__lowercase			,			emb_dim=37)







    def       UpperCamelCase__       (     self)	->     str:
        self.config_tester.run_common_tests()







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:int        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Optional[int]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Dict        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_token_classification(*__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_multiple_choice(*__lowercase)







    @slow
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase					:Any        =		TFFlaubertModel.from_pretrained(__lowercase)
            self.assertIsNotNone(__lowercase)


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






    '''simple docstring'''






    @slow
    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[Any]        =		TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')

        __UpperCamelCase					:Tuple        =		tf.convert_to_tensor(
            [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]			,			dtype=tf.intaa			,			)  # "J'aime flaubert !"

        __UpperCamelCase					:Union[str, Any]        =		model(__lowercase)[0]
        __UpperCamelCase					:str        =		tf.TensorShape((1, 8, 512))
        self.assertEqual(output.shape			,			__lowercase)
        # compare the actual values for a slice.
        __UpperCamelCase					:Optional[int]        =		tf.convert_to_tensor(
            [
                [
                    [-1.8_76_87_73, -1.56_65_55, 0.27_07_24_18],
                    [-1.6_92_00_38, -0.5_87_35_05, 1.9_32_95_99],
                    [-2.9_56_39_85, -1.6_99_38_35, 1.7_97_20_52],
                ]
            ]			,			dtype=tf.floataa			,			)

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





