




from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


lowerCamelCase_	 =		getLogger(__name__)


lowerCamelCase_	 =		4
lowerCamelCase_	 =		7

lowerCamelCase_	 =		{
    'float16': torch.floataa,
    'float32': torch.floataa,
}
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str						)				->				Optional[Any]:
   if isinstance(__A ,  torch.Tensor						):
      return image
   elif isinstance(__A ,  PIL.Image.Image						):
      _SCREAMING_SNAKE_CASE =   [image]

   if isinstance(image[0] ,  PIL.Image.Image						):
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   image[0].size
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

      _SCREAMING_SNAKE_CASE =   [np.array(i.resize((w, h)						)						)[None, :] for i in image]
      _SCREAMING_SNAKE_CASE =   np.concatenate(__A ,  axis=0						)
      _SCREAMING_SNAKE_CASE =   np.array(__A						).astype(np.floataa						) / 2_55.0
      _SCREAMING_SNAKE_CASE =   image.transpose(0 ,  3 ,  1 ,  2						)
      _SCREAMING_SNAKE_CASE =   2.0 * image - 1.0
      _SCREAMING_SNAKE_CASE =   torch.from_numpy(__A						)
   elif isinstance(image[0] ,  torch.Tensor						):
      _SCREAMING_SNAKE_CASE =   torch.cat(__A ,  dim=0						)

   return image







class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			Union[str, Any]						,   __lowerCamelCase      :			OnnxRuntimeModel						,   __lowerCamelCase      :			OnnxRuntimeModel						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			OnnxRuntimeModel						,   __lowerCamelCase      :			DDPMScheduler						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			int = 3_5_0						,   ):

      """simple docstring"""
      super().__init__(
          vae=__lowerCamelCase						,   text_encoder=__lowerCamelCase						,   tokenizer=__lowerCamelCase						,   unet=__lowerCamelCase						,   low_res_scheduler=__lowerCamelCase						,   scheduler=__lowerCamelCase						,   safety_checker=__lowerCamelCase						,   feature_extractor=__lowerCamelCase						,   watermarker=__lowerCamelCase						,   max_noise_level=__lowerCamelCase						,   )



   def __call__(					self      :			Optional[int]						,   __lowerCamelCase      :			Union[str, List[str]]						,   __lowerCamelCase      :			Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]						,   __lowerCamelCase      :			int = 7_5						,   __lowerCamelCase      :			float = 9.0						,   __lowerCamelCase      :			int = 2_0						,   __lowerCamelCase      :			Optional[Union[str, List[str]]] = None						,   __lowerCamelCase      :			Optional[int] = 1						,   __lowerCamelCase      :			float = 0.0						,   __lowerCamelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None						,   __lowerCamelCase      :			Optional[torch.FloatTensor] = None						,   __lowerCamelCase      :			Optional[np.ndarray] = None						,   __lowerCamelCase      :			Optional[np.ndarray] = None						,   __lowerCamelCase      :			Optional[str] = "pil"						,   __lowerCamelCase      :			bool = True						,   __lowerCamelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,   __lowerCamelCase      :			Optional[int] = 1						,   ):

      """simple docstring"""
      self.check_inputs(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)

      # 2. Define call parameters
      if prompt is not None and isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         _SCREAMING_SNAKE_CASE =   1
      elif prompt is not None and isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         _SCREAMING_SNAKE_CASE =   len(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   prompt_embeds.shape[0]

      _SCREAMING_SNAKE_CASE =   self._execution_device
      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      _SCREAMING_SNAKE_CASE =   guidance_scale > 1.0

      # 3. Encode input prompt
      _SCREAMING_SNAKE_CASE =   self._encode_prompt(
          __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   prompt_embeds=__lowerCamelCase						,   negative_prompt_embeds=__lowerCamelCase						,   )

      _SCREAMING_SNAKE_CASE =   ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

      # 4. Preprocess image
      _SCREAMING_SNAKE_CASE =   preprocess(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   image.cpu()

      # 5. set timesteps
      self.scheduler.set_timesteps(__lowerCamelCase						,   device=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   self.scheduler.timesteps

      # 5. Add noise to image
      _SCREAMING_SNAKE_CASE =   torch.tensor([noise_level]						,   dtype=torch.long						,   device=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   torch.randn(image.shape						,   generator=__lowerCamelCase						,   device=__lowerCamelCase						,   dtype=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   self.low_res_scheduler.add_noise(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   2 if do_classifier_free_guidance else 1
      _SCREAMING_SNAKE_CASE =   np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
      _SCREAMING_SNAKE_CASE =   np.concatenate([noise_level] * image.shape[0]					)

      # 6. Prepare latent variables
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   image.shape[2:]
      _SCREAMING_SNAKE_CASE =   self.prepare_latents(
          batch_size * num_images_per_prompt						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   )

      # 7. Check that sizes of image and latents match
      _SCREAMING_SNAKE_CASE =   image.shape[1]
      if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
         raise ValueError(
             "Incorrect configuration settings! The config of `pipeline.unet` expects"
             F""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
             F""" `num_channels_image`: {num_channels_image} """
             F""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
             " `pipeline.unet` or your `image` input."					)

      # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      _SCREAMING_SNAKE_CASE =   self.prepare_extra_step_kwargs(__lowerCamelCase						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   next(
          (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")						,   "tensor(float)"					)
      _SCREAMING_SNAKE_CASE =   ORT_TO_NP_TYPE[timestep_dtype]

      # 9. Denoising loop
      _SCREAMING_SNAKE_CASE =   len(__lowerCamelCase					) - num_inference_steps * self.scheduler.order
      with self.progress_bar(total=__lowerCamelCase					) as progress_bar:
         for i, t in enumerate(__lowerCamelCase					):
            # expand the latents if we are doing classifier free guidance
            _SCREAMING_SNAKE_CASE =   np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

            # concat latents, mask, masked_image_latents in the channel dimension
            _SCREAMING_SNAKE_CASE =   self.scheduler.scale_model_input(__lowerCamelCase						,   __lowerCamelCase					)
            _SCREAMING_SNAKE_CASE =   np.concatenate([latent_model_input, image]						,   axis=1					)

            # timestep to tensor
            _SCREAMING_SNAKE_CASE =   np.array([t]						,   dtype=__lowerCamelCase					)

            # predict the noise residual
            _SCREAMING_SNAKE_CASE =   self.unet(
                sample=__lowerCamelCase						,   timestep=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   class_labels=noise_level.astype(np.intaa					)						,   )[0]

            # perform guidance
            if do_classifier_free_guidance:
               _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   np.split(__lowerCamelCase						,   2					)
            _SCREAMING_SNAKE_CASE =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            _SCREAMING_SNAKE_CASE =   self.scheduler.step(
                torch.from_numpy(__lowerCamelCase					)						,   __lowerCamelCase						,   __lowerCamelCase						,   **__lowerCamelCase					).prev_sample

            # call the callback, if provided
            if i == len(__lowerCamelCase					) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
               progress_bar.update()
               if callback is not None and i % callback_steps == 0:
                  callback(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)

        # 10. Post-processing
      _SCREAMING_SNAKE_CASE =   self.decode_latents(latents.float()					)

      # 11. Convert to PIL
      if output_type == "pil":
         _SCREAMING_SNAKE_CASE =   self.numpy_to_pil(__lowerCamelCase					)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1 / 0.0_8_3_3_3 * latents
      _SCREAMING_SNAKE_CASE =   self.vae(latent_sample=__lowerCamelCase					)[0]
      _SCREAMING_SNAKE_CASE =   np.clip(image / 2 + 0.5						,   0						,   1					)
      _SCREAMING_SNAKE_CASE =   image.transpose((0, 2, 3, 1)					)
      return image



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Union[str, List[str]]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			Optional[str]						,   __lowerCamelCase      :			Optional[torch.FloatTensor] = None						,   __lowerCamelCase      :			Optional[torch.FloatTensor] = None						,   ):

      """simple docstring"""
      if prompt is not None and isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         _SCREAMING_SNAKE_CASE =   1
      elif prompt is not None and isinstance(__lowerCamelCase						,   __lowerCamelCase					):
         _SCREAMING_SNAKE_CASE =   len(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   prompt_embeds.shape[0]

      if prompt_embeds is None:
         _SCREAMING_SNAKE_CASE =   self.tokenizer(
             __lowerCamelCase						,   padding="max_length"						,   max_length=self.tokenizer.model_max_length						,   truncation=__lowerCamelCase						,   return_tensors="pt"						,   )
         _SCREAMING_SNAKE_CASE =   text_inputs.input_ids
         _SCREAMING_SNAKE_CASE =   self.tokenizer(__lowerCamelCase						,   padding="longest"						,   return_tensors="pt"					).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
             __lowerCamelCase						,   __lowerCamelCase					):
            _SCREAMING_SNAKE_CASE =   self.tokenizer.batch_decode(
                untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)

         # no positional arguments to text_encoder
         _SCREAMING_SNAKE_CASE =   self.text_encoder(
             input_ids=text_input_ids.int().to(__lowerCamelCase					)						,   )
         _SCREAMING_SNAKE_CASE =   prompt_embeds[0]

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   prompt_embeds.shape
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      _SCREAMING_SNAKE_CASE =   prompt_embeds.repeat(1						,   __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   prompt_embeds.reshape(bs_embed * num_images_per_prompt						,   __lowerCamelCase						,   -1					)

      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
         _SCREAMING_SNAKE_CASE =   4_2
         if negative_prompt is None:
            _SCREAMING_SNAKE_CASE =   [""] * batch_size
         elif type(__lowerCamelCase					) is not type(__lowerCamelCase					):
            raise TypeError(
                F"""`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase					)} !="""
                F""" {type(__lowerCamelCase					)}."""					)
         elif isinstance(__lowerCamelCase						,   __lowerCamelCase					):
            _SCREAMING_SNAKE_CASE =   [negative_prompt]
         elif batch_size != len(__lowerCamelCase					):
            raise ValueError(
                F"""`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase					)}, but `prompt`:"""
                F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                " the batch size of `prompt`."					)
         else:
            _SCREAMING_SNAKE_CASE =   negative_prompt

         _SCREAMING_SNAKE_CASE =   text_input_ids.shape[-1]
         _SCREAMING_SNAKE_CASE =   self.tokenizer(
             __lowerCamelCase						,   padding="max_length"						,   max_length=__lowerCamelCase						,   truncation=__lowerCamelCase						,   return_tensors="pt"						,   )

         # if hasattr(uncond_input, "attention_mask"):
         #     attention_mask = uncond_input.attention_mask.to(device)
         # else:
         #     attention_mask = None

         _SCREAMING_SNAKE_CASE =   self.text_encoder(
             input_ids=uncond_input.input_ids.int().to(__lowerCamelCase					)						,   )
         _SCREAMING_SNAKE_CASE =   uncond_embeddings[0]

      if do_classifier_free_guidance:
         _SCREAMING_SNAKE_CASE =   uncond_embeddings.shape[1]
         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         _SCREAMING_SNAKE_CASE =   uncond_embeddings.repeat(1						,   __lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   uncond_embeddings.reshape(batch_size * num_images_per_prompt						,   __lowerCamelCase						,   -1					)

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         _SCREAMING_SNAKE_CASE =   np.concatenate([uncond_embeddings, prompt_embeds]					)

      return prompt_embeds







'''simple docstring'''



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Union[str, Any]=1_3						,   __lowerCamelCase      :			str=7						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			str=True						,   __lowerCamelCase      :			Union[str, Any]=True						,   __lowerCamelCase      :			List[Any]=9_9						,   __lowerCamelCase      :			List[str]=3_2						,   __lowerCamelCase      :			List[Any]=1_8						,   __lowerCamelCase      :			List[Any]=4_3						,   __lowerCamelCase      :			int=5						,   __lowerCamelCase      :			Any=4						,   __lowerCamelCase      :			List[str]=3_7						,   __lowerCamelCase      :			Any="gelu"						,   __lowerCamelCase      :			List[str]=0.1						,   __lowerCamelCase      :			List[Any]=0.1						,   __lowerCamelCase      :			Union[str, Any]=5_1_2						,   __lowerCamelCase      :			int=1_6						,   __lowerCamelCase      :			str=2						,   __lowerCamelCase      :			Optional[int]=0.0_2						,   __lowerCamelCase      :			Dict=3						,   __lowerCamelCase      :			str=4						,   __lowerCamelCase      :			Union[str, Any]=None						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   parent
      _SCREAMING_SNAKE_CASE =   batch_size
      _SCREAMING_SNAKE_CASE =   seq_length
      _SCREAMING_SNAKE_CASE =   is_training
      _SCREAMING_SNAKE_CASE =   use_input_mask
      _SCREAMING_SNAKE_CASE =   use_token_type_ids
      _SCREAMING_SNAKE_CASE =   use_labels
      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   hidden_size
      _SCREAMING_SNAKE_CASE =   input_embedding_size
      _SCREAMING_SNAKE_CASE =   output_embedding_size
      _SCREAMING_SNAKE_CASE =   num_hidden_layers
      _SCREAMING_SNAKE_CASE =   num_attention_heads
      _SCREAMING_SNAKE_CASE =   intermediate_size
      _SCREAMING_SNAKE_CASE =   hidden_act
      _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
      _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE =   max_position_embeddings
      _SCREAMING_SNAKE_CASE =   type_vocab_size
      _SCREAMING_SNAKE_CASE =   type_sequence_label_size
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   num_labels
      _SCREAMING_SNAKE_CASE =   num_choices
      _SCREAMING_SNAKE_CASE =   scope



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_input_mask:
         _SCREAMING_SNAKE_CASE =   random_attention_mask([self.batch_size, self.seq_length]					)

      _SCREAMING_SNAKE_CASE =   None
      if self.use_token_type_ids:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size					)

      _SCREAMING_SNAKE_CASE =   None
      _SCREAMING_SNAKE_CASE =   None
      _SCREAMING_SNAKE_CASE =   None
      if self.use_labels:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size]						,   self.type_sequence_label_size					)
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels					)
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size]						,   self.num_choices					)

      _SCREAMING_SNAKE_CASE =   RemBertConfig(
          vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   input_embedding_size=self.input_embedding_size						,   output_embedding_size=self.output_embedding_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   is_decoder=__lowerCamelCase						,   initializer_range=self.initializer_range						,   )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   self.prepare_config_and_inputs()

      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   vocab_size=2					)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertModel(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   RemBertModel(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			List[str]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForCausalLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForMaskedLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   RemBertForCausalLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()

      # first forward pass
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   use_cache=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _SCREAMING_SNAKE_CASE =   ids_tensor((self.batch_size, 3)						,   config.vocab_size					)
      _SCREAMING_SNAKE_CASE =   ids_tensor((self.batch_size, 3)						,   vocab_size=2					)

      # append to next input_ids and
      _SCREAMING_SNAKE_CASE =   torch.cat([input_ids, next_tokens]						,   dim=-1					)
      _SCREAMING_SNAKE_CASE =   torch.cat([input_mask, next_mask]						,   dim=-1					)

      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   )["hidden_states"][0]
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   past_key_values=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   )["hidden_states"][0]

      # select random slice
      _SCREAMING_SNAKE_CASE =   ids_tensor((1,)						,   output_from_past.shape[-1]					).item()
      _SCREAMING_SNAKE_CASE =   output_from_no_past[:, -3:, random_slice_idx].detach()
      _SCREAMING_SNAKE_CASE =   output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__lowerCamelCase						,   __lowerCamelCase						,   atol=1e-3					)					)



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForQuestionAnswering(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   start_positions=__lowerCamelCase						,   end_positions=__lowerCamelCase						,   )
      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 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   RemBertForSequenceClassification(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   RemBertForTokenClassification(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_choices
      _SCREAMING_SNAKE_CASE =   RemBertForMultipleChoice(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   input_ids.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   token_type_ids.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   input_mask.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase						,   )
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.prepare_config_and_inputs()
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   config_and_inputs
      _SCREAMING_SNAKE_CASE =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_torch
class 	lowercase_					(				A  ,						A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		(
       (
           RemBertModel,
           RemBertForMaskedLM,
           RemBertForCausalLM,
           RemBertForMultipleChoice,
           RemBertForQuestionAnswering,
           RemBertForSequenceClassification,
           RemBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_       						=		(RemBertForCausalLM,) if is_torch_available() else ()
   lowerCamelCase_       						=		(
       {
           '''feature-extraction''': RemBertModel,
           '''fill-mask''': RemBertForMaskedLM,
           '''question-answering''': RemBertForQuestionAnswering,
           '''text-classification''': RemBertForSequenceClassification,
           '''text-generation''': RemBertForCausalLM,
           '''token-classification''': RemBertForTokenClassification,
           '''zero-shot''': RemBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertModelTester(self					)
      _SCREAMING_SNAKE_CASE =   ConfigTester(self						,   config_class=__lowerCamelCase						,   hidden_size=3_7					)



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _SCREAMING_SNAKE_CASE =   type
         self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      # This regression test was failing with PyTorch < 1.3
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   self.model_tester.prepare_config_and_inputs_for_decoder()

      _SCREAMING_SNAKE_CASE =   None

      self.model_tester.create_and_check_model_as_decoder(
          __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   )



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _SCREAMING_SNAKE_CASE =   RemBertModel.from_pretrained(__lowerCamelCase					)
         self.assertIsNotNone(__lowerCamelCase					)







@require_torch
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      # Test exact values at the last hidden layer
      _SCREAMING_SNAKE_CASE =   RemBertModel.from_pretrained("google/rembert"					)
      _SCREAMING_SNAKE_CASE =   torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]					)
      _SCREAMING_SNAKE_CASE =   torch.tensor([[0, 0, 0, 1, 1]]					)
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   1_1_5_2

      _SCREAMING_SNAKE_CASE =   torch.Size((1, 5, hidden_size)					)
      self.assertEqual(output["last_hidden_state"].shape						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.tensor(
          [
              [
                  [0.0_7_5_4, -0.2_0_2_2, 0.1_9_0_4],
                  [-0.3_3_5_4, -0.3_6_9_2, -0.4_7_9_1],
                  [-0.2_3_1_4, -0.6_7_2_9, -0.0_7_4_9],
                  [-0.0_3_9_6, -0.3_1_0_5, -0.4_2_3_4],
                  [-0.1_5_7_1, -0.0_5_2_5, 0.5_3_5_3],
              ]
          ]					)

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]						,   __lowerCamelCase						,   atol=1e-4					)					)

