


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A      :    Tuple		=					logging.get_logger(__name__)

def       lowercase_		(		_A			:    int    ,					_A			:    List[str]=False						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  OrderedDict()
      for key, value in state_dict.items():
            if encoder_only and not key.startswith("head"						):
                  lowerCamelCase__		:				Any    =  "segformer.encoder." + key
            if key.startswith("backbone"						):
                  lowerCamelCase__		:				Optional[int]    =  key.replace("backbone"    ,					"segformer.encoder"						)
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  lowerCamelCase__		:				Optional[int]    =  key[key.find("patch_embed"						) + len("patch_embed"						)]
                  lowerCamelCase__		:				Optional[Any]    =  key.replace(F"patch_embed{idx}"    ,					F"patch_embeddings.{int(a_						)-1}"						)
            if "norm" in key:
                  lowerCamelCase__		:				Union[str, Any]    =  key.replace("norm"    ,					"layer_norm"						)
            if "segformer.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  lowerCamelCase__		:				Tuple    =  key[key.find("segformer.encoder.layer_norm"						) + len("segformer.encoder.layer_norm"						)]
                  lowerCamelCase__		:				Tuple    =  key.replace(F"layer_norm{idx}"    ,					F"layer_norm.{int(a_						)-1}"						)
            if "layer_norm1" in key:
                  lowerCamelCase__		:				Dict    =  key.replace("layer_norm1"    ,					"layer_norm_1"						)
            if "layer_norm2" in key:
                  lowerCamelCase__		:				str    =  key.replace("layer_norm2"    ,					"layer_norm_2"						)
            if "block" in key:
                  # replace for example block1 by block.0
                  lowerCamelCase__		:				Any    =  key[key.find("block"						) + len("block"						)]
                  lowerCamelCase__		:				str    =  key.replace(F"block{idx}"    ,					F"block.{int(a_						)-1}"						)
            if "attn.q" in key:
                  lowerCamelCase__		:				Tuple    =  key.replace("attn.q"    ,					"attention.self.query"						)
            if "attn.proj" in key:
                  lowerCamelCase__		:				Any    =  key.replace("attn.proj"    ,					"attention.output.dense"						)
            if "attn" in key:
                  lowerCamelCase__		:				Tuple    =  key.replace("attn"    ,					"attention.self"						)
            if "fc1" in key:
                  lowerCamelCase__		:				Optional[Any]    =  key.replace("fc1"    ,					"dense1"						)
            if "fc2" in key:
                  lowerCamelCase__		:				Tuple    =  key.replace("fc2"    ,					"dense2"						)
            if "linear_pred" in key:
                  lowerCamelCase__		:				Dict    =  key.replace("linear_pred"    ,					"classifier"						)
            if "linear_fuse" in key:
                  lowerCamelCase__		:				List[Any]    =  key.replace("linear_fuse.conv"    ,					"linear_fuse"						)
                  lowerCamelCase__		:				Tuple    =  key.replace("linear_fuse.bn"    ,					"batch_norm"						)
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  lowerCamelCase__		:				Union[str, Any]    =  key[key.find("linear_c"						) + len("linear_c"						)]
                  lowerCamelCase__		:				Union[str, Any]    =  key.replace(F"linear_c{idx}"    ,					F"linear_c.{int(a_						)-1}"						)
            if key.startswith("head"						):
                  lowerCamelCase__		:				Optional[int]    =  key.replace("head"    ,					"classifier"						)
            lowerCamelCase__		:				Any    =  value

      return new_state_dict

def       lowercase_		(		_A			:    List[str]    ,					_A			:    Optional[int]						):

      """simple docstring"""




      for i in range(config.num_encoder_blocks						):
            for j in range(config.depths[i]						):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  lowerCamelCase__		:				int    =  state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"						)
                  lowerCamelCase__		:				Dict    =  state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.bias"						)
                  # next, add keys and values (in that order) to the state dict
                  lowerCamelCase__		:				Dict    =  kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCamelCase__		:				Dict    =  kv_bias[: config.hidden_sizes[i]]
                  lowerCamelCase__		:				List[Any]    =  kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCamelCase__		:				List[Any]    =  kv_bias[
                      config.hidden_sizes[i] :
                  ]

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCamelCase__		:				Tuple    =  Image.open(requests.get(a_    ,					stream=a_						).raw						)

      return image

@torch.no_grad()
def       lowercase_		(		_A			:    Optional[Any]    ,					_A			:    List[Any]    ,					_A			:    List[str]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  SegformerConfig()
      lowerCamelCase__		:				Dict    =  False

      # set attributes based on model_name
      lowerCamelCase__		:				Optional[Any]    =  "huggingface/label-files"
      if "segformer" in model_name:
            lowerCamelCase__		:				Any    =  model_name[len("segformer."						) : len("segformer."						) + 2]
            if "ade" in model_name:
                  lowerCamelCase__		:				Union[str, Any]    =  150
                  lowerCamelCase__		:				Union[str, Any]    =  "ade20k-id2label.json"
                  lowerCamelCase__		:				Union[str, Any]    =  (1, 150, 128, 128)
            elif "city" in model_name:
                  lowerCamelCase__		:				Optional[Any]    =  19
                  lowerCamelCase__		:				Union[str, Any]    =  "cityscapes-id2label.json"
                  lowerCamelCase__		:				str    =  (1, 19, 128, 128)
            else:
                  raise ValueError(F"Model {model_name} not supported"						)
      elif "mit" in model_name:
            lowerCamelCase__		:				Union[str, Any]    =  True
            lowerCamelCase__		:				str    =  model_name[4:6]
            lowerCamelCase__		:				Union[str, Any]    =  1000
            lowerCamelCase__		:				Optional[int]    =  "imagenet-1k-id2label.json"
            lowerCamelCase__		:				int    =  (1, 1000)
      else:
            raise ValueError(F"Model {model_name} not supported"						)

      # set config attributes
      lowerCamelCase__		:				List[str]    =  json.load(open(hf_hub_download(a_    ,					a_    ,					repo_type="dataset"						)    ,					"r"						)						)
      lowerCamelCase__		:				Optional[int]    =  {int(a_						): v for k, v in idalabel.items()}
      lowerCamelCase__		:				List[Any]    =  idalabel
      lowerCamelCase__		:				Dict    =  {v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            lowerCamelCase__		:				int    =  [64, 128, 320, 512]
            lowerCamelCase__		:				Dict    =  256
      elif size == "b2":
            lowerCamelCase__		:				int    =  [64, 128, 320, 512]
            lowerCamelCase__		:				List[str]    =  768
            lowerCamelCase__		:				Optional[Any]    =  [3, 4, 6, 3]
      elif size == "b3":
            lowerCamelCase__		:				Union[str, Any]    =  [64, 128, 320, 512]
            lowerCamelCase__		:				Optional[Any]    =  768
            lowerCamelCase__		:				str    =  [3, 4, 18, 3]
      elif size == "b4":
            lowerCamelCase__		:				List[str]    =  [64, 128, 320, 512]
            lowerCamelCase__		:				Dict    =  768
            lowerCamelCase__		:				Optional[int]    =  [3, 8, 27, 3]
      elif size == "b5":
            lowerCamelCase__		:				Union[str, Any]    =  [64, 128, 320, 512]
            lowerCamelCase__		:				Optional[int]    =  768
            lowerCamelCase__		:				Optional[Any]    =  [3, 6, 40, 3]
      else:
            raise ValueError(F"Size {size} not supported"						)

      # load image processor (only resize + normalize)
      lowerCamelCase__		:				List[str]    =  SegformerImageProcessor(
          image_scale=(512, 512)    ,					keep_ratio=a_    ,					align=a_    ,					do_random_crop=a_						)

      # prepare image
      lowerCamelCase__		:				Any    =  prepare_img()
      lowerCamelCase__		:				Optional[int]    =  image_processor(images=a_    ,					return_tensors="pt"						).pixel_values

      logger.info(F"Converting model {model_name}..."						)

      # load original state dict
      if encoder_only:
            lowerCamelCase__		:				List[str]    =  torch.load(a_    ,					map_location=torch.device("cpu"						)						)
      else:
            lowerCamelCase__		:				Any    =  torch.load(a_    ,					map_location=torch.device("cpu"						)						)["state_dict"]

      # rename keys
      lowerCamelCase__		:				Optional[Any]    =  rename_keys(a_    ,					encoder_only=a_						)
      if not encoder_only:
            del state_dict["decode_head.conv_seg.weight"]
            del state_dict["decode_head.conv_seg.bias"]

      # key and value matrices need special treatment
      read_in_k_v(a_    ,					a_						)

      # create HuggingFace model and load state dict
      if encoder_only:
            lowerCamelCase__		:				Any    =  False
            lowerCamelCase__		:				List[Any]    =  SegformerForImageClassification(a_						)
      else:
            lowerCamelCase__		:				Optional[int]    =  SegformerForSemanticSegmentation(a_						)
      model.load_state_dict(a_						)
      model.eval()

      # forward pass
      lowerCamelCase__		:				List[str]    =  model(a_						)
      lowerCamelCase__		:				Dict    =  outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            lowerCamelCase__		:				str    =  torch.tensor(
                [
                    [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                    [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                    [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
                ]						)
      elif model_name == "segformer.b1.512x512.ade.160k":
            lowerCamelCase__		:				int    =  torch.tensor(
                [
                    [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
                    [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
                    [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
                ]						)
      elif model_name == "segformer.b2.512x512.ade.160k":
            lowerCamelCase__		:				str    =  torch.tensor(
                [
                    [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
                    [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
                    [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
                ]						)
      elif model_name == "segformer.b3.512x512.ade.160k":
            lowerCamelCase__		:				Optional[int]    =  torch.tensor(
                [
                    [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
                    [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
                    [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
                ]						)
      elif model_name == "segformer.b4.512x512.ade.160k":
            lowerCamelCase__		:				Union[str, Any]    =  torch.tensor(
                [
                    [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
                    [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
                    [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
                ]						)
      elif model_name == "segformer.b5.640x640.ade.160k":
            lowerCamelCase__		:				Optional[int]    =  torch.tensor(
                [
                    [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
                    [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
                    [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
                ]						)
      # Cityscapes checkpoints
      elif model_name == "segformer.b0.1024x1024.city.160k":
            lowerCamelCase__		:				str    =  torch.tensor(
                [
                    [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
                    [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
                    [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
                ]						)
      elif model_name == "segformer.b0.512x1024.city.160k":
            lowerCamelCase__		:				Optional[Any]    =  torch.tensor(
                [
                    [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
                    [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
                    [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
                ]						)
      elif model_name == "segformer.b0.640x1280.city.160k":
            lowerCamelCase__		:				Optional[Any]    =  torch.tensor(
                [
                    [
                        [-1.1372E01, -1.2787E01, -1.3477E01],
                        [-1.2536E01, -1.4194E01, -1.4409E01],
                        [-1.3217E01, -1.4888E01, -1.5327E01],
                    ],
                    [
                        [-1.4791E01, -1.7122E01, -1.8277E01],
                        [-1.7163E01, -1.9192E01, -1.9533E01],
                        [-1.7897E01, -1.9991E01, -2.0315E01],
                    ],
                    [
                        [7.6723E-01, 4.1921E-01, -7.7878E-02],
                        [4.7772E-01, 9.5557E-03, -2.8082E-01],
                        [3.6032E-01, -2.4826E-01, -5.1168E-01],
                    ],
                ]						)
      elif model_name == "segformer.b0.768x768.city.160k":
            lowerCamelCase__		:				int    =  torch.tensor(
                [
                    [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
                    [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
                    [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
                ]						)
      elif model_name == "segformer.b1.1024x1024.city.160k":
            lowerCamelCase__		:				List[Any]    =  torch.tensor(
                [
                    [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                    [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                    [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
                ]						)
      elif model_name == "segformer.b2.1024x1024.city.160k":
            lowerCamelCase__		:				Union[str, Any]    =  torch.tensor(
                [
                    [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
                    [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
                    [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
                ]						)
      elif model_name == "segformer.b3.1024x1024.city.160k":
            lowerCamelCase__		:				Optional[int]    =  torch.tensor(
                [
                    [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
                    [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
                    [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
                ]						)
      elif model_name == "segformer.b4.1024x1024.city.160k":
            lowerCamelCase__		:				Optional[Any]    =  torch.tensor(
                [
                    [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
                    [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
                    [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
                ]						)
      elif model_name == "segformer.b5.1024x1024.city.160k":
            lowerCamelCase__		:				Tuple    =  torch.tensor(
                [
                    [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
                    [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
                    [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
                ]						)
      else:
            lowerCamelCase__		:				Optional[Any]    =  logits.argmax(-1						).item()
            print("Predicted class:"    ,					model.config.idalabel[predicted_class_idx]						)

      # verify logits
      if not encoder_only:
            assert logits.shape == expected_shape
            assert torch.allclose(logits[0, :3, :3, :3]    ,					a_    ,					atol=1E-2						)

      # finally, save model and image processor
      logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."						)
      Path(a_						).mkdir(exist_ok=a_						)
      model.save_pretrained(a_						)
      image_processor.save_pretrained(a_						)


if __name__ == "__main__":
    A      :    Any		=					argparse.ArgumentParser()

    parser.add_argument(
        "--model_name",
        default="segformer.b0.512x512.ade.160k",
        type=str,
        help="Name of the model you'd like to convert.",
    )
    parser.add_argument(
        "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    A      :    List[Any]		=					parser.parse_args()
    convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_attention_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

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

								snake_case_				=							RobertaConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_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=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

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

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)






@require_flax
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





