


import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase					:       str			    =     logging.get_logger(__name__)



def UpperCamelCase_				(				__a      )	->   Any:
      # initialize config
      if "resnet-50" in model_name:
            a__	:  int     =   ResNetConfig.from_pretrained("microsoft/resnet-50"      )
      elif "resnet-101" in model_name:
            a__	:  Optional[int]     =   ResNetConfig.from_pretrained("microsoft/resnet-101"      )
      else:
            raise ValueError("Model name should include either resnet50 or resnet101"      )

      a__	:  Tuple     =   DetrConfig(use_timm_backbone=__a			,				backbone_config=__a      )

      # set label attributes
      a__	:  Any     =   "panoptic" in model_name
      if is_panoptic:
            a__	:  Dict     =   250
      else:
            a__	:  Union[str, Any]     =   91
            a__	:  str     =   "huggingface/label-files"
            a__	:  Tuple     =   "coco-detection-id2label.json"
            a__	:  List[str]     =   json.load(open(hf_hub_download(__a			,				__a			,				repo_type="dataset"      )			,				"r"      )      )
            a__	:  str     =   {int(__a      ): v for k, v in idalabel.items()}
            a__	:  Union[str, Any]     =   idalabel
            a__	:  Optional[Any]     =   {v: k for k, v in idalabel.items()}

      return config, is_panoptic



def UpperCamelCase_				(				__a      )	->   Dict:
      # here we list all keys to be renamed (original name on the left, our name on the right)
      a__	:  List[Any]     =   []

      # stem
      # fmt: off
      rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")      )
      rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")      )
      rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")      )
      rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")      )
      rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")      )
      # stages
      for stage_idx in range(len(config.backbone_config.depths      )      ):
            for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
                  # shortcut
                  if layer_idx == 0:
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                            )      )
                  # 3 convs
                  for i in range(3      ):
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                            )      )
                        rename_keys.append(
                            (
                                f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                                f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                            )      )
    # fmt: on

      for i in range(config.encoder_layers      ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (
                    f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
                    f'''encoder.layers.{i}.self_attn.out_proj.weight''',
                )      )
            rename_keys.append(
                (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')      )
            rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight''')      )
            rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias''')      )
            rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight''')      )
            rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias''')      )
            rename_keys.append(
                (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')      )
            rename_keys.append(
                (f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias''')      )
            rename_keys.append(
                (f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight''')      )
            rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias''')      )
            # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
            rename_keys.append(
                (
                    f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
                    f'''decoder.layers.{i}.self_attn.out_proj.weight''',
                )      )
            rename_keys.append(
                (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')      )
            rename_keys.append(
                (
                    f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
                    f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
                )      )
            rename_keys.append(
                (
                    f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
                    f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
                )      )
            rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight''')      )
            rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias''')      )
            rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight''')      )
            rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias''')      )
            rename_keys.append(
                (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')      )
            rename_keys.append(
                (f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias''')      )
            rename_keys.append(
                (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')      )
            rename_keys.append(
                (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')      )
            rename_keys.append(
                (f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight''')      )
            rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias''')      )

      # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
      rename_keys.extend(
          [
              ("input_proj.weight", "input_projection.weight"),
              ("input_proj.bias", "input_projection.bias"),
              ("query_embed.weight", "query_position_embeddings.weight"),
              ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
              ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
              ("class_embed.weight", "class_labels_classifier.weight"),
              ("class_embed.bias", "class_labels_classifier.bias"),
              ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
              ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
              ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
              ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
              ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
              ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
          ]      )

      return rename_keys



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   int:
      a__	:  Tuple     =   state_dict.pop(__a      )
      a__	:  Any     =   val



def UpperCamelCase_				(				__a			,				__a=False      )	->   int:
      a__	:  Optional[int]     =   ""
      if is_panoptic:
            a__	:  Optional[Any]     =   "detr."

      # first: transformer encoder
      for i in range(6      ):
            # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
            a__	:  List[Any]     =   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
            a__	:  Optional[int]     =   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''      )
            # next, add query, keys and values (in that order) to the state dict
            a__	:  Tuple     =   in_proj_weight[:256, :]
            a__	:  List[Any]     =   in_proj_bias[:256]
            a__	:  Any     =   in_proj_weight[256:512, :]
            a__	:  Dict     =   in_proj_bias[256:512]
            a__	:  int     =   in_proj_weight[-256:, :]
            a__	:  Dict     =   in_proj_bias[-256:]
      # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
      for i in range(6      ):
            # read in weights + bias of input projection layer of self-attention
            a__	:  Optional[Any]     =   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
            a__	:  List[Any]     =   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''      )
            # next, add query, keys and values (in that order) to the state dict
            a__	:  str     =   in_proj_weight[:256, :]
            a__	:  List[str]     =   in_proj_bias[:256]
            a__	:  Any     =   in_proj_weight[256:512, :]
            a__	:  List[Any]     =   in_proj_bias[256:512]
            a__	:  Dict     =   in_proj_weight[-256:, :]
            a__	:  List[str]     =   in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            a__	:  List[str]     =   state_dict.pop(
                f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''      )
            a__	:  Optional[Any]     =   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''      )
            # next, add query, keys and values (in that order) of cross-attention to the state dict
            a__	:  str     =   in_proj_weight_cross_attn[:256, :]
            a__	:  str     =   in_proj_bias_cross_attn[:256]
            a__	:  Dict     =   in_proj_weight_cross_attn[256:512, :]
            a__	:  Dict     =   in_proj_bias_cross_attn[256:512]
            a__	:  Dict     =   in_proj_weight_cross_attn[-256:, :]
            a__	:  Tuple     =   in_proj_bias_cross_attn[-256:]



def UpperCamelCase_				(				)	->   int:
      a__	:  Any     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      a__	:  Optional[Any]     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )

      return im



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a=None			,				__a=False      )	->   Optional[int]:
      a__,	a__	:  List[str]     =   get_detr_config(__a      )

      # load original model from torch hub
      a__	:  str     =   {
          "detr-resnet-50": "detr_resnet50",
          "detr-resnet-101": "detr_resnet101",
      }
      logger.info(f'''Converting model {model_name}...'''      )
      a__	:  int     =   torch.hub.load("facebookresearch/detr"			,				model_name_to_original_name[model_name]			,				pretrained=__a      ).eval()
      a__	:  Dict     =   detr.state_dict()
      # rename keys
      for src, dest in create_rename_keys(__a      ):
            if is_panoptic:
                  a__	:  Dict     =   "detr." + src
            rename_key(__a			,				__a			,				__a      )
      # query, key and value matrices need special treatment
      read_in_q_k_v(__a			,				is_panoptic=__a      )
      # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
      a__	:  Optional[int]     =   "detr.model." if is_panoptic else "model."
      for key in state_dict.copy().keys():
            if is_panoptic:
                  if (
                      key.startswith("detr"      )
                      and not key.startswith("class_labels_classifier"      )
                      and not key.startswith("bbox_predictor"      )
                  ):
                        a__	:  Any     =   state_dict.pop(__a      )
                        a__	:  List[str]     =   val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        a__	:  Optional[Any]     =   state_dict.pop(__a      )
                        a__	:  int     =   val
                  elif key.startswith("bbox_attention"      ) or key.startswith("mask_head"      ):
                        continue
                  else:
                        a__	:  Dict     =   state_dict.pop(__a      )
                        a__	:  Optional[int]     =   val
            else:
                  if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
                        a__	:  Tuple     =   state_dict.pop(__a      )
                        a__	:  Union[str, Any]     =   val

    # finally, create HuggingFace model and load state dict
      a__	:  Optional[Any]     =   DetrForSegmentation(__a      ) if is_panoptic else DetrForObjectDetection(__a      )
      model.load_state_dict(__a      )
      model.eval()

      # verify our conversion on an image
      a__	:  Optional[int]     =   "coco_panoptic" if is_panoptic else "coco_detection"
      a__	:  Optional[int]     =   DetrImageProcessor(format=__a      )

      a__	:  Union[str, Any]     =   processor(images=prepare_img()			,				return_tensors="pt"      )
      a__	:  List[str]     =   encoding["pixel_values"]

      a__	:  Tuple     =   detr(__a      )
      a__	:  List[str]     =   model(__a      )

      assert torch.allclose(outputs.logits			,				original_outputs["pred_logits"]			,				atol=1e-3      )
      assert torch.allclose(outputs.pred_boxes			,				original_outputs["pred_boxes"]			,				atol=1e-3      )
      if is_panoptic:
            assert torch.allclose(outputs.pred_masks			,				original_outputs["pred_masks"]			,				atol=1e-4      )
      print("Looks ok!"      )

      if pytorch_dump_folder_path is not None:
            # 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      )
            processor.save_pretrained(__a      )

      if push_to_hub:
            # Upload model and image processor to the hub
            logger.info("Uploading PyTorch model and image processor to the hub..."      )
            model.push_to_hub(f'''nielsr/{model_name}'''      )
            processor.push_to_hub(f'''nielsr/{model_name}'''      )


if __name__ == "__main__":
    UpperCamelCase					:       int			    =     argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        default="""detr-resnet-50""",
        type=str,
        choices=["""detr-resnet-50""", """detr-resnet-101"""],
        help="""Name of the DETR model you'd like to convert.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
    UpperCamelCase					:       List[Any]			    =     parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""

import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


a							      =     get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')





@require_sentencepiece
@require_tokenizers
class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      SpeechTaTokenizer
 UpperCAmelCase       :	Tuple     =      False
 UpperCAmelCase       :	Optional[int]     =      True

 def      lowerCAmelCase_				(					self			:			Tuple						):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _A     =  SpeechTaTokenizer(_UpperCAmelCase						)

       _A     =  AddedToken('<mask>'       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						)
       _A     =  mask_token
       tokenizer.add_special_tokens({'mask_token': mask_token}						)
       tokenizer.add_tokens(['<ctc_blank>']						)

       tokenizer.save_pretrained(self.tmpdirname						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple						):
       _A     =  'this is a test'
       _A     =  'this is a test'
       return input_text, output_text

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Dict=20       ,				_UpperCAmelCase			:			str=5						):
       _A  ,       _A     =  self.get_input_output_texts(_UpperCAmelCase						)
       _A     =  tokenizer.encode(_UpperCAmelCase       ,				add_special_tokens=_UpperCAmelCase						)
       _A     =  tokenizer.decode(_UpperCAmelCase       ,				clean_up_tokenization_spaces=_UpperCAmelCase						)
       return text, ids

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  '<pad>'
       _A     =  1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)       ,				_UpperCAmelCase						)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  list(self.get_tokenizer().get_vocab().keys()						)

       self.assertEqual(vocab_keys[0]       ,				'<s>'						)
       self.assertEqual(vocab_keys[1]       ,				'<pad>'						)
       self.assertEqual(vocab_keys[-4]       ,				'œ'						)
       self.assertEqual(vocab_keys[-2]       ,				'<mask>'						)
       self.assertEqual(vocab_keys[-1]       ,				'<ctc_blank>'						)
       self.assertEqual(len(_UpperCAmelCase						)       ,				81						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       self.assertEqual(self.get_tokenizer().vocab_size       ,				79						)

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.get_tokenizers(do_lower_case=_UpperCAmelCase						)
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
                   _A     =  tokenizer.vocab_size
                   _A     =  len(_UpperCAmelCase						)

                   self.assertNotEqual(_UpperCAmelCase       ,				0						)

                   # We usually have added tokens from the start in tests because our vocab fixtures are
                   # smaller than the original vocabs - let's not assert this
                   # self.assertEqual(vocab_size, all_size)

                   _A     =  ['aaaaa bbbbbb', 'cccccccccdddddddd']
                   _A     =  tokenizer.add_tokens(_UpperCAmelCase						)
                   _A     =  tokenizer.vocab_size
                   _A     =  len(_UpperCAmelCase						)

                   self.assertNotEqual(_UpperCAmelCase       ,				0						)
                   self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)
                   self.assertEqual(_UpperCAmelCase       ,				len(_UpperCAmelCase						)						)
                   self.assertEqual(_UpperCAmelCase       ,				all_size + len(_UpperCAmelCase						)						)

                   _A     =  tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'       ,				add_special_tokens=_UpperCAmelCase						)

                   self.assertGreaterEqual(len(_UpperCAmelCase						)       ,				4						)
                   self.assertGreater(tokens[0]       ,				tokenizer.vocab_size - 1						)
                   self.assertGreater(tokens[-3]       ,				tokenizer.vocab_size - 1						)

                   _A     =  {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                   _A     =  tokenizer.add_special_tokens(_UpperCAmelCase						)
                   _A     =  tokenizer.vocab_size
                   _A     =  len(_UpperCAmelCase						)

                   self.assertNotEqual(_UpperCAmelCase       ,				0						)
                   self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)
                   self.assertEqual(_UpperCAmelCase       ,				len(_UpperCAmelCase						)						)
                   self.assertEqual(_UpperCAmelCase       ,				all_size_a + len(_UpperCAmelCase						)						)

                   _A     =  tokenizer.encode(
                       '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'       ,				add_special_tokens=_UpperCAmelCase						)

                   self.assertGreaterEqual(len(_UpperCAmelCase						)       ,				6						)
                   self.assertGreater(tokens[0]       ,				tokenizer.vocab_size - 1						)
                   self.assertGreater(tokens[0]       ,				tokens[1]						)
                   self.assertGreater(tokens[-3]       ,				tokenizer.vocab_size - 1						)
                   self.assertGreater(tokens[-3]       ,				tokens[-4]						)
                   self.assertEqual(tokens[0]       ,				tokenizer.eos_token_id						)
                   self.assertEqual(tokens[-3]       ,				tokenizer.pad_token_id						)

 def      lowerCAmelCase_				(					self			:			str						):
       pass

 def      lowerCAmelCase_				(					self			:			Any						):
       pass

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.get_tokenizer()

       _A     =  tokenizer.tokenize('This is a test'						)
       # fmt: off
       self.assertListEqual(_UpperCAmelCase       ,				[SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't']						)
       # fmt: on

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)       ,				[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]       ,				)

       _A     =  tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']						)

       _A     =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
       # fmt: off
       self.assertListEqual(_UpperCAmelCase       ,				[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]						)
       # fmt: on

       _A     =  tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.']						)



 @slow
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       # Use custom sequence because this tokenizer does not handle numbers.
       _A     =  [
           'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
           'general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
           'Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '
           'models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.',
           'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
           'conditioning on both left and right context in all layers.',
           'The quick brown fox jumps over the lazy dog.',
       ]

       # fmt: off
       _A     =  {
           'input_ids': [
               [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
               [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
               [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
           ],
           'attention_mask': [
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
           ]
       }
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase       ,				model_name='microsoft/speecht5_asr'       ,				revision='c5ef64c71905caeccde0e4462ef3f9077224c524'       ,				sequences=_UpperCAmelCase       ,				)

