

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_				:      Optional[int]           =			logging.get_logger(__name__)

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   SwinConfig(
			    embed_dim=1_92		,					depths=(2, 2, 18, 2)		,					num_heads=(6, 12, 24, 48)		,					window_size=12		,					out_features=["""stage2""", """stage3""", """stage4"""]		,					)

			SCREAMING_SNAKE_CASE__										=   DetaConfig(
			    backbone_config=snake_case__		,					num_queries=9_00		,					encoder_ffn_dim=20_48		,					decoder_ffn_dim=20_48		,					num_feature_levels=5		,					assign_first_stage=snake_case__		,					with_box_refine=snake_case__		,					two_stage=snake_case__		,					)

			# set labels
			SCREAMING_SNAKE_CASE__										=   """huggingface/label-files"""
			if "o365" in model_name:
						SCREAMING_SNAKE_CASE__										=   3_66
						SCREAMING_SNAKE_CASE__										=   """object365-id2label.json"""
			else:
						SCREAMING_SNAKE_CASE__										=   91
						SCREAMING_SNAKE_CASE__										=   """coco-detection-id2label.json"""

			SCREAMING_SNAKE_CASE__										=   num_labels
			SCREAMING_SNAKE_CASE__										=   json.load(open(cached_download(hf_hub_url(snake_case__		,					snake_case__		,					repo_type="""dataset"""     )     )		,					"""r"""     )     )
			SCREAMING_SNAKE_CASE__										=   {int(snake_case__     ): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE__										=   idalabel
			SCREAMING_SNAKE_CASE__										=   {v: k for k, v in idalabel.items()}

			return config

def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")     )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")     )
			# stages
			for i in range(len(config.backbone_config.depths     )     ):
						for j in range(config.backbone_config.depths[i]     ):
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

						if i < 3:
									rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")     )
									rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")     )

			rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")     )
			rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")     )
			rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")     )
			rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")     )
			rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")     )
			rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")     )

			# transformer encoder
			for i in range(config.encoder_layers     ):
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")     )
						rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")     )

			# transformer decoder
			for i in range(config.decoder_layers     ):
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")     )
						rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")     )

			# fmt: on

			return rename_keys

def 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   dct.pop(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   val

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
			for i in range(len(backbone_config.depths     )     ):
						SCREAMING_SNAKE_CASE__										=   num_features[i]
						for j in range(backbone_config.depths[i]     ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									SCREAMING_SNAKE_CASE__										=   state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
									SCREAMING_SNAKE_CASE__										=   state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
									# next, add query, keys and values (in that order) to the state dict
									SCREAMING_SNAKE_CASE__										=   in_proj_weight[:dim, :]
									SCREAMING_SNAKE_CASE__										=   in_proj_bias[: dim]
									SCREAMING_SNAKE_CASE__										=   in_proj_weight[
									    dim : dim * 2, :
									]
									SCREAMING_SNAKE_CASE__										=   in_proj_bias[
									    dim : dim * 2
									]
									SCREAMING_SNAKE_CASE__										=   in_proj_weight[
									    -dim :, :
									]
									SCREAMING_SNAKE_CASE__										=   in_proj_bias[-dim :]
									# fmt: on

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   config.d_model
			for i in range(config.decoder_layers     ):
						# read in weights + bias of input projection layer of self-attention
						SCREAMING_SNAKE_CASE__										=   state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
						SCREAMING_SNAKE_CASE__										=   state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""     )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE__										=   in_proj_weight[:hidden_size, :]
						SCREAMING_SNAKE_CASE__										=   in_proj_bias[:hidden_size]
						SCREAMING_SNAKE_CASE__										=   in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						SCREAMING_SNAKE_CASE__										=   in_proj_bias[hidden_size : hidden_size * 2]
						SCREAMING_SNAKE_CASE__										=   in_proj_weight[-hidden_size:, :]
						SCREAMING_SNAKE_CASE__										=   in_proj_bias[-hidden_size:]

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
			SCREAMING_SNAKE_CASE__										=   Image.open(requests.get(snake_case__		,					stream=snake_case__     ).raw     )

			return im

@torch.no_grad()
def 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   get_deta_config(snake_case__     )

			# load original state dict
			if model_name == "deta-swin-large":
						SCREAMING_SNAKE_CASE__										=   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""		,					filename="""adet_swin_ft.pth"""     )
			elif model_name == "deta-swin-large-o365":
						SCREAMING_SNAKE_CASE__										=   hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""		,					filename="""deta_swin_pt_o365.pth"""     )
			else:
						raise ValueError(f"""Model name {model_name} not supported"""     )

			SCREAMING_SNAKE_CASE__										=   torch.load(snake_case__		,					map_location="""cpu"""     )["""model"""]

			# original state dict
			for name, param in state_dict.items():
						print(snake_case__		,					param.shape     )

			# rename keys
			SCREAMING_SNAKE_CASE__										=   create_rename_keys(snake_case__     )
			for src, dest in rename_keys:
						rename_key(snake_case__		,					snake_case__		,					snake_case__     )
			read_in_swin_q_k_v(snake_case__		,					config.backbone_config     )
			read_in_decoder_q_k_v(snake_case__		,					snake_case__     )

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									SCREAMING_SNAKE_CASE__										=   state_dict.pop(snake_case__     )
									SCREAMING_SNAKE_CASE__										=   val
						if "input_proj" in key:
									SCREAMING_SNAKE_CASE__										=   state_dict.pop(snake_case__     )
									SCREAMING_SNAKE_CASE__										=   val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									SCREAMING_SNAKE_CASE__										=   state_dict.pop(snake_case__     )
									SCREAMING_SNAKE_CASE__										=   val

    # finally, create HuggingFace model and load state dict
			SCREAMING_SNAKE_CASE__										=   DetaForObjectDetection(snake_case__     )
			model.load_state_dict(snake_case__     )
			model.eval()

			SCREAMING_SNAKE_CASE__										=   """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(snake_case__     )

			# load image processor
			SCREAMING_SNAKE_CASE__										=   DetaImageProcessor(format="""coco_detection"""     )

			# verify our conversion on image
			SCREAMING_SNAKE_CASE__										=   prepare_img()
			SCREAMING_SNAKE_CASE__										=   processor(images=snake_case__		,					return_tensors="""pt"""     )
			SCREAMING_SNAKE_CASE__										=   encoding["""pixel_values"""]
			SCREAMING_SNAKE_CASE__										=   model(pixel_values.to(snake_case__     )     )

			# verify logits
			print("""Logits:"""		,					outputs.logits[0, :3, :3]     )
			print("""Boxes:"""		,					outputs.pred_boxes[0, :3, :3]     )
			if model_name == "deta-swin-large":
						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]     )
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]     )
			elif model_name == "deta-swin-large-o365":
						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]     )
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]     )
			assert torch.allclose(outputs.logits[0, :3, :3]		,					expected_logits.to(snake_case__     )		,					atol=1e-4     )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,					expected_boxes.to(snake_case__     )		,					atol=1e-4     )
			print("""Everything ok!"""     )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""     )
						Path(snake_case__     ).mkdir(exist_ok=snake_case__     )
						model.save_pretrained(snake_case__     )
						processor.save_pretrained(snake_case__     )

			# Push to hub
			if push_to_hub:
						print("""Pushing model and processor to hub..."""     )
						model.push_to_hub(f"""jozhang97/{model_name}"""     )
						processor.push_to_hub(f"""jozhang97/{model_name}"""     )


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

						parser.add_argument(
						    "--model_name",
						    type=str,
						    default="deta-swin-large",
						    choices=["deta-swin-large", "deta-swin-large-o365"],
						    help="Name of the 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 or not to push the converted model to the 🤗 hub."
						)
						A_				:      List[Any]           =			parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase						:  Dict							       =				get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")



@require_sentencepiece
@require_tokenizers
class 				_snake_case					(   a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       int				=     GPTSwaTokenizer
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:       Any				=     True
  SCREAMING_SNAKE_CASE			:       Any				=     False

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCAmelCase 						=							GPTSwaTokenizer(_SCREAMING_SNAKE_CASE      , eos_token='<unk>'      , bos_token='<unk>'      , pad_token='<unk>'	)

      tokenizer.save_pretrained(self.tmpdirname	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							'This is a test'
      lowerCAmelCase 						=							'This is a test'
      return input_text, output_text

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							'<s>'
      lowerCAmelCase 						=							1

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							list(self.get_tokenizer().get_vocab().keys()	)

      self.assertEqual(vocab_keys[0]      , '<unk>'	)
      self.assertEqual(vocab_keys[1]      , '<s>'	)
      self.assertEqual(vocab_keys[-1]      , 'j'	)
      self.assertEqual(len(_SCREAMING_SNAKE_CASE	)      , 20_00	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertEqual(self.get_tokenizer().vocab_size      , 20_00	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							GPTSwaTokenizer(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							tokenizer.tokenize('This is a test'	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , ['▁This', '▁is', '▁a', '▁t', 'est']	)

      self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)      , [4_65, 2_87, 2_65, 6_31, 8_42]	)

      lowerCAmelCase 						=							tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
      # fmt: off
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      , )
      # fmt: on

      lowerCAmelCase 						=							tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]      , )

      lowerCAmelCase 						=							tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE	)
      # fmt: off
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']	)
      # fmt: on

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							GPTSwaTokenizer(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							['This is a test', 'I was born in 92000, and this is falsé.']
      lowerCAmelCase 						=							[
          [4_65, 2_87, 2_65, 6_31, 8_42],
          [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
      ]

      # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
      for text, expected_ids in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          self.assertListEqual(tokenizer.encode_fast(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)

      # Test that decode_fast returns the input text
      for text, token_ids in zip(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          self.assertEqual(tokenizer.decode_fast(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[
          '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
          'Hey there, how are you doing this fine day?',
          'This is a text with a trailing spaces followed by a dot     .',
          'Häj sväjs lillebrör! =)',
          'Det är inget fel på Mr. Cool',
      ]

      # fmt: off
      lowerCAmelCase 						=							{'input_ids': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
      # fmt: on
      self.tokenizer_integration_test_util(
          expected_encoding=_SCREAMING_SNAKE_CASE      , model_name='AI-Sweden/gpt-sw3-126m'      , sequences=_SCREAMING_SNAKE_CASE      , )





