



'''simple docstring'''





import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def        lowerCamelCase  (				_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						384
    lowercase__		       =						7
    if "tiny" in model_name:
        lowercase__		       =						96
        lowercase__		       =						(2, 2, 6, 2)
        lowercase__		       =						(3, 6, 12, 24)
    elif "small" in model_name:
        lowercase__		       =						96
        lowercase__		       =						(2, 2, 18, 2)
        lowercase__		       =						(3, 6, 12, 24)
    elif "base" in model_name:
        lowercase__		       =						128
        lowercase__		       =						(2, 2, 18, 2)
        lowercase__		       =						(4, 8, 16, 32)
        lowercase__		       =						12
        lowercase__		       =						512
    elif "large" in model_name:
        lowercase__		       =						192
        lowercase__		       =						(2, 2, 18, 2)
        lowercase__		       =						(6, 12, 24, 48)
        lowercase__		       =						12
        lowercase__		       =						768

    # set label information
    lowercase__		       =						150
    lowercase__		       =						"huggingface/label-files"
    lowercase__		       =						"ade20k-id2label.json"
    lowercase__		       =						json.load(open(hf_hub_download(_snake_case     ,_snake_case     ,repo_type="dataset"       )     ,"r"       )       )
    lowercase__		       =						{int(_snake_case       ): v for k, v in idalabel.items()}
    lowercase__		       =						{v: k for k, v in idalabel.items()}

    lowercase__		       =						SwinConfig(
        embed_dim=_snake_case     ,depths=_snake_case     ,num_heads=_snake_case     ,window_size=_snake_case     ,out_features=["stage1", "stage2", "stage3", "stage4"]     ,)
    lowercase__		       =						UperNetConfig(
        backbone_config=_snake_case     ,auxiliary_in_channels=_snake_case     ,num_labels=_snake_case     ,idalabel=_snake_case     ,labelaid=_snake_case     ,)

    return config




def        lowerCamelCase  (				_snake_case  :						List[Any]       ):





    '''simple docstring'''


    lowercase__		       =						[]

    # fmt: off
    # stem
    rename_keys.append(("backbone.patch_embed.projection.weight", "backbone.embeddings.patch_embeddings.projection.weight")       )
    rename_keys.append(("backbone.patch_embed.projection.bias", "backbone.embeddings.patch_embeddings.projection.bias")       )
    rename_keys.append(("backbone.patch_embed.norm.weight", "backbone.embeddings.norm.weight")       )
    rename_keys.append(("backbone.patch_embed.norm.bias", "backbone.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.stages.{i}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')       )

        if i < 3:
            rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')       )
            rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.bias''')       )
        rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''')       )
        rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''')       )

    # decode head
    rename_keys.extend(
        [
            ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
            ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
            ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
            ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
        ]       )
    # fmt: on

    return rename_keys




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						str     ,_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__		       =						dct.pop(_snake_case       )
    lowercase__		       =						val




def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						List[Any]       ):





    '''simple docstring'''


    lowercase__		       =						[int(backbone_config.embed_dim * 2**i       ) for i in range(len(backbone_config.depths       )       )]
    for i in range(len(backbone_config.depths       )       ):
        lowercase__		       =						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)
            lowercase__		       =						state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''       )
            lowercase__		       =						state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''       )
            # next, add query, keys and values (in that order) to the state dict
            lowercase__		       =						in_proj_weight[:dim, :]
            lowercase__		       =						in_proj_bias[: dim]
            lowercase__		       =						in_proj_weight[
                dim : dim * 2, :
            ]
            lowercase__		       =						in_proj_bias[
                dim : dim * 2
            ]
            lowercase__		       =						in_proj_weight[
                -dim :, :
            ]
            lowercase__		       =						in_proj_bias[-dim :]
            # fmt: on




def        lowerCamelCase  (				_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__       ,       lowercase__		       =						x.shape
    lowercase__		       =						x.reshape(_snake_case     ,4     ,in_channel // 4       )
    lowercase__		       =						x[:, [0, 2, 1, 3], :].transpose(1     ,2       ).reshape(_snake_case     ,_snake_case       )
    return x




def        lowerCamelCase  (				_snake_case  :						List[str]       ):





    '''simple docstring'''


    lowercase__       ,       lowercase__		       =						x.shape
    lowercase__		       =						x.reshape(_snake_case     ,in_channel // 4     ,4       )
    lowercase__		       =						x[:, :, [0, 2, 1, 3]].transpose(1     ,2       ).reshape(_snake_case     ,_snake_case       )

    return x




def        lowerCamelCase  (				_snake_case  :						int       ):





    '''simple docstring'''


    lowercase__		       =						x.shape[0]
    lowercase__		       =						x.reshape(4     ,in_channel // 4       )
    lowercase__		       =						x[[0, 2, 1, 3], :].transpose(0     ,1       ).reshape(_snake_case       )
    return x




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


    lowercase__		       =						x.shape[0]
    lowercase__		       =						x.reshape(in_channel // 4     ,4       )
    lowercase__		       =						x[:, [0, 2, 1, 3]].transpose(0     ,1       ).reshape(_snake_case       )
    return x




def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__		       =						{
        "upernet-swin-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth",
        "upernet-swin-small": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth",
        "upernet-swin-base": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth",
        "upernet-swin-large": "https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth",
    }
    lowercase__		       =						model_name_to_url[model_name]
    lowercase__		       =						torch.hub.load_state_dict_from_url(_snake_case     ,map_location="cpu"     ,file_name=_snake_case       )[
        "state_dict"
    ]

    for name, param in state_dict.items():
        print(_snake_case     ,param.shape       )

    lowercase__		       =						get_upernet_config(_snake_case       )
    lowercase__		       =						UperNetForSemanticSegmentation(_snake_case       )
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        lowercase__		       =						state_dict.pop(_snake_case       )
        if "bn" in key:
            lowercase__		       =						key.replace("bn"     ,"batch_norm"       )
        lowercase__		       =						val

    # rename keys
    lowercase__		       =						create_rename_keys(_snake_case       )
    for src, dest in rename_keys:
        rename_key(_snake_case     ,_snake_case     ,_snake_case       )
    read_in_q_k_v(_snake_case     ,config.backbone_config       )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                lowercase__		       =						reverse_correct_unfold_reduction_order(_snake_case       )
            if "norm" in key:
                lowercase__		       =						reverse_correct_unfold_norm_order(_snake_case       )

    model.load_state_dict(_snake_case       )

    # verify on image
    lowercase__		       =						"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
    lowercase__		       =						Image.open(requests.get(_snake_case     ,stream=_snake_case       ).raw       ).convert("RGB"       )

    lowercase__		       =						SegformerImageProcessor()
    lowercase__		       =						processor(_snake_case     ,return_tensors="pt"       ).pixel_values

    with torch.no_grad():
        lowercase__		       =						model(_snake_case       )
        lowercase__		       =						outputs.logits

    print(logits.shape       )
    print("First values of logits:"     ,logits[0, 0, :3, :3]       )
    # assert values
    if model_name == "upernet-swin-tiny":
        lowercase__		       =						torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]       )
    elif model_name == "upernet-swin-small":
        lowercase__		       =						torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]       )
    elif model_name == "upernet-swin-base":
        lowercase__		       =						torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]       )
    elif model_name == "upernet-swin-large":
        lowercase__		       =						torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]       )
    print("Logits:"     ,outputs.logits[0, 0, :3, :3]       )
    assert torch.allclose(outputs.logits[0, 0, :3, :3]     ,_snake_case     ,atol=1e-4       )
    print("Looks ok!"       )

    if pytorch_dump_folder_path is not None:
        print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
        model.save_pretrained(_snake_case       )
        print(f'''Saving processor to {pytorch_dump_folder_path}'''       )
        processor.save_pretrained(_snake_case       )

    if push_to_hub:
        print(f'''Pushing model and processor for {model_name} to hub'''       )
        model.push_to_hub(f'''openmmlab/{model_name}'''       )
        processor.push_to_hub(f'''openmmlab/{model_name}'''       )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="upernet-swin-tiny",
     type=str,
     choices=[f'''upernet-swin-{size}''' for size in ["tiny", "small", "base", "large"]],
     help="Name of the Swin + UperNet model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''





import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[int]						   =					CodeGenTokenizer
       lowerCAmelCase__  :List[Any]						   =					CodeGenTokenizerFast
       lowerCAmelCase__  :str						   =					True
       lowerCAmelCase__  :Tuple						   =					{"add_prefix_space": True}
       lowerCAmelCase__  :Dict						   =					False



       def 							_a      (				self      )  ->							Union[str, Any]:
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowercase__		       =						[
               "l",
               "o",
               "w",
               "e",
               "r",
               "s",
               "t",
               "i",
               "d",
               "n",
               "\u0120",
               "\u0120l",
               "\u0120n",
               "\u0120lo",
               "\u0120low",
               "er",
               "\u0120lowest",
               "\u0120newer",
               "\u0120wider",
               "<unk>",
               "<|endoftext|>",
           ]
           lowercase__		       =						dict(zip(UpperCAmelCase_     ,range(len(UpperCAmelCase_      )      )      )      )
           lowercase__		       =						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
           lowercase__		       =						{"unk_token": "<unk>"}

           lowercase__		       =						os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"]      )
           lowercase__		       =						os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["merges_file"]      )
           with open(self.vocab_file     ,"w"     ,encoding="utf-8"      ) as fp:
               fp.write(json.dumps(UpperCAmelCase_      ) + "\n"      )
           with open(self.merges_file     ,"w"     ,encoding="utf-8"      ) as fp:
               fp.write("\n".join(UpperCAmelCase_      )      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							int:
           kwargs.update(self.special_tokens_map      )
           return CodeGenTokenizer.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							Tuple:
           kwargs.update(self.special_tokens_map      )
           return CodeGenTokenizerFast.from_pretrained(self.tmpdirname     ,**UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						"lower newer"
           lowercase__		       =						"lower newer"
           return input_text, output_text



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						CodeGenTokenizer(self.vocab_file     ,self.merges_file     ,**self.special_tokens_map      )
           lowercase__		       =						"lower newer"
           lowercase__		       =						["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
           lowercase__		       =						tokenizer.tokenize(UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           lowercase__		       =						tokens + [tokenizer.unk_token]
           lowercase__		       =						[14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           if not self.test_rust_tokenizer:
               return

           lowercase__		       =						self.get_tokenizer()
           lowercase__		       =						self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_      )

           lowercase__		       =						"lower newer"

           # Testing tokenization
           lowercase__		       =						tokenizer.tokenize(UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.tokenize(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Testing conversion to ids without special tokens
           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_     ,add_special_tokens=UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Testing conversion to ids with special tokens
           lowercase__		       =						self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						tokenizer.encode(UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_      )
           lowercase__		       =						rust_tokenizer.encode(UpperCAmelCase_      )
           self.assertListEqual(UpperCAmelCase_     ,UpperCAmelCase_      )

           # Testing the unknown token
           lowercase__		       =						tokens + [rust_tokenizer.unk_token]
           lowercase__		       =						[14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )     ,UpperCAmelCase_      )



       def 							_a      (				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Any:
           # It's very difficult to mix/test pretokenization with byte-level
           # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
           pass



       def 							_a      (				self     ,UpperCAmelCase_=15      )  ->							Union[str, Any]:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                   lowercase__		       =						self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )

                   # Simple input
                   lowercase__		       =						"This is a simple input"
                   lowercase__		       =						["This is a simple input 1", "This is a simple input 2"]
                   lowercase__		       =						("This is a simple input", "This is a pair")
                   lowercase__		       =						[
                       ("This is a simple input 1", "This is a simple input 2"),
                       ("This is a simple pair 1", "This is a simple pair 2"),
                   ]

                   # Simple input tests
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Simple input
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Simple input
                   self.assertRaises(
                       UpperCAmelCase_     ,tokenizer_r.batch_encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"     ,)

                   # Pair input
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Pair input
                   self.assertRaises(UpperCAmelCase_     ,tokenizer_r.encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"      )

                   # Pair input
                   self.assertRaises(
                       UpperCAmelCase_     ,tokenizer_r.batch_encode_plus     ,UpperCAmelCase_     ,max_length=UpperCAmelCase_     ,padding="max_length"     ,)



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						CodeGenTokenizer.from_pretrained(self.tmpdirname     ,pad_token="<pad>"      )

           # Simple input
           lowercase__		       =						"This is a simple input"
           lowercase__		       =						["This is a simple input looooooooong", "This is a simple input"]
           lowercase__		       =						("This is a simple input", "This is a pair")
           lowercase__		       =						[
               ("This is a simple input loooooong", "This is a simple input"),
               ("This is a simple pair loooooong", "This is a simple pair"),
           ]

           lowercase__		       =						tokenizer.pad_token_id

           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding="max_length"     ,max_length=30     ,return_tensors="np"      )
           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding=UpperCAmelCase_     ,truncate=UpperCAmelCase_     ,return_tensors="np"      )
           lowercase__		       =						tokenizer(*UpperCAmelCase_     ,padding="max_length"     ,max_length=60     ,return_tensors="np"      )
           lowercase__		       =						tokenizer(UpperCAmelCase_     ,padding=UpperCAmelCase_     ,truncate=UpperCAmelCase_     ,return_tensors="np"      )

           # s
           # test single string max_length padding
           self.assertEqual(out_s["input_ids"].shape[-1]     ,30      )
           self.assertTrue(pad_token_id in out_s["input_ids"]      )
           self.assertTrue(0 in out_s["attention_mask"]      )

           # s2
           # test automatic padding
           self.assertEqual(out_sa["input_ids"].shape[-1]     ,33      )
           # long slice doesn't have padding
           self.assertFalse(pad_token_id in out_sa["input_ids"][0]      )
           self.assertFalse(0 in out_sa["attention_mask"][0]      )
           # short slice does have padding
           self.assertTrue(pad_token_id in out_sa["input_ids"][1]      )
           self.assertTrue(0 in out_sa["attention_mask"][1]      )

           # p
           # test single pair max_length padding
           self.assertEqual(out_p["input_ids"].shape[-1]     ,60      )
           self.assertTrue(pad_token_id in out_p["input_ids"]      )
           self.assertTrue(0 in out_p["attention_mask"]      )

           # p2
           # test automatic padding pair
           self.assertEqual(out_pa["input_ids"].shape[-1]     ,52      )
           # long slice pair doesn't have padding
           self.assertFalse(pad_token_id in out_pa["input_ids"][0]      )
           self.assertFalse(0 in out_pa["attention_mask"][0]      )
           # short slice pair does have padding
           self.assertTrue(pad_token_id in out_pa["input_ids"][1]      )
           self.assertTrue(0 in out_pa["attention_mask"][1]      )



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						"$$$"
           lowercase__		       =						CodeGenTokenizer.from_pretrained(self.tmpdirname     ,bos_token=UpperCAmelCase_     ,add_bos_token=UpperCAmelCase_      )

           lowercase__		       =						"This is a simple input"
           lowercase__		       =						["This is a simple input 1", "This is a simple input 2"]

           lowercase__		       =						tokenizer.bos_token_id

           lowercase__		       =						tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tokenizer(UpperCAmelCase_      )

           self.assertEqual(out_s.input_ids[0]     ,UpperCAmelCase_      )
           self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

           lowercase__		       =						tokenizer.decode(out_s.input_ids      )
           lowercase__		       =						tokenizer.batch_decode(out_sa.input_ids      )

           self.assertEqual(decode_s.split()[0]     ,UpperCAmelCase_      )
           self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )



       @slow
       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"      )

           lowercase__		       =						"\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
           lowercase__		       =						"\nif len_a > len_b:      result = a\nelse:      result = b"

           lowercase__		       =						tokenizer.encode(UpperCAmelCase_      )
           lowercase__		       =						["^#", re.escape("<|endoftext|>"      ), "^'''", "^\"\"\"", "\n\n\n"]
           lowercase__		       =						tokenizer.decode(UpperCAmelCase_     ,truncate_before_pattern=UpperCAmelCase_      )
           self.assertEqual(UpperCAmelCase_     ,UpperCAmelCase_      )







       def 							_a      (				self      )  ->							Any:
           pass




