



'''simple docstring'''





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)




def        lowerCamelCase  (				_snake_case  :						Optional[Any]       ):





    '''simple docstring'''


    lowercase__		       =						OrderedDict()
    for key, value in state_dict.items():
        if key.startswith("module.encoder"       ):
            lowercase__		       =						key.replace("module.encoder"     ,"glpn.encoder"       )
        if key.startswith("module.decoder"       ):
            lowercase__		       =						key.replace("module.decoder"     ,"decoder.stages"       )
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            lowercase__		       =						key[key.find("patch_embed"       ) + len("patch_embed"       )]
            lowercase__		       =						key.replace(f'''patch_embed{idx}'''     ,f'''patch_embeddings.{int(__snake_case       )-1}'''       )
        if "norm" in key:
            lowercase__		       =						key.replace("norm"     ,"layer_norm"       )
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            lowercase__		       =						key[key.find("glpn.encoder.layer_norm"       ) + len("glpn.encoder.layer_norm"       )]
            lowercase__		       =						key.replace(f'''layer_norm{idx}'''     ,f'''layer_norm.{int(__snake_case       )-1}'''       )
        if "layer_norm1" in key:
            lowercase__		       =						key.replace("layer_norm1"     ,"layer_norm_1"       )
        if "layer_norm2" in key:
            lowercase__		       =						key.replace("layer_norm2"     ,"layer_norm_2"       )
        if "block" in key:
            # replace for example block1 by block.0
            lowercase__		       =						key[key.find("block"       ) + len("block"       )]
            lowercase__		       =						key.replace(f'''block{idx}'''     ,f'''block.{int(__snake_case       )-1}'''       )
        if "attn.q" in key:
            lowercase__		       =						key.replace("attn.q"     ,"attention.self.query"       )
        if "attn.proj" in key:
            lowercase__		       =						key.replace("attn.proj"     ,"attention.output.dense"       )
        if "attn" in key:
            lowercase__		       =						key.replace("attn"     ,"attention.self"       )
        if "fc1" in key:
            lowercase__		       =						key.replace("fc1"     ,"dense1"       )
        if "fc2" in key:
            lowercase__		       =						key.replace("fc2"     ,"dense2"       )
        if "linear_pred" in key:
            lowercase__		       =						key.replace("linear_pred"     ,"classifier"       )
        if "linear_fuse" in key:
            lowercase__		       =						key.replace("linear_fuse.conv"     ,"linear_fuse"       )
            lowercase__		       =						key.replace("linear_fuse.bn"     ,"batch_norm"       )
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            lowercase__		       =						key[key.find("linear_c"       ) + len("linear_c"       )]
            lowercase__		       =						key.replace(f'''linear_c{idx}'''     ,f'''linear_c.{int(__snake_case       )-1}'''       )
        if "bot_conv" in key:
            lowercase__		       =						key.replace("bot_conv"     ,"0.convolution"       )
        if "skip_conv1" in key:
            lowercase__		       =						key.replace("skip_conv1"     ,"1.convolution"       )
        if "skip_conv2" in key:
            lowercase__		       =						key.replace("skip_conv2"     ,"2.convolution"       )
        if "fusion1" in key:
            lowercase__		       =						key.replace("fusion1"     ,"1.fusion"       )
        if "fusion2" in key:
            lowercase__		       =						key.replace("fusion2"     ,"2.fusion"       )
        if "fusion3" in key:
            lowercase__		       =						key.replace("fusion3"     ,"3.fusion"       )
        if "fusion" in key and "conv" in key:
            lowercase__		       =						key.replace("conv"     ,"convolutional_layer"       )
        if key.startswith("module.last_layer_depth"       ):
            lowercase__		       =						key.replace("module.last_layer_depth"     ,"head.head"       )
        lowercase__		       =						value

    return new_state_dict




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





    '''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)
            lowercase__		       =						state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''       )
            lowercase__		       =						state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''       )
            # next, add keys and values (in that order) to the state dict
            lowercase__		       =						kv_weight[
                : config.hidden_sizes[i], :
            ]
            lowercase__		       =						kv_bias[: config.hidden_sizes[i]]
            lowercase__		       =						kv_weight[
                config.hidden_sizes[i] :, :
            ]
            lowercase__		       =						kv_bias[config.hidden_sizes[i] :]




def        lowerCamelCase  (				):





    '''simple docstring'''


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

    return image




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						List[Any]     ,_snake_case  :						List[str]     ,_snake_case  :						Dict=False     ,_snake_case  :						Tuple=None       ):





    '''simple docstring'''


    lowercase__		       =						GLPNConfig(hidden_sizes=[64, 128, 320, 512]     ,decoder_hidden_size=64     ,depths=[3, 8, 27, 3]       )

    # load image processor (only resize + rescale)
    lowercase__		       =						GLPNImageProcessor()

    # prepare image
    lowercase__		       =						prepare_img()
    lowercase__		       =						image_processor(images=__snake_case     ,return_tensors="pt"       ).pixel_values

    logger.info("Converting model..."       )

    # load original state dict
    lowercase__		       =						torch.load(__snake_case     ,map_location=torch.device("cpu"       )       )

    # rename keys
    lowercase__		       =						rename_keys(__snake_case       )

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

    # create HuggingFace model and load state dict
    lowercase__		       =						GLPNForDepthEstimation(__snake_case       )
    model.load_state_dict(__snake_case       )
    model.eval()

    # forward pass
    lowercase__		       =						model(__snake_case       )
    lowercase__		       =						outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            lowercase__		       =						torch.tensor(
                [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]       )
        elif "kitti" in model_name:
            lowercase__		       =						torch.tensor(
                [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]       )
        else:
            raise ValueError(f'''Unknown model name: {model_name}'''       )

        lowercase__		       =						torch.Size([1, 480, 640]       )

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]     ,__snake_case     ,atol=1e-4       )
        print("Looks ok!"       )

    # finally, push to hub if required
    if push_to_hub:
        logger.info("Pushing model and image processor to the hub..."       )
        model.push_to_hub(
            repo_path_or_name=Path(__snake_case     ,__snake_case       )     ,organization="nielsr"     ,commit_message="Add model"     ,use_temp_dir=__snake_case     ,)
        image_processor.push_to_hub(
            repo_path_or_name=Path(__snake_case     ,__snake_case       )     ,organization="nielsr"     ,commit_message="Add image processor"     ,use_temp_dir=__snake_case     ,)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     argparse.ArgumentParser()

 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."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
 )
 parser.add_argument(
     "--model_name",
     default="glpn-kitti",
     type=str,
     help="Name of the model in case you're pushing to the hub.",
 )
 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




'''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




