



import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_snake_case		=  logging.getLogger(__name__)



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a=-1):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    label_idx







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							Dict    =    mode.value
														_lowerCAmelCase     :							Optional[int]    =    os.path.join(__a,			f"{mode}.txt")
														_lowerCAmelCase     :							Optional[int]    =    1
														_lowerCAmelCase     :							str    =    []
														with open(__a,			encoding="utf-8") as f:
																					_lowerCAmelCase     :							Any    =    []
																					_lowerCAmelCase     :							int    =    []
																					for line in f:
																												if line.startswith("-DOCSTART-") or line == "" or line == "\n":
																																			if words:
																																										examples.append(InputExample(guid=f"{mode}-{guid_index}",			words=__a,			labels=__a))
																																										guid_index += 1
																																										_lowerCAmelCase     :							List[Any]    =    []
																																										_lowerCAmelCase     :							Any    =    []
																												else:
																																			_lowerCAmelCase     :							int    =    line.split(" ")
																																			words.append(splits[0])
																																			if len(__a) > 1:
																																										labels.append(splits[self.label_idx].replace("\n",			""))
																																			else:
																																										# Examples could have no label for mode = "test"
																																										labels.append("O")
																					if words:
																												examples.append(InputExample(guid=f"{mode}-{guid_index}",			words=__a,			labels=__a))
														return examples







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    0
														for line in test_input_reader:
																					if line.startswith("-DOCSTART-") or line == "" or line == "\n":
																												writer.write(__a)
																												if not preds_list[example_id]:
																																			example_id += 1
																					elif preds_list[example_id]:
																												_lowerCAmelCase     :							int    =    line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
																												writer.write(__a)
																					else:
																												logger.warning("Maximum sequence length exceeded: No prediction for '%s'.",			line.split()[0])







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if path:
																					with open(__a,			"r") as f:
																												_lowerCAmelCase     :							Dict    =    f.read().splitlines()
																					if "O" not in labels:
																												_lowerCAmelCase     :							List[Any]    =    ["O"] + labels
																					return labels
														else:
																					return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class   UpperCAmelCase_    (       a):







							def __init__(   self):

														'''simple docstring'''

														super().__init__(label_idx=-2)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if path:
																					with open(__a,			"r") as f:
																												_lowerCAmelCase     :							Any    =    f.read().splitlines()
																					if "O" not in labels:
																												_lowerCAmelCase     :							Optional[Any]    =    ["O"] + labels
																					return labels
														else:
																					return [
																					    "O",
																					    "B-ADVP",
																					    "B-INTJ",
																					    "B-LST",
																					    "B-PRT",
																					    "B-NP",
																					    "B-SBAR",
																					    "B-VP",
																					    "B-ADJP",
																					    "B-CONJP",
																					    "B-PP",
																					    "I-ADVP",
																					    "I-INTJ",
																					    "I-LST",
																					    "I-PRT",
																					    "I-NP",
																					    "I-SBAR",
																					    "I-VP",
																					    "I-ADJP",
																					    "I-CONJP",
																					    "I-PP",
																					]



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							int    =    mode.value
														_lowerCAmelCase     :							List[str]    =    os.path.join(__a,			f"{mode}.txt")
														_lowerCAmelCase     :							Dict    =    1
														_lowerCAmelCase     :							Optional[int]    =    []

														with open(__a,			encoding="utf-8") as f:
																					for sentence in parse_incr(__a):
																												_lowerCAmelCase     :							List[str]    =    []
																												_lowerCAmelCase     :							Union[str, Any]    =    []
																												for token in sentence:
																																			words.append(token["form"])
																																			labels.append(token["upos"])
																												assert len(__a) == len(__a)
																												if words:
																																			examples.append(InputExample(guid=f"{mode}-{guid_index}",			words=__a,			labels=__a))
																																			guid_index += 1
														return examples







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    0
														for sentence in parse_incr(__a):
																					_lowerCAmelCase     :							List[Any]    =    preds_list[example_id]
																					_lowerCAmelCase     :							Union[str, Any]    =    ""
																					for token in sentence:
																												out += f"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
																					out += "\n"
																					writer.write(__a)
																					example_id += 1







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														if path:
																					with open(__a,			"r") as f:
																												return f.read().splitlines()
														else:
																					return [
																					    "ADJ",
																					    "ADP",
																					    "ADV",
																					    "AUX",
																					    "CCONJ",
																					    "DET",
																					    "INTJ",
																					    "NOUN",
																					    "NUM",
																					    "PART",
																					    "PRON",
																					    "PROPN",
																					    "PUNCT",
																					    "SCONJ",
																					    "SYM",
																					    "VERB",
																					    "X",
																					]

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   int    					=				[]
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
            f'''stage{idx}.patch_embed.proj.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
            f'''stage{idx}.patch_embed.proj.bias''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
            f'''stage{idx}.patch_embed.norm.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
            f'''stage{idx}.patch_embed.norm.bias''',
        ))
    return embed

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[Any]    					=				[]
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
        ))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias'''))
    return attention_weights

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Tuple    					=				[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token"))
    return token

def 			lowercase_				(			):
    lowercase__ :   List[str]    					=				[]
    head.append(("layernorm.weight", "norm.weight"))
    head.append(("layernorm.bias", "norm.bias"))
    head.append(("classifier.weight", "head.weight"))
    head.append(("classifier.bias", "head.bias"))
    return head

def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Optional[Any]    					=				"imagenet-1k-id2label.json"
    lowercase__ :   List[str]    					=				1000

    lowercase__ :   Dict    					=				"huggingface/label-files"
    lowercase__ :   List[Any]    					=				num_labels
    lowercase__ :   Tuple    					=				json.load(open(cached_download(hf_hub_url(_lowerCamelCase		,	_lowerCamelCase		,	repo_type="dataset"))		,	"r"))
    lowercase__ :   Tuple    					=				{int(_lowerCamelCase): v for k, v in idalabel.items()}

    lowercase__ :   Any    					=				idalabel
    lowercase__ :   List[Any]    					=				{v: k for k, v in idalabel.items()}

    lowercase__ :   Optional[int]    					=				CvtConfig(num_labels=_lowerCamelCase		,	idalabel=_lowerCamelCase		,	labelaid=_lowerCamelCase)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/"		,	1)[-1][4:6] == "13":
        lowercase__ :   Any    					=				[1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/"		,	1)[-1][4:6] == "21":
        lowercase__ :   Tuple    					=				[1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowercase__ :   Union[str, Any]    					=				[2, 2, 20]
        lowercase__ :   Optional[Any]    					=				[3, 12, 16]
        lowercase__ :   Optional[Any]    					=				[192, 768, 1024]

    lowercase__ :   Union[str, Any]    					=				CvtForImageClassification(_lowerCamelCase)
    lowercase__ :   Tuple    					=				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    lowercase__ :   int    					=				image_size
    lowercase__ :   Dict    					=				torch.load(_lowerCamelCase		,	map_location=torch.device("cpu"))

    lowercase__ :   Any    					=				OrderedDict()
    lowercase__ :   int    					=				[]

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            lowercase__ :   Dict    					=				list_of_state_dict + cls_token(_lowerCamelCase)
        lowercase__ :   List[str]    					=				list_of_state_dict + embeddings(_lowerCamelCase)
        for cnt in range(config.depth[idx]):
            lowercase__ :   Any    					=				list_of_state_dict + attention(_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   List[str]    					=				list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_lowerCamelCase)
    for i in range(len(_lowerCamelCase)):
        lowercase__ :   Dict    					=				original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_lowerCamelCase)
    model.save_pretrained(_lowerCamelCase)
    image_processor.save_pretrained(_lowerCamelCase)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      parser.add_argument(
          '''--cvt_model''',
          default='''cvt-w24''',
          type=str,
          help='''Name of the cvt model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--image_size''',
          default=384,
          type=int,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--cvt_file_name''',
          default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
          type=str,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      UpperCamelCase  							=						parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



