import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__           =							logging.get_logger(__name__)

A__           =							{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

A__           =							{
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

A__           =							{
    """ctrl""": 256,
}

A__           =							{
    """Pregnancy""": 168_629,
    """Christianity""": 7_675,
    """Explain""": 106_423,
    """Fitness""": 63_440,
    """Saving""": 63_163,
    """Ask""": 27_171,
    """Ass""": 95_985,
    """Joke""": 163_509,
    """Questions""": 45_622,
    """Thoughts""": 49_605,
    """Retail""": 52_342,
    """Feminism""": 164_338,
    """Writing""": 11_992,
    """Atheism""": 192_263,
    """Netflix""": 48_616,
    """Computing""": 39_639,
    """Opinion""": 43_213,
    """Alone""": 44_967,
    """Funny""": 58_917,
    """Gaming""": 40_358,
    """Human""": 4_088,
    """India""": 1_331,
    """Joker""": 77_138,
    """Diet""": 36_206,
    """Legal""": 11_859,
    """Norman""": 4_939,
    """Tip""": 72_689,
    """Weight""": 52_343,
    """Movies""": 46_273,
    """Running""": 23_425,
    """Science""": 2_090,
    """Horror""": 37_793,
    """Confession""": 60_572,
    """Finance""": 12_250,
    """Politics""": 16_360,
    """Scary""": 191_985,
    """Support""": 12_654,
    """Technologies""": 32_516,
    """Teenage""": 66_160,
    """Event""": 32_769,
    """Learned""": 67_460,
    """Notion""": 182_770,
    """Wikipedia""": 37_583,
    """Books""": 6_665,
    """Extract""": 76_050,
    """Confessions""": 102_701,
    """Conspiracy""": 75_932,
    """Links""": 63_674,
    """Narcissus""": 150_425,
    """Relationship""": 54_766,
    """Relationships""": 134_796,
    """Reviews""": 41_671,
    """News""": 4_256,
    """Translation""": 26_820,
    """multilingual""": 128_406,
}





def  _lowerCamelCase					(   a_				: Optional[int]):
			lowerCamelCase						:Tuple						  =     set()
			lowerCamelCase						:Tuple						  =     word[0]
			for char in word[1:]:
						pairs.add((prev_char, char))
						lowerCamelCase						:Any						  =     char

			lowerCamelCase						:Union[str, Any]						  =     set(__A)
			return pairs


class   _lowerCAmelCase		(     __A				):
							_UpperCAmelCase						    =     VOCAB_FILES_NAMES
							_UpperCAmelCase						    =     PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase						    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase						    =     CONTROL_CODES







							def __init__(				self     :    Dict	,					__snake_case     :    Tuple	,					__snake_case     :    int	,					__snake_case     :    Dict="<unk>"	,					**__snake_case     :    Tuple		):
										super().__init__(unk_token=__snake_case	,					**__snake_case		)

										with open(__snake_case	,					encoding='''utf-8'''		) as vocab_handle:
													lowerCamelCase						:List[Any]						  =     json.load(__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     {v: k for k, v in self.encoder.items()}
										with open(__snake_case	,					encoding='''utf-8'''		) as merges_handle:
													lowerCamelCase						:Tuple						  =     merges_handle.read().split('''\n'''		)[1:-1]
										lowerCamelCase						:List[str]						  =     [tuple(merge.split()		) for merge in merges]
										lowerCamelCase						:Tuple						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:Dict						  =     {}







							@property
							def 				snake_case  (				self     :    Optional[Any]		):
										return len(self.encoder		)







							def 				snake_case  (				self     :    Optional[int]		):
										return dict(self.encoder	,					**self.added_tokens_encoder		)







							def 				snake_case  (				self     :    str	,					__snake_case     :    Optional[int]		):

										if token in self.cache:
													return self.cache[token]
										lowerCamelCase						:Dict						  =     tuple(__snake_case		)
										lowerCamelCase						:Any						  =     tuple(list(word[:-1]		) + [word[-1] + '''</w>''']		)
										lowerCamelCase						:Dict						  =     get_pairs(__snake_case		)

										if not pairs:
													return token

										while True:
													lowerCamelCase						:List[Any]						  =     min(__snake_case	,					key=lambda __snake_case		: self.bpe_ranks.get(__snake_case	,					float('''inf'''		)		)		)
													if bigram not in self.bpe_ranks:
																break
													lowerCamelCase						:Tuple						  =     bigram
													lowerCamelCase						:Union[str, Any]						  =     []
													lowerCamelCase						:Dict						  =     0
													while i < len(__snake_case		):
																try:
																			lowerCamelCase						:List[Any]						  =     word.index(__snake_case	,					__snake_case		)
																except ValueError:
																			new_word.extend(word[i:]		)
																			break
																else:
																			new_word.extend(word[i:j]		)
																			lowerCamelCase						:int						  =     j

																if word[i] == first and i < len(__snake_case		) - 1 and word[i + 1] == second:
																			new_word.append(first + second		)
																			i += 2
																else:
																			new_word.append(word[i]		)
																			i += 1
													lowerCamelCase						:int						  =     tuple(__snake_case		)
													lowerCamelCase						:Optional[Any]						  =     new_word
													if len(__snake_case		) == 1:
																break
													else:
																lowerCamelCase						:int						  =     get_pairs(__snake_case		)
										lowerCamelCase						:List[str]						  =     '''@@ '''.join(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     word[:-4]
										lowerCamelCase						:Union[str, Any]						  =     word
										return word







							def 				snake_case  (				self     :    int	,					__snake_case     :    List[str]		):
										lowerCamelCase						:int						  =     []

										lowerCamelCase						:Any						  =     re.findall(R'''\S+\n?'''	,					__snake_case		)

										for token in words:
													split_tokens.extend(list(self.bpe(__snake_case		).split(''' '''		)		)		)
										return split_tokens







							def 				snake_case  (				self     :    int	,					__snake_case     :    str		):
										return self.encoder.get(__snake_case	,					self.encoder.get(self.unk_token		)		)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    List[str]		):
										return self.decoder.get(__snake_case	,					self.unk_token		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    List[str]		):
										lowerCamelCase						:Optional[Any]						  =     ''' '''.join(__snake_case		).replace('''@@ '''	,					''''''		).strip()
										return out_string







							def 				snake_case  (				self     :    List[Any]	,					__snake_case     :    str	,					__snake_case     :    Optional[str] = None		):

										if not os.path.isdir(__snake_case		):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
													return
										lowerCamelCase						:Union[str, Any]						  =     os.path.join(
										    __snake_case	,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:Union[str, Any]						  =     os.path.join(
										    __snake_case	,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

										with open(__snake_case	,					'''w'''	,					encoding='''utf-8'''		) as f:
													f.write(json.dumps(self.encoder	,					indent=2	,					sort_keys=__snake_case	,					ensure_ascii=__snake_case		) + '''\n'''		)

										lowerCamelCase						:Dict						  =     0
										with open(__snake_case	,					'''w'''	,					encoding='''utf-8'''		) as writer:
													writer.write('''#version: 0.2\n'''		)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,					key=lambda __snake_case		: kv[1]		):
																if index != token_index:
																			logger.warning(
																			    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																			    ''' Please check that the tokenizer is not corrupted!'''		)
																			lowerCamelCase						:List[str]						  =     token_index
																writer.write(''' '''.join(__snake_case		) + '''\n'''		)
																index += 1

										return vocab_file, merge_file

							# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
							#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
							#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
							#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
							#     return ''.join(tokens_generated_so_far)




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def  _lowerCamelCase					(   a_				: str						,			a_				: str=False):
			lowerCamelCase						:Optional[int]						  =     []

			# fmt: off
			# stem:
			rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token'''))
			rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings'''))

			rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''))
			rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''))

			# backbone
			rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight'''))
			rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight'''))
			rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias'''))

			for stage_idx in range(len(config.backbone_config.depths)):
						for layer_idx in range(config.backbone_config.depths[stage_idx]):
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight"))
									rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias"))

						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight"))
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight"))
						rename_keys.append((F"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", F"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias"))

			# transformer encoder
			for i in range(config.num_hidden_layers):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight"))
						rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias"))
						rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight"))
						rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias"))
						rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight"))
						rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias"))
						rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight"))
						rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias"))
						rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight"))
						rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias"))

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''layernorm.weight'''),
						        ('''norm.bias''', '''layernorm.bias'''),
						        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
						        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
						    ])

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						lowerCamelCase						:List[Any]						  =     [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''') else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('''norm.weight''', '''vit.layernorm.weight'''),
						        ('''norm.bias''', '''vit.layernorm.bias'''),
						        ('''head.weight''', '''classifier.weight'''),
						        ('''head.bias''', '''classifier.bias'''),
						    ])
			# fmt: on

			return rename_keys





def  _lowerCamelCase					(   a_				: Any						,			a_				: Any						,			a_				: int=False):
			for i in range(config.num_hidden_layers):
						if base_model:
									lowerCamelCase						:Union[str, Any]						  =     ''''''
						else:
									lowerCamelCase						:Optional[int]						  =     '''vit.'''
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCamelCase						:Optional[Any]						  =     state_dict.pop(F"blocks.{i}.attn.qkv.weight")
						lowerCamelCase						:Any						  =     state_dict.pop(F"blocks.{i}.attn.qkv.bias")
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase						:Any						  =     in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase						:Tuple						  =     in_proj_bias[: config.hidden_size]
						lowerCamelCase						:int						  =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase						:Tuple						  =     in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCamelCase						:Optional[Any]						  =     in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase						:List[Any]						  =     in_proj_bias[-config.hidden_size :]





def  _lowerCamelCase					(   a_				: int):
			lowerCamelCase						:Any						  =     ['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(a_						,			a_)





def  _lowerCamelCase					(   a_				: int						,			a_				: Any						,			a_				: Tuple):
			lowerCamelCase						:Optional[Any]						  =     dct.pop(a_)
			lowerCamelCase						:str						  =     val





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase						:Tuple						  =     Image.open(requests.get(a_						,			stream=a_).raw)
			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: Optional[Any]						,			a_				: Optional[Any]=False):
			lowerCamelCase						:Optional[int]						  =     BitConfig(
			    global_padding='''same'''						,			layer_type='''bottleneck'''						,			depths=(3, 4, 9)						,			out_features=['''stage3''']						,			embedding_dynamic_padding=a_						,			)
			lowerCamelCase						:Optional[int]						  =     ViTHybridConfig(backbone_config=a_						,			image_size=3_84						,			num_labels=10_00)
			lowerCamelCase						:List[Any]						  =     False

			# load original model from timm
			lowerCamelCase						:List[str]						  =     timm.create_model(a_						,			pretrained=a_)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowerCamelCase						:List[str]						  =     timm_model.state_dict()
			if base_model:
						remove_classification_head_(a_)
			lowerCamelCase						:Tuple						  =     create_rename_keys(a_						,			a_)
			for src, dest in rename_keys:
						rename_key(a_						,			a_						,			a_)
			read_in_q_k_v(a_						,			a_						,			a_)

			lowerCamelCase						:List[str]						  =     '''huggingface/label-files'''
			lowerCamelCase						:Any						  =     '''imagenet-1k-id2label.json'''
			lowerCamelCase						:List[Any]						  =     json.load(open(hf_hub_download(a_						,			a_						,			repo_type='''dataset''')						,			'''r'''))
			lowerCamelCase						:Optional[Any]						  =     {int(a_): v for k, v in idalabel.items()}
			lowerCamelCase						:Optional[int]						  =     idalabel
			lowerCamelCase						:Union[str, Any]						  =     {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						lowerCamelCase						:Optional[Any]						  =     ViTHybridModel(a_).eval()
			else:
						lowerCamelCase						:Dict						  =     ViTHybridForImageClassification(a_).eval()
			model.load_state_dict(a_)

			# create image processor
			lowerCamelCase						:Dict						  =     create_transform(**resolve_data_config({}						,			model=a_))
			lowerCamelCase						:str						  =     transform.transforms

			lowerCamelCase						:int						  =     {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			lowerCamelCase						:Any						  =     ViTHybridImageProcessor(
			    do_resize=a_						,			size={'''shortest_edge''': timm_transforms[0].size}						,			resample=pillow_resamplings[timm_transforms[0].interpolation.value]						,			do_center_crop=a_						,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}						,			do_normalize=a_						,			image_mean=timm_transforms[-1].mean.tolist()						,			image_std=timm_transforms[-1].std.tolist()						,			)

			lowerCamelCase						:Dict						  =     prepare_img()
			lowerCamelCase						:str						  =     transform(a_).unsqueeze(0)
			lowerCamelCase						:str						  =     processor(a_						,			return_tensors='''pt''').pixel_values

			# verify pixel values
			assert torch.allclose(a_						,			a_)

			# verify logits
			with torch.no_grad():
						lowerCamelCase						:Optional[int]						  =     model(a_)
						lowerCamelCase						:Union[str, Any]						  =     outputs.logits

			print('''Predicted class:'''						,			logits.argmax(-1).item())
			if base_model:
						lowerCamelCase						:Union[str, Any]						  =     timm_model.forward_features(a_)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(a_						,			outputs.pooler_output						,			atol=1e-3)
			else:
						lowerCamelCase						:List[str]						  =     timm_model(a_)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(a_						,			outputs.logits						,			atol=1e-3)
			print('''Looks ok!''')

			if pytorch_dump_folder_path is not None:
						Path(a_).mkdir(exist_ok=a_)
						print(F"Saving model {vit_name} to {pytorch_dump_folder_path}")
						model.save_pretrained(a_)
						print(F"Saving processor to {pytorch_dump_folder_path}")
						processor.save_pretrained(a_)

			if push_to_hub:
						print(F"Pushing model and processor to the hub {vit_name}")
						model.push_to_hub(F"ybelkada/{vit_name}")
						processor.push_to_hub(F"ybelkada/{vit_name}")


if __name__ == "__main__":
			A__           =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--vit_name""",
			    default="""vit_base_r50_s16_384""",
			    type=str,
			    help="""Name of the hybrid ViT timm 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 to upload the model to the HuggingFace hub."""
			)

			A__           =							parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



