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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
						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    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					int           =							[]
						token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')		)
						return token
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							[]
						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    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					str           =							1_000

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					List[Any]           =							num_labels
						SCREAMING_SNAKE_CASE							:					List[str]           =							json.load(open(cached_download(hf_hub_url(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							{int(_lowercase		): v for k, v in idalabel.items()}

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

						SCREAMING_SNAKE_CASE							:					int           =							CvtConfig(num_labels=_lowercase	,						idalabel=_lowercase	,						labelaid=_lowercase		)

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

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

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												SCREAMING_SNAKE_CASE							:					Any           =							[2, 2, 20]
												SCREAMING_SNAKE_CASE							:					List[str]           =							[3, 12, 16]
												SCREAMING_SNAKE_CASE							:					int           =							[192, 768, 1_024]

						SCREAMING_SNAKE_CASE							:					Any           =							CvtForImageClassification(_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		)
						SCREAMING_SNAKE_CASE							:					Any           =							image_size
						SCREAMING_SNAKE_CASE							:					Any           =							torch.load(_lowercase	,						map_location=torch.device('''cpu'''		)		)

						SCREAMING_SNAKE_CASE							:					str           =							OrderedDict()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]

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

						SCREAMING_SNAKE_CASE							:					Any           =							list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(_lowercase		)
						for i in range(len(_lowercase		)		):
												SCREAMING_SNAKE_CASE							:					Tuple           =							original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(_lowercase		)
						model.save_pretrained(_lowercase		)
						image_processor.save_pretrained(_lowercase		)


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

if __name__ == "__main__":
					__UpperCamelCase	: Union[str, Any]    		=  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	: Tuple    		=  parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							analyze_text(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							list(''' ''' + ascii_lowercase		)
						# what is our total sum of probabilities.
						SCREAMING_SNAKE_CASE							:					Tuple           =							sum(single_char_strings.values()		)

						# one length string
						SCREAMING_SNAKE_CASE							:					Tuple           =							0
						# for each alpha we go in our dict and if it is in it we calculate entropy
						for ch in my_alphas:
												if ch in single_char_strings:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							single_char_strings[ch]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							my_str / all_sum
																		my_fir_sum += prob * math.loga(_lowercase		)  # entropy formula.

    # print entropy
						print(f"""{round(-1 * my_fir_sum		):.1f}"""		)

						# two len string
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sum(two_char_strings.values()		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							0
						# for each alpha (two in size) calculate entropy.
						for cha in my_alphas:
												for cha in my_alphas:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							cha + cha
																		if sequence in two_char_strings:
																								SCREAMING_SNAKE_CASE							:					Any           =							two_char_strings[sequence]
																								SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		) / all_sum
																								my_sec_sum += prob * math.loga(_lowercase		)

    # print second entropy
						print(f"""{round(-1 * my_sec_sum		):.1f}"""		)

						# print the difference between them
						print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)		):.1f}"""		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							Counter()  # type: ignore
						SCREAMING_SNAKE_CASE							:					Any           =							Counter()  # type: ignore
						single_char_strings[text[-1]] += 1

						# first case when we have space at start.
						two_char_strings[" " + text[0]] += 1
						for i in range(0	,						len(_lowercase		) - 1		):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings
def    A			(					):
						import doctest

						doctest.testmod()
						# text = (
						#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
						#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
						#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
						#     "beloved evident through no service elderly is. Blind there if every no so "
						#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
						#     "of message cottage windows do besides against uncivil.  Delightful "
						#     "unreserved impossible few estimating men favourable see entreaties. She "
						#     "propriety immediate was improving. He or entrance humoured likewise "
						#     "moderate. Much nor game son say feel. Fat make met can must form into "
						#     "gate. Me we offending prevailed discovery. "
						# )

						# calculate_prob(text)


if __name__ == "__main__":
					main()





