

import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def        _lowercase  (					a_					:       Tuple       ,a_					:       Union[str, Any]=False       ,a_					:       str=False       ,a_					:       Optional[int]="data2vec."					)					->						Any:




					'''simple docstring'''







					__magic_name__				=     'backbone.' if is_semantic else ''

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

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F'{prefix}cls_token', F'{hf_prefix}embeddings.cls_token'),
					        (F'{prefix}patch_embed.proj.weight', F'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
					        (F'{prefix}patch_embed.proj.bias', F'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
					    ]					)

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ('mask_token', F'{hf_prefix}embeddings.mask_token'),
										        (
										            'rel_pos_bias.relative_position_bias_table',
										            F'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
										        ),
										        (
										            'rel_pos_bias.relative_position_index',
										            F'{hf_prefix}encoder.relative_position_bias.relative_position_index',
										        ),
										        ('norm.weight', 'layernorm.weight'),
										        ('norm.bias', 'layernorm.bias'),
										    ]					)
					elif is_semantic:
										# semantic segmentation classification heads
										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'),
										    ]					)
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('fc_norm.weight', F'{hf_prefix}pooler.layernorm.weight'),
										        ('fc_norm.bias', F'{hf_prefix}pooler.layernorm.bias'),
										        ('head.weight', 'classifier.weight'),
										        ('head.bias', 'classifier.bias'),
										    ]					)

					return rename_keys




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       Dict       ,a_					:       int=False       ,a_					:       Dict=False       ,a_					:       int="data2vec_vision."					)					->						Union[str, Any]:




					'''simple docstring'''







					for i in range(config.num_hidden_layers					):
										__magic_name__				=     'backbone.' if is_semantic else ''
										# queries, keys and values
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'					)
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'					)
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'					)

										__magic_name__				=     in_proj_weight[
										    : config.hidden_size, :
										]
										__magic_name__				=     q_bias
										__magic_name__				=     in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__magic_name__				=     in_proj_weight[
										    -config.hidden_size :, :
										]
										__magic_name__				=     v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.gamma_1'					)
										__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.gamma_2'					)

										__magic_name__				=     gamma_a
										__magic_name__				=     gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'					)
															__magic_name__				=     state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'					)

															__magic_name__				=     table
															__magic_name__				=     index




def        _lowercase  (					)					->						Dict:




					'''simple docstring'''







					__magic_name__				=     argparse.ArgumentParser(
					    'Convert Data2VecVision to HF for image classification and pretraining'       ,add_help=a_					)
					parser.add_argument('--hf_checkpoint_name'       ,type=a_					)
					parser.add_argument('--input_size'       ,default=2_2_4       ,type=a_       ,help='images input size'					)
					parser.add_argument('--beit_checkpoint'       ,default=''       ,help='beit checkpoint'					)

					return parser.parse_args()




def        _lowercase  (					a_					:       Tuple       ,a_					:       Dict       ,a_					:       Any					)					->						List[str]:




					'''simple docstring'''







					def load_state_dict(a_					:       Tuple       ,a_					:       List[str]       ,a_					:       Optional[Any]=""       ,a_					:       Any="relative_position_index"					):
										__magic_name__				=     []
										__magic_name__				=     []
										__magic_name__				=     []
										# copy state_dict so _load_from_state_dict can modify it
										__magic_name__				=     getattr(a_       ,'_metadata'       ,a_					)
										__magic_name__				=     state_dict.copy()
										if metadata is not None:
															__magic_name__				=     metadata

										def load(a_					:       Dict       ,a_					:       List[Any]=""					):
															__magic_name__				=     {} if metadata is None else metadata.get(prefix[:-1]       ,{}					)
															module._load_from_state_dict(
															    a_       ,a_       ,a_       ,a_       ,a_       ,a_       ,a_					)
															for name, child in module._modules.items():
																				if child is not None:
																									load(a_       ,prefix + name + '.'					)

										load(a_       ,prefix=a_					)

										__magic_name__				=     []
										__magic_name__				=     []
										for key in missing_keys:
															__magic_name__				=     True
															for ignore_key in ignore_missing.split('|'					):
																				if ignore_key in key:
																									__magic_name__				=     False
																									break
															if keep_flag:
																				warn_missing_keys.append(a_					)
															else:
																				ignore_missing_keys.append(a_					)

										__magic_name__				=     warn_missing_keys

										if len(a_					) > 0:
															print(
															    'Weights of {} not initialized from pretrained model: {}'.format(
															        model.__class__.__name__       ,a_					)					)
										if len(a_					) > 0:
															print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__       ,a_					)					)
										if len(a_					) > 0:
															print(
															    'Ignored weights of {} not initialized from pretrained model: {}'.format(
															        model.__class__.__name__       ,a_					)					)
										if len(a_					) > 0:
															print('\n'.join(a_					)					)

					__magic_name__				=     {
					    'pretrained': False,
					    'use_shared_rel_pos_bias': True,
					    'use_abs_pos_emb': False,
					    'init_values': 0.1,
					}

					if is_finetuned:
										model_kwargs.update(
										    {
										        'num_classes': 1_0_0_0,
										        'use_mean_pooling': True,
										        'init_scale': 0.001,
										        'use_rel_pos_bias': True,
										    }					)

					__magic_name__				=     create_model(
					    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'       ,**a_       ,)
					__magic_name__				=     model.patch_embed.patch_size
					__magic_name__				=     (args.input_size // patch_size[0], args.input_size // patch_size[1])
					__magic_name__				=     torch.load(args.beit_checkpoint       ,map_location='cpu'					)

					print(F'Load ckpt from {args.beit_checkpoint}'					)
					__magic_name__				=     None
					for model_key in ("model", "module"):
										if model_key in checkpoint:
															__magic_name__				=     checkpoint[model_key]
															print(F'Load state_dict by model_key = {model_key}'					)
															break

					__magic_name__				=     list(checkpoint_model.keys()					)
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(a_					)

										if "relative_position_bias_table" in key:
															__magic_name__				=     checkpoint_model[key]
															__magic_name__,						__magic_name__				=     rel_pos_bias.size()
															__magic_name__,						__magic_name__				=     model.state_dict()[key].size()
															__magic_name__				=     model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(a_       ,a_       ,prefix=''					)

					return model




def        _lowercase  (					)					->						List[str]:




					'''simple docstring'''







					__magic_name__				=     get_args()

					__magic_name__				=     'ft1k' in args.hf_checkpoint_name
					__magic_name__				=     'large' in args.hf_checkpoint_name

					if is_finetuned:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
										# into this folder.
										import modeling_finetune  # noqa: F401
					else:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
										# into this folder
										# IMPORTANT: Note that for now we've only converted the down-stream
										# model and not the full pretrained model. This means for the integration
										# test you need to add a `return x` after the following line:
										# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
										# to make the integration test pass.
										import modeling_cyclical  # noqa: F401

					# 1. Create model config
					__magic_name__				=     DataaVecVisionConfig()
					if is_finetuned:
										__magic_name__				=     True
										__magic_name__				=     False
										__magic_name__				=     True
										__magic_name__				=     1_0_0_0

										__magic_name__				=     'huggingface/label-files'
										__magic_name__				=     'imagenet-1k-id2label.json'
										__magic_name__				=     json.load(open(hf_hub_download(a_       ,a_       ,repo_type='dataset'					)       ,'r'					)					)
										__magic_name__				=     {int(a_					): v for k, v in idalabel.items()}
										__magic_name__				=     idalabel
										__magic_name__				=     {v: k for k, v in idalabel.items()}
					else:
										__magic_name__				=     False
										__magic_name__				=     True
										__magic_name__				=     False

					if is_large:
										__magic_name__				=     1_0_2_4
										__magic_name__				=     4_0_9_6
										__magic_name__				=     2_4
										__magic_name__				=     1_6

					# 2. Load Beit model
					__magic_name__				=     load_beit_model(a_       ,a_       ,a_					)
					orig_model.eval()

					# 3. Forward Beit model
					__magic_name__				=     BeitImageProcessor(size=config.image_size       ,do_center_crop=a_					)
					__magic_name__				=     Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'					)
					__magic_name__				=     image_processor(images=a_       ,return_tensors='pt'					)
					__magic_name__				=     encoding['pixel_values']

					__magic_name__				=     (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__magic_name__				=     orig_model(*a_					)

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__magic_name__				=     DataaVecVisionForImageClassification(a_					)
										hf_model.eval()
										__magic_name__				=     False
										__magic_name__				=     'data2vec_vision.'
					else:
										__magic_name__				=     DataaVecVisionModel(a_					)
										hf_model.eval()
										__magic_name__				=     True
										__magic_name__				=     ''

					__magic_name__				=     create_rename_keys(a_       ,hf_prefix=a_       ,has_lm_head=a_					)
					__magic_name__				=     orig_model.state_dict()
					for src, dest in rename_keys:
										__magic_name__				=     state_dict.pop(a_					)
										__magic_name__				=     val

					read_in_q_k_v(a_       ,a_       ,hf_prefix=a_       ,has_lm_head=a_					)
					__magic_name__,						__magic_name__				=     hf_model.load_state_dict(a_       ,strict=a_					)
					print('HF missing'       ,a_					)
					print('HF unexpected_keys'       ,a_					)

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__magic_name__				=     hf_model(a_					)

					__magic_name__				=     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__magic_name__				=     torch.max(torch.abs(hf_output - orig_model_output					)					).item()

					print(F'max_absolute_diff = {max_absolute_diff}'					)
					__magic_name__				=     torch.allclose(a_       ,a_       ,atol=1e-3					)
					print('Do both models output the same tensors?'       ,'🔥' if success else '💩'					)
					if not success:
										raise Exception('Something went wRoNg'					)

					# 7. Save
					print(F'Saving to {args.hf_checkpoint_name}'					)
					hf_model.save_pretrained(args.hf_checkpoint_name					)
					image_processor.save_pretrained(args.hf_checkpoint_name					)


if __name__ == "__main__":
						main()
						# Run the following to convert checkpoints
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_base.pt \
						#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./pretrained_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large"
						#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
						#          --beit_checkpoint ./finetuned_large.pt \
						#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"







from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						if np.any(tableau[:, -1]	,			where=tableau[:, -1] < 0   ):
											raise ValueError('RHS must be > 0'   )

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

						# Number of decision variables x1, x2, x3...
						__magic_name__				=     n_vars

						# Number of artificial variables to be minimised
						__magic_name__				=     len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

						# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
						__magic_name__				=     (self.n_art_vars > 0) + 1

						# Number of slack variables added to make inequalities into equalities
						__magic_name__				=     self.n_rows - self.n_stages

						# Objectives for each stage
						__magic_name__				=     ['max']

						# In two stage simplex, first minimise then maximise
						if self.n_art_vars:
											self.objectives.append('min'   )

						__magic_name__				=     ['']

						# Index of current pivot row and column
						__magic_name__				=     None
						__magic_name__				=     None

						# Does objective row only contain (non)-negative values?
						__magic_name__				=     False







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





						if len(__UpperCamelCase   ) != 3:
											raise ValueError('Must provide n_vars, n_slack, and n_art_vars'   )

						if not all(x >= 0 and isinstance(__UpperCamelCase	,			__UpperCamelCase   ) for x in args   ):
											raise ValueError('All arguments must be non-negative integers'   )

						# decision | slack | artificial
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						for i in range(3   ):
											for j in range(args[i]   ):
																titles.append(string_starts[i] + str(j + 1   )   )
						titles.append('RHS'   )
						return titles







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     np.argmax(sign * tableau[0, : self.n_vars]   )

						# Choice is only valid if below 0 for maximise, and above for minimise
						if sign * self.tableau[0, col_idx] <= 0:
											__magic_name__				=     True
											return 0, 0

						# Pivot row is chosen as having the lowest quotient when elements of
						# the pivot column divide the right-hand side

						# Slice excluding the objective rows
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

						# Elements of pivot column within slice
						__magic_name__				=     tableau[s, col_idx]

						# Array filled with nans
						__magic_name__				=     np.full(self.n_rows - self.n_stages	,			np.nan   )

						# If element in pivot column is greater than zeron_stages, return
						# quotient or nan otherwise
						__magic_name__				=     np.divide(__UpperCamelCase	,			__UpperCamelCase	,			out=__UpperCamelCase	,			where=divisor > 0   )

						# Arg of minimum quotient excluding the nan values. n_stages is added
						# to compensate for earlier exclusion of objective columns
						__magic_name__				=     np.nanargmin(__UpperCamelCase   ) + self.n_stages
						return row_idx, col_idx







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     piv_row[col_idx]

						# Entry becomes 1
						piv_row *= 1 / piv_val

						# Variable in pivot column becomes basic, ie the only non-zero entry
						for idx, coeff in enumerate(tableau[:, col_idx]   ):
											tableau[idx] += -coeff * piv_row
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

						# Slice containing ids for artificial columns
						__magic_name__				=     slice(-self.n_art_vars - 1	,			-1   )

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

						# Delete the objective row of the first stage
						__magic_name__				=     np.delete(__UpperCamelCase	,			0	,			axis=0   )

						__magic_name__				=     1
						self.n_rows -= 1
						__magic_name__				=     0
						__magic_name__				=     False
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     self.generate_col_titles(
																    self.n_vars	,			self.n_slack	,			self.n_art_vars   )

																# Find the values of each variable at optimal solution
																return self.interpret_tableau(self.tableau	,			self.col_titles   )

											__magic_name__,						__magic_name__				=     self.find_pivot(self.tableau   )

											# If there are no more negative values in objective row
											if self.stop_iter:
																# Delete artificial variable columns and rows. Update attributes
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

						for i in range(self.n_vars   ):
											# Gives ids of nonzero entries in the ith column
											__magic_name__				=     np.nonzero(tableau[:, i]   )
											__magic_name__				=     len(nonzero[0]   )

											# First entry in the nonzero ids
											__magic_name__				=     nonzero[0][0]
											__magic_name__				=     tableau[nonzero_rowidx, i]

											# If there is only one nonzero value in column, which is one
											if n_nonzero == nonzero_val == 1:
																__magic_name__				=     tableau[nonzero_rowidx, -1]
																__magic_name__				=     rhs_val

        # Check for basic variables
						for title in col_titles:
											# Don't add RHS or slack variables to output dict
											if title[0] not in "R-s-a":
																output_dict.setdefault(__UpperCamelCase	,			0   )
						return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()




