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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__=False       ,  snake_case__=False       ,  snake_case__="data2vec."							)					->     List[Any]:
							lowerCAmelCase     		=		'''backbone.''' if is_semantic else ''''''

							lowerCAmelCase     		=		[]
							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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__=False       ,  snake_case__=False       ,  snake_case__="data2vec_vision."							)					->     Optional[Any]:
							for i in range(config.num_hidden_layers							):
														lowerCAmelCase     		=		'''backbone.''' if is_semantic else ''''''
														# queries, keys and values
														lowerCAmelCase     		=		state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"							)
														lowerCAmelCase     		=		state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"							)
														lowerCAmelCase     		=		state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"							)

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

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

														lowerCAmelCase     		=		gamma_a
														lowerCAmelCase     		=		gamma_a

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

																					lowerCAmelCase     		=		table
																					lowerCAmelCase     		=		index

def        SCREAMING_SNAKE_CASE_					(						)					->     Dict:
							lowerCAmelCase     		=		argparse.ArgumentParser(
							    '''Convert Data2VecVision to HF for image classification and pretraining'''       ,  add_help=snake_case__							)
							parser.add_argument('''--hf_checkpoint_name'''       ,  type=snake_case__							)
							parser.add_argument('''--input_size'''       ,  default=2_2_4       ,  type=snake_case__       ,  help='''images input size'''							)
							parser.add_argument('''--beit_checkpoint'''       ,  default=''''''       ,  help='''beit checkpoint'''							)

							return parser.parse_args()

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[str]:
							def load_state_dict(snake_case__       ,  snake_case__       ,  snake_case__=""       ,  snake_case__="relative_position_index"							):
														lowerCAmelCase     		=		[]
														lowerCAmelCase     		=		[]
														lowerCAmelCase     		=		[]
														# copy state_dict so _load_from_state_dict can modify it
														lowerCAmelCase     		=		getattr(snake_case__       ,  '''_metadata'''       ,  snake_case__							)
														lowerCAmelCase     		=		state_dict.copy()
														if metadata is not None:
																					lowerCAmelCase     		=		metadata

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

														load(snake_case__       ,  prefix=snake_case__							)

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

														lowerCAmelCase     		=		warn_missing_keys

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

							lowerCAmelCase     		=		{
							    '''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''': 0.0_01,
														        '''use_rel_pos_bias''': True,
														    }							)

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

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

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

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

							load_state_dict(snake_case__       ,  snake_case__       ,  prefix=''''''							)

							return model

def        SCREAMING_SNAKE_CASE_					(						)					->     List[str]:
							lowerCAmelCase     		=		get_args()

							lowerCAmelCase     		=		'''ft1k''' in args.hf_checkpoint_name
							lowerCAmelCase     		=		'''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
							lowerCAmelCase     		=		DataaVecVisionConfig()
							if is_finetuned:
														lowerCAmelCase     		=		True
														lowerCAmelCase     		=		False
														lowerCAmelCase     		=		True
														lowerCAmelCase     		=		1_0_0_0

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

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

							# 2. Load Beit model
							lowerCAmelCase     		=		load_beit_model(snake_case__       ,  snake_case__       ,  snake_case__							)
							orig_model.eval()

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

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

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

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

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

							# 5. Forward HF Data2VecVision model
							with torch.no_grad():
														lowerCAmelCase     		=		hf_model(snake_case__							)

							lowerCAmelCase     		=		hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

							print(f"max_absolute_diff = {max_absolute_diff}"							)
							lowerCAmelCase     		=		torch.allclose(snake_case__       ,  snake_case__       ,  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"




import warnings

from .generation import FlaxGenerationMixin




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	warnings.warn(
	    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
	    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""				,					UpperCamelCase_				,					)



