
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()
UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=False							):
	lowercase       =    []

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    dct.pop(__SCREAMING_SNAKE_CASE							)
	lowercase       =    val






def 							UpperCAmelCase_		(							):
	lowercase       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowercase       =    Image.open(requests.get(__SCREAMING_SNAKE_CASE ,							stream=__SCREAMING_SNAKE_CASE							).raw							)
	return im






@torch.no_grad()
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=False							):
	lowercase       =    BitConfig(
	    global_padding='same' ,							layer_type='bottleneck' ,							depths=(3, 4, 9) ,							out_features=['stage3'] ,							embedding_dynamic_padding=__SCREAMING_SNAKE_CASE ,							)
	lowercase       =    ViTHybridConfig(backbone_config=__SCREAMING_SNAKE_CASE ,							image_size=384 ,							num_labels=1000							)
	lowercase       =    False

	# load original model from timm
	lowercase       =    timm.create_model(__SCREAMING_SNAKE_CASE ,							pretrained=__SCREAMING_SNAKE_CASE							)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	lowercase       =    timm_model.state_dict()
	if base_model:
		remove_classification_head_(__SCREAMING_SNAKE_CASE							)
	lowercase       =    create_rename_keys(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	for src, dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	read_in_q_k_v(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		lowercase       =    ViTHybridModel(__SCREAMING_SNAKE_CASE							).eval()
	else:
		lowercase       =    ViTHybridForImageClassification(__SCREAMING_SNAKE_CASE							).eval()
	model.load_state_dict(__SCREAMING_SNAKE_CASE							)

	# create image processor
	lowercase       =    create_transform(**resolve_data_config({} ,							model=__SCREAMING_SNAKE_CASE							)							)
	lowercase       =    transform.transforms

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

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

	lowercase       =    prepare_img()
	lowercase       =    transform(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)
	lowercase       =    processor(__SCREAMING_SNAKE_CASE ,							return_tensors='pt'							).pixel_values

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

	# verify logits
	with torch.no_grad():
		lowercase       =    model(__SCREAMING_SNAKE_CASE							)
		lowercase       =    outputs.logits

	print('Predicted class:' ,							logits.argmax(-1							).item()							)
	if base_model:
		lowercase       =    timm_model.forward_features(__SCREAMING_SNAKE_CASE							)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(__SCREAMING_SNAKE_CASE ,							outputs.pooler_output ,							atol=1e-3							)
	else:
		lowercase       =    timm_model(__SCREAMING_SNAKE_CASE							)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__SCREAMING_SNAKE_CASE ,							outputs.logits ,							atol=1e-3							)
	print('Looks ok!'							)

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

	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__":
	UpperCAmelCase	      = 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.'''
	)

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



import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   :




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case       ):
		return None







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       [
	    # (model_name, model_kwargs)
	    ("""bert-base-cased""", {}),
	    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
	]





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
		with NamedTemporaryFile(mode='w+t'       ) as vocab_file:
			vocab_file.write('\n'.join(snake_case       )       )
			vocab_file.flush()
			lowercase       =    BertTokenizerFast(vocab_file.name       )

		with TemporaryDirectory() as bert_save_dir:
			lowercase       =    BertModel(BertConfig(vocab_size=len(snake_case       )       )       )
			model.save_pretrained(snake_case       )
			self._test_export(snake_case		,    'pt'		,    12		,    snake_case       )





	@require_tf
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'tf'		,    12		,    **snake_case       )
			lowercase       =    quantize(Path(snake_case       )       )

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case       ).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'       )





	@require_torch
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			lowercase       =    self._test_export(snake_case		,    'pt'		,    12		,    **snake_case       )
			lowercase       =    quantize(snake_case       )

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(snake_case       ).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case=None		,    **snake_case       ):
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				lowercase       =    Path(snake_case       ).joinpath('model.onnx'       )

			# Remove folder if exists
			if path.parent.exists():
				path.parent.rmdir()

			# Export
			convert(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    **snake_case       )

			return path
		except Exception as e:
			self.fail(snake_case       )





	@require_torch
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import BertModel

		lowercase       =    BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'pt'       )





	@require_tf
	@require_tokenizers
	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import TFBertModel

		lowercase       =    TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'       )       )
		lowercase       =    BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'       )
		self._test_infer_dynamic_axis(snake_case		,    snake_case		,    'tf'       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case       ):
		lowercase       =    FeatureExtractionPipeline(snake_case		,    snake_case       )

		lowercase       =    ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    infer_shapes(snake_case		,    snake_case       )

		# Assert all variables are present
		self.assertEqual(len(snake_case       )		,    len(snake_case       )       )
		self.assertTrue(all(var_name in shapes for var_name in variable_names       )       )
		self.assertSequenceEqual(variable_names[:3]		,    snake_case       )
		self.assertSequenceEqual(variable_names[3:]		,    snake_case       )

		# Assert inputs are {0: batch, 1: sequence}
		for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
			self.assertDictEqual(shapes[var_name]		,    {0: 'batch', 1: 'sequence'}       )

		# Assert outputs are {0: batch, 1: sequence} and {0: batch}
		self.assertDictEqual(shapes['output_0']		,    {0: 'batch', 1: 'sequence'}       )
		self.assertDictEqual(shapes['output_1']		,    {0: 'batch'}       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['input_ids', 'attention_mask', 'token_type_ids']
		lowercase       =    {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		lowercase  ,					lowercase       =    ensure_valid_input(FuncContiguousArgs()		,    snake_case		,    snake_case       )

		# Should have exactly the same number of args (all are valid)
		self.assertEqual(len(snake_case       )		,    3       )

		# Should have exactly the same input names
		self.assertEqual(set(snake_case       )		,    set(snake_case       )       )

		# Parameter should be reordered according to their respective place in the function:
		# (input_ids, token_type_ids, attention_mask)
		self.assertEqual(snake_case		,    (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])       )

		# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
		lowercase  ,					lowercase       =    ensure_valid_input(FuncNonContiguousArgs()		,    snake_case		,    snake_case       )

		# Should have exactly the one arg (all before the one not provided "some_other_args")
		self.assertEqual(len(snake_case       )		,    1       )
		self.assertEqual(len(snake_case       )		,    1       )

		# Should have only "input_ids"
		self.assertEqual(inputs_args[0]		,    tokens['input_ids']       )
		self.assertEqual(ordered_input_names[0]		,    'input_ids'       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    generate_identified_filename(Path('/home/something/my_fake_model.onnx'       )		,    '-test'       )
		self.assertEqual('/home/something/my_fake_model-test.onnx'		,    generated.as_posix()       )

