


'''simple docstring'''

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				: Optional[Any]							    =				logging.get_logger(__name__)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				[]

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

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(lowerCamelCase,				lowerCamelCase)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				dct.pop(lowerCamelCase)
						__lowerCAmelCase							       =				val

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

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase							       =				BitConfig(
						    global_padding='''same''',				layer_type='''bottleneck''',				depths=(3, 4, 9),				out_features=['''stage3'''],				embedding_dynamic_padding=lowerCamelCase,				)
						__lowerCAmelCase							       =				ViTHybridConfig(backbone_config=lowerCamelCase,				image_size=3_8_4,				num_labels=1_0_0_0)
						__lowerCAmelCase							       =				False

						# load original model from timm
						__lowerCAmelCase							       =				timm.create_model(lowerCamelCase,				pretrained=lowerCamelCase)
						timm_model.eval()

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

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

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

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

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

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

						__lowerCAmelCase							       =				prepare_img()
						__lowerCAmelCase							       =				transform(lowerCamelCase).unsqueeze(0)
						__lowerCAmelCase							       =				processor(lowerCamelCase,				return_tensors='''pt''').pixel_values

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

						# verify logits
						with torch.no_grad():
												__lowerCAmelCase							       =				model(lowerCamelCase)
												__lowerCAmelCase							       =				outputs.logits

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

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

						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				: Any							    =				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				: Optional[Any]							    =				parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase				: str							    =				["""gpt2"""]
_UpperCAmelCase				: Optional[int]							    =				"""gpt2"""

if is_tf_available():


	class       a__				( tf.Module      ):







					"""simple docstring"""



					def __init__(self       ,			__lowercase		):
											super().__init__()
											__lowerCAmelCase							       =				tokenizer
											__lowerCAmelCase							       =				AutoConfig.from_pretrained(__lowercase		)
											__lowerCAmelCase							       =				TFGPTaLMHeadModel.from_config(__lowercase		)




					@tf.function(input_signature=(tf.TensorSpec((None,)       ,			tf.string       ,			name='''text'''		),)		)
					def 					_snake_case  (self       ,			__lowercase		):
											__lowerCAmelCase							       =				self.tokenizer(__lowercase		)
											__lowerCAmelCase							       =				tokenized['''input_ids'''].to_tensor()

											__lowerCAmelCase							       =				tf.cast(input_ids_dense > 0       ,			tf.intaa		)
											# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

											__lowerCAmelCase							       =				self.model(input_ids=__lowercase       ,			attention_mask=__lowercase		)['''logits''']

											return outputs


@require_tf
@require_keras_nlp
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										super().setUp()

										__lowerCAmelCase							       =				[GPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__lowerCAmelCase							       =				[TFGPTaTokenizer.from_pretrained(__lowercase		) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers		) == len(self.tf_tokenizers		)

										__lowerCAmelCase							       =				[
										    '''This is a straightforward English test sentence.''',
										    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
										    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
										    '''And some much more rare Chinese: 齉 堃 齉堃''',
										    '''Je vais aussi écrire en français pour tester les accents''',
										    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
										]
										__lowerCAmelCase							       =				list(zip(self.test_sentences       ,			self.test_sentences[::-1]		)		)




				def 					_snake_case  (self		):
										for tokenizer, tf_tokenizer in zip(self.tokenizers       ,			self.tf_tokenizers		):
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tokenizer([test_inputs]       ,			return_tensors='''tf'''		)
																						__lowerCAmelCase							       =				tf_tokenizer([test_inputs]		)

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												__lowerCAmelCase							       =				python_outputs[key].numpy()
																												__lowerCAmelCase							       =				tf_outputs[key].numpy()

																												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																												self.assertTrue(tf.reduce_all(tf.cast(__lowercase       ,			tf.intaa		) == tf_outputs_values		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.function(__lowercase		)
																for test_inputs in self.test_sentences:
																						__lowerCAmelCase							       =				tf.constant(__lowercase		)
																						__lowerCAmelCase							       =				compiled_tokenizer(__lowercase		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)

																						for key in eager_outputs.keys():
																												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				ModelToSave(tokenizer=__lowercase		)
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				model.serving(__lowercase		)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__lowerCAmelCase							       =				Path(__lowercase		) / '''saved.model'''
																						tf.saved_model.save(__lowercase       ,			__lowercase       ,			signatures={'''serving_default''': model.serving}		)
																						__lowerCAmelCase							       =				tf.saved_model.load(__lowercase		)
																__lowerCAmelCase							       =				loaded_model.signatures['''serving_default'''](__lowercase		)['''output_0''']
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																__lowerCAmelCase							       =				tf_tokenizer(__lowercase		)  # Build model with some sample inputs

																__lowerCAmelCase							       =				tf_tokenizer.get_config()
																__lowerCAmelCase							       =				TFGPTaTokenizer.from_config(__lowercase		)
																__lowerCAmelCase							       =				model_from_config(__lowercase		)

																for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)




				@slow
				def 					_snake_case  (self		):
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__lowerCAmelCase							       =				12_31_23

																for max_length in [3, 5, 10_24]:
																						__lowerCAmelCase							       =				tf.convert_to_tensor([self.test_sentences[0]]		)
																						__lowerCAmelCase							       =				tf_tokenizer(__lowercase       ,			max_length=__lowercase		)

																						__lowerCAmelCase							       =				out['''input_ids'''].numpy().shape[1]

																						assert out_length == max_length

