



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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				str=False		):

				"""simple docstring"""
				a__				      =		[]
				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'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
								rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
								rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
								rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
								rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
								rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
								rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
								rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
								rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')		)
								rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')		)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("""cls_token""", """deit.embeddings.cls_token"""),
				        ("""dist_token""", """deit.embeddings.distillation_token"""),
				        ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
				        ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
				        ("""pos_embed""", """deit.embeddings.position_embeddings"""),
				    ]		)

				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 "deit" from all keys that start with "deit"
								a__				      =		[(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
				else:
								# layernorm + classification heads
								rename_keys.extend(
								    [
								        ("""norm.weight""", """deit.layernorm.weight"""),
								        ("""norm.bias""", """deit.layernorm.bias"""),
								        ("""head.weight""", """cls_classifier.weight"""),
								        ("""head.bias""", """cls_classifier.bias"""),
								        ("""head_dist.weight""", """distillation_classifier.weight"""),
								        ("""head_dist.bias""", """distillation_classifier.bias"""),
								    ]		)

				return rename_keys

def 			A_      (	__a     :				Optional[int] ,		__a     :				Dict ,		__a     :				Tuple=False		):

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

def 			A_      (	__a     :				str ,		__a     :				List[Any] ,		__a     :				str		):

				"""simple docstring"""
				a__				      =		dct.pop(__a		)
				a__				      =		val

def 			A_      (	):

				"""simple docstring"""
				a__				      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				a__				      =		Image.open(requests.get(__a ,		stream=__a		).raw		)
				return im

@torch.no_grad()
def 			A_      (	__a     :				Optional[int] ,		__a     :				int		):

				"""simple docstring"""
				a__				      =		DeiTConfig()
				# all deit models have fine-tuned heads
				a__				      =		False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				a__				      =		1_000
				a__				      =		"""huggingface/label-files"""
				a__				      =		"""imagenet-1k-id2label.json"""
				a__				      =		json.load(open(hf_hub_download(__a ,		__a ,		repo_type="""dataset"""		) ,		"""r"""		)		)
				a__				      =		{int(__a		): v for k, v in idalabel.items()}
				a__				      =		idalabel
				a__				      =		{v: k for k, v in idalabel.items()}
				a__				      =		int(deit_name[-6:-4]		)
				a__				      =		int(deit_name[-3:]		)
				# size of the architecture
				if deit_name[9:].startswith("""tiny"""		):
								a__				      =		192
								a__				      =		768
								a__				      =		12
								a__				      =		3
				elif deit_name[9:].startswith("""small"""		):
								a__				      =		384
								a__				      =		1_536
								a__				      =		12
								a__				      =		6
				if deit_name[9:].startswith("""base"""		):
								pass
				elif deit_name[4:].startswith("""large"""		):
								a__				      =		1_024
								a__				      =		4_096
								a__				      =		24
								a__				      =		16

				# load original model from timm
				a__				      =		timm.create_model(__a ,		pretrained=__a		)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				a__				      =		timm_model.state_dict()
				a__				      =		create_rename_keys(__a ,		__a		)
				for src, dest in rename_keys:
								rename_key(__a ,		__a ,		__a		)
				read_in_q_k_v(__a ,		__a ,		__a		)

				# load HuggingFace model
				a__				      =		DeiTForImageClassificationWithTeacher(__a		).eval()
				model.load_state_dict(__a		)

				# Check outputs on an image, prepared by DeiTImageProcessor
				a__				      =		int(
				    (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
				a__				      =		DeiTImageProcessor(size=__a ,		crop_size=config.image_size		)
				a__				      =		image_processor(images=prepare_img() ,		return_tensors="""pt"""		)
				a__				      =		encoding["""pixel_values"""]
				a__				      =		model(__a		)

				a__				      =		timm_model(__a		)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(__a ,		outputs.logits ,		atol=1e-3		)

				Path(__a		).mkdir(exist_ok=__a		)
				print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(__a		)


if __name__ == "__main__":
				UpperCAmelCase							 =					argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--deit_name""",
				    default="""vit_deit_base_distilled_patch16_224""",
				    type=str,
				    help="""Name of the DeiT 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."""
				)

				UpperCAmelCase							 =					parser.parse_args()
				convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
	raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
UpperCAmelCase							 =					False
UpperCAmelCase							 =					False

def 			A_      (	__a     :				Namespace		):

				"""simple docstring"""
				return TrainCommand(__a		)




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@staticmethod
							def _a      (							a_		):
											a__				      =		parser.add_parser("""train"""  ,  help="""CLI tool to train a model on a task."""		)

											train_parser.add_argument(
											    """--train_data"""  ,  type=a_  ,  required=a_  ,  help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""  ,  )
											train_parser.add_argument(
											    """--column_label"""  ,  type=a_  ,  default=0  ,  help="""Column of the dataset csv file with example labels."""		)
											train_parser.add_argument(
											    """--column_text"""  ,  type=a_  ,  default=1  ,  help="""Column of the dataset csv file with example texts."""		)
											train_parser.add_argument(
											    """--column_id"""  ,  type=a_  ,  default=2  ,  help="""Column of the dataset csv file with example ids."""		)
											train_parser.add_argument(
											    """--skip_first_row"""  ,  action="""store_true"""  ,  help="""Skip the first row of the csv file (headers)."""		)

											train_parser.add_argument("""--validation_data"""  ,  type=a_  ,  default=""""""  ,  help="""path to validation dataset."""		)
											train_parser.add_argument(
											    """--validation_split"""  ,  type=a_  ,  default=0.1  ,  help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""  ,  )

											train_parser.add_argument("""--output"""  ,  type=a_  ,  default="""./"""  ,  help="""path to saved the trained model."""		)

											train_parser.add_argument(
											    """--task"""  ,  type=a_  ,  default="""text_classification"""  ,  help="""Task to train the model on."""		)
											train_parser.add_argument(
											    """--model"""  ,  type=a_  ,  default="""bert-base-uncased"""  ,  help="""Model's name or path to stored model."""		)
											train_parser.add_argument("""--train_batch_size"""  ,  type=a_  ,  default=32  ,  help="""Batch size for training."""		)
											train_parser.add_argument("""--valid_batch_size"""  ,  type=a_  ,  default=64  ,  help="""Batch size for validation."""		)
											train_parser.add_argument("""--learning_rate"""  ,  type=a_  ,  default=3E-5  ,  help="""Learning rate."""		)
											train_parser.add_argument("""--adam_epsilon"""  ,  type=a_  ,  default=1E-0_8  ,  help="""Epsilon for Adam optimizer."""		)
											train_parser.set_defaults(func=a_		)


							def __init__(							self  ,  a_		):
											a__				      =		logging.get_logger("""transformers-cli/training"""		)

											a__				      =		"""tf""" if is_tf_available() else """torch"""

											os.makedirs(args.output  ,  exist_ok=a_		)
											a__				      =		args.output

											a__				      =		args.column_label
											a__				      =		args.column_text
											a__				      =		args.column_id

											self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''		)
											if args.task == "text_classification":
															a__				      =		TextClassificationPipeline.from_pretrained(args.model		)
											elif args.task == "token_classification":
															raise NotImplementedError
											elif args.task == "question_answering":
															raise NotImplementedError

											self.logger.info(F'''Loading dataset from {args.train_data}'''		)
											a__				      =		Processor.create_from_csv(
											    args.train_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )
											a__				      =		None
											if args.validation_data:
															self.logger.info(F'''Loading validation dataset from {args.validation_data}'''		)
															a__				      =		Processor.create_from_csv(
															    args.validation_data  ,  column_label=args.column_label  ,  column_text=args.column_text  ,  column_id=args.column_id  ,  skip_first_row=args.skip_first_row  ,  )

											a__				      =		args.validation_split
											a__				      =		args.train_batch_size
											a__				      =		args.valid_batch_size
											a__				      =		args.learning_rate
											a__				      =		args.adam_epsilon


							def _a      (							self		):
											if self.framework == "tf":
															return self.run_tf()
											return self.run_torch()


							def _a      (							self		):
											raise NotImplementedError







							def _a      (							self		):
											self.pipeline.fit(
											    self.train_dataset  ,  validation_data=self.valid_dataset  ,  validation_split=self.validation_split  ,  learning_rate=self.learning_rate  ,  adam_epsilon=self.adam_epsilon  ,  train_batch_size=self.train_batch_size  ,  valid_batch_size=self.valid_batch_size  ,  )

											# Save trained pipeline
											self.pipeline.save_pretrained(self.output		)

