



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


@dataclass
class   __lowerCAmelCase  :
					"""simple docstring"""






					A__					:		str      							=     field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
					A__					:		Optional[str]      							=     field(
					    default=UpperCAmelCase_				,  metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
					A__					:		Optional[str]      							=     field(
					    default=UpperCAmelCase_				,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
					A__					:		Optional[str]      							=     field(
					    default=UpperCAmelCase_				,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,  )
					A__					:		bool      							=     field(default=UpperCAmelCase_				,  metadata={"help": "Whether tp freeze the encoder."}   )
					A__					:		bool      							=     field(default=UpperCAmelCase_				,  metadata={"help": "Whether  to freeze the embeddings."}   )







@dataclass
class   __lowerCAmelCase  :
					"""simple docstring"""






					A__					:		str      							=     field(
					    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
					A__					:		Optional[str]      							=     field(
					    default="summarization"				,  metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}				,  )
					A__					:		Optional[int]      							=     field(
					    default=10_24				,  metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }				,  )
					A__					:		Optional[int]      							=     field(
					    default=1_28				,  metadata={
					        "help": (
					            "The maximum total sequence length for target text after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }				,  )
					A__					:		Optional[int]      							=     field(
					    default=1_42				,  metadata={
					        "help": (
					            "The maximum total sequence length for validation target text after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded. "
					            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
					            "during ``evaluate`` and ``predict``."
					        )
					    }				,  )
					A__					:		Optional[int]      							=     field(
					    default=1_42				,  metadata={
					        "help": (
					            "The maximum total sequence length for test target text after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }				,  )
					A__					:		Optional[int]      							=     field(default=-1				,  metadata={"help": "# training examples. -1 means use all."}   )
					A__					:		Optional[int]      							=     field(default=-1				,  metadata={"help": "# validation examples. -1 means use all."}   )
					A__					:		Optional[int]      							=     field(default=-1				,  metadata={"help": "# test examples. -1 means use all."}   )
					A__					:		Optional[str]      							=     field(default=UpperCAmelCase_				,  metadata={"help": "Source language id for translation."}   )
					A__					:		Optional[str]      							=     field(default=UpperCAmelCase_				,  metadata={"help": "Target language id for translation."}   )
					A__					:		Optional[int]      							=     field(default=UpperCAmelCase_				,  metadata={"help": "# num_beams to use for evaluation."}   )
					A__					:		bool      							=     field(
					    default=UpperCAmelCase_				,  metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}				,  )







def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			logger.info(f'''***** {split} metrics *****'''       )
			for key in sorted(metrics.keys()       ):
						logger.info(f'''  {key} = {metrics[key]}'''       )
			save_json(__UpperCamelCase			,						os.path.join(__UpperCamelCase			,						f'''{split}_results.json'''       )       )


def A    (							)    ->   List[str]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			A__	  =     HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)       )

			if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						A__  ,			A__  ,			A__	  =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
			else:
						A__  ,			A__  ,			A__	  =     parser.parse_args_into_dataclasses()

			check_output_dir(__UpperCamelCase       )

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,						datefmt='%m/%d/%Y %H:%M:%S'			,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,						)
			logger.warning(
			    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'			,						training_args.local_rank			,						training_args.device			,						training_args.n_gpu			,						bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED       )			,						training_args.fpaa			,						)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank       ):
						transformers.utils.logging.set_verbosity_info()
			logger.info('Training/evaluation parameters %s'			,						__UpperCamelCase       )

			# Set seed
			set_seed(training_args.seed       )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			A__	  =     AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,						cache_dir=model_args.cache_dir			,						)

			A__	  =     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
			for p in extra_model_params:
						if getattr(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       ):
									assert hasattr(__UpperCamelCase			,						__UpperCamelCase       ), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
									setattr(__UpperCamelCase			,						__UpperCamelCase			,						getattr(__UpperCamelCase			,						__UpperCamelCase       )       )

			A__	  =     AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,						cache_dir=model_args.cache_dir			,						)
			A__	  =     AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path			,						from_tf='.ckpt' in model_args.model_name_or_path			,						config=__UpperCamelCase			,						cache_dir=model_args.cache_dir			,						)

			# use task specific params
			use_task_specific_params(__UpperCamelCase			,						data_args.task       )

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						A__	  =     model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(__UpperCamelCase			,						(MBartTokenizer, MBartTokenizerFast)       ):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(__UpperCamelCase			,						__UpperCamelCase       ):
									A__	  =     tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									A__	  =     tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

			if model_args.freeze_embeds:
						freeze_embeds(__UpperCamelCase       )
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder()       )
						assert_all_frozen(model.get_encoder()       )

			A__	  =     SeqaSeqDataset

			# Get datasets
			A__	  =     (
			    dataset_class(
			        __UpperCamelCase			,						type_path='train'			,						data_dir=data_args.data_dir			,						n_obs=data_args.n_train			,						max_target_length=data_args.max_target_length			,						max_source_length=data_args.max_source_length			,						prefix=model.config.prefix or ''			,						)
			    if training_args.do_train
			    else None
			)
			A__	  =     (
			    dataset_class(
			        __UpperCamelCase			,						type_path='val'			,						data_dir=data_args.data_dir			,						n_obs=data_args.n_val			,						max_target_length=data_args.val_max_target_length			,						max_source_length=data_args.max_source_length			,						prefix=model.config.prefix or ''			,						)
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			A__	  =     (
			    dataset_class(
			        __UpperCamelCase			,						type_path='test'			,						data_dir=data_args.data_dir			,						n_obs=data_args.n_test			,						max_target_length=data_args.test_max_target_length			,						max_source_length=data_args.max_source_length			,						prefix=model.config.prefix or ''			,						)
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			A__	  =     (
			    build_compute_metrics_fn(data_args.task			,						__UpperCamelCase       ) if training_args.predict_with_generate else None
			)
			A__	  =     SeqaSeqTrainer(
			    model=__UpperCamelCase			,						args=__UpperCamelCase			,						data_args=__UpperCamelCase			,						train_dataset=__UpperCamelCase			,						eval_dataset=__UpperCamelCase			,						data_collator=SeqaSeqDataCollator(
			        __UpperCamelCase			,						__UpperCamelCase			,						model.config.decoder_start_token_id			,						training_args.tpu_num_cores       )			,						compute_metrics=__UpperCamelCase			,						tokenizer=__UpperCamelCase			,						)

			A__	  =     {}
			# Training
			if training_args.do_train:
						logger.info('*** Train ***'       )

						A__	  =     trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
						A__	  =     train_result.metrics
						A__	  =     data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics('train'			,						__UpperCamelCase			,						training_args.output_dir       )
									all_metrics.update(__UpperCamelCase       )

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir			,						'trainer_state.json'       )       )

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***'       )

						A__	  =     trainer.evaluate(metric_key_prefix='val'       )
						A__	  =     data_args.n_val
						A__	  =     round(metrics['val_loss']			,						4       )

						if trainer.is_world_process_zero():
									handle_metrics('val'			,						__UpperCamelCase			,						training_args.output_dir       )
									all_metrics.update(__UpperCamelCase       )

			if training_args.do_predict:
						logger.info('*** Predict ***'       )

						A__	  =     trainer.predict(test_dataset=__UpperCamelCase			,						metric_key_prefix='test'       )
						A__	  =     test_output.metrics
						A__	  =     data_args.n_test

						if trainer.is_world_process_zero():
									A__	  =     round(metrics['test_loss']			,						4       )
									handle_metrics('test'			,						__UpperCamelCase			,						training_args.output_dir       )
									all_metrics.update(__UpperCamelCase       )

									if training_args.predict_with_generate:
												A__	  =     tokenizer.batch_decode(
												    test_output.predictions			,						skip_special_tokens=__UpperCamelCase			,						clean_up_tokenization_spaces=__UpperCamelCase       )
												A__	  =     lmap(str.strip			,						__UpperCamelCase       )
												write_txt_file(__UpperCamelCase			,						os.path.join(training_args.output_dir			,						'test_generations.txt'       )       )

			if trainer.is_world_process_zero():
						save_json(__UpperCamelCase			,						os.path.join(training_args.output_dir			,						'all_results.json'       )       )

			return all_metrics


def A    (							__UpperCamelCase       )    ->   Any:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()









import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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


def A    (							__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Dict:
			A__	  =     'backbone.' if is_semantic else ''

			A__	  =     []
			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'''beit.encoder.layer.{i}.layernorm_before.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')       )
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')       )
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')       )

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

			if has_lm_head:
						# mask token + layernorm
						rename_keys.extend(
						    [
						        ('mask_token', 'beit.embeddings.mask_token'),
						        ('norm.weight', 'layernorm.weight'),
						        ('norm.bias', 'layernorm.bias'),
						    ]       )
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
						        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
						        ('head.weight', 'classifier.weight'),
						        ('head.bias', 'classifier.bias'),
						    ]       )

			return rename_keys


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

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

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

						A__	  =     gamma_a
						A__	  =     gamma_a


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							)    ->   Dict:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False       )    ->   str:
			A__	  =     False if 'rvlcdip' in checkpoint_url else True
			A__	  =     BeitConfig(use_absolute_position_embeddings=__UpperCamelCase			,						use_mask_token=__UpperCamelCase       )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						A__	  =     1_024
						A__	  =     4_096
						A__	  =     24
						A__	  =     16

			# labels
			if "rvlcdip" in checkpoint_url:
						A__	  =     16
						A__	  =     'huggingface/label-files'
						A__	  =     'rvlcdip-id2label.json'
						A__	  =     json.load(open(hf_hub_download(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
						A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
						A__	  =     idalabel
						A__	  =     {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			A__	  =     torch.hub.load_state_dict_from_url(__UpperCamelCase			,						map_location='cpu'       )['model']

			A__	  =     create_rename_keys(__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			read_in_q_k_v(__UpperCamelCase			,						__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )

			# load HuggingFace model
			A__	  =     BeitForMaskedImageModeling(__UpperCamelCase       ) if has_lm_head else BeitForImageClassification(__UpperCamelCase       )
			model.eval()
			model.load_state_dict(__UpperCamelCase       )

			# Check outputs on an image
			A__	  =     BeitImageProcessor(
			    size=config.image_size			,						resample=PILImageResampling.BILINEAR			,						do_center_crop=__UpperCamelCase       )
			A__	  =     prepare_img()

			A__	  =     image_processor(images=__UpperCamelCase			,						return_tensors='pt'       )
			A__	  =     encoding['pixel_values']

			A__	  =     model(__UpperCamelCase       )
			A__	  =     outputs.logits

			# verify logits
			A__	  =     [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
			assert logits.shape == torch.Size(__UpperCamelCase       ), "Shape of logits not as expected"

			Path(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
			print(f'''Saving model to {pytorch_dump_folder_path}'''       )
			model.save_pretrained(__UpperCamelCase       )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
			image_processor.save_pretrained(__UpperCamelCase       )

			if push_to_hub:
						if has_lm_head:
									A__	  =     'dit-base' if 'base' in checkpoint_url else 'dit-large'
						else:
									A__	  =     'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
						image_processor.push_to_hub(
						    repo_path_or_name=Path(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add image processor'			,						use_temp_dir=__UpperCamelCase			,						)
						model.push_to_hub(
						    repo_path_or_name=Path(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add model'			,						use_temp_dir=__UpperCamelCase			,						)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
							    type=str,
							    help='''URL to the original PyTorch checkpoint (.pth file).''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




