

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,
)


lowerCamelCase      				=     logging.getLogger(__name__)





@dataclass
class     snake_case_			:


							"""simple docstring"""





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





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}					)
							__UpperCAmelCase       =field(
							    default="""summarization"""			, metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}			, )
							__UpperCAmelCase       =field(
							    default=1024			, metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=128			, metadata={
							        """help""": (
							            """The maximum total sequence length for target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=142			, 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``."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=142			, 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."""
							        )
							    }			, )
							__UpperCAmelCase       =field(default=-1			, metadata={"""help""": """# training examples. -1 means use all."""}					)
							__UpperCAmelCase       =field(default=-1			, metadata={"""help""": """# validation examples. -1 means use all."""}					)
							__UpperCAmelCase       =field(default=-1			, metadata={"""help""": """# test examples. -1 means use all."""}					)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Source language id for translation."""}					)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """Target language id for translation."""}					)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """# num_beams to use for evaluation."""}					)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}			, )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					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        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							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.
										__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
					else:
										__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							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.

					__lowerCAmelCase					=							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		,					)

					__lowerCAmelCase					=							('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__      )      )

					__lowerCAmelCase					=							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		,					)
					__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							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__      ):
															__lowerCAmelCase					=							tokenizer.lang_code_to_id[data_args.tgt_lang]
										else:
															__lowerCAmelCase					=							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()      )

					__lowerCAmelCase					=							SeqaSeqDataset

					# Get datasets
					__lowerCAmelCase					=							(
					    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
					)
					__lowerCAmelCase					=							(
					    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
					)
					__lowerCAmelCase					=							(
					    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
					__lowerCAmelCase					=							(
					    build_compute_metrics_fn(data_args.task		,					UpperCAmelCase__      ) if training_args.predict_with_generate else None
					)
					__lowerCAmelCase					=							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__		,					)

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

										__lowerCAmelCase					=							trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
										__lowerCAmelCase					=							train_result.metrics
										__lowerCAmelCase					=							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 ***'      )

										__lowerCAmelCase					=							trainer.evaluate(metric_key_prefix='val'      )
										__lowerCAmelCase					=							data_args.n_val
										__lowerCAmelCase					=							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 ***'      )

										__lowerCAmelCase					=							trainer.predict(test_dataset=UpperCAmelCase__		,					metric_key_prefix='test'      )
										__lowerCAmelCase					=							test_output.metrics
										__lowerCAmelCase					=							data_args.n_test

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

															if training_args.predict_with_generate:
																				__lowerCAmelCase					=							tokenizer.batch_decode(
																				    test_output.predictions		,					skip_special_tokens=UpperCAmelCase__		,					clean_up_tokenization_spaces=UpperCAmelCase__      )
																				__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					main()


if __name__ == "__main__":
		main()









import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if "img_encoder.pos_embed" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.pos_embed'		,					'vision_model.embeddings.position_embeddings'      )
					if "img_encoder.patch_embed.proj" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.patch_embed.proj'		,					'vision_model.embeddings.patch_embeddings.projection'      )
					if "img_encoder.patch_embed.norm" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.patch_embed.norm'		,					'vision_model.embeddings.layernorm'      )
					if "img_encoder.layers" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.layers'		,					'vision_model.encoder.stages'      )
					if "blocks" in name and "res" not in name:
										__lowerCAmelCase					=							name.replace('blocks'		,					'layers'      )
					if "attn" in name and "pre_assign" not in name:
										__lowerCAmelCase					=							name.replace('attn'		,					'self_attn'      )
					if "proj" in name and "self_attn" in name and "text" not in name:
										__lowerCAmelCase					=							name.replace('proj'		,					'out_proj'      )
					if "pre_assign_attn.attn.proj" in name:
										__lowerCAmelCase					=							name.replace('pre_assign_attn.attn.proj'		,					'pre_assign_attn.attn.out_proj'      )
					if "norm1" in name:
										__lowerCAmelCase					=							name.replace('norm1'		,					'layer_norm1'      )
					if "norm2" in name and "pre_assign" not in name:
										__lowerCAmelCase					=							name.replace('norm2'		,					'layer_norm2'      )
					if "img_encoder.norm" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.norm'		,					'vision_model.layernorm'      )
					# text encoder
					if "text_encoder.token_embedding" in name:
										__lowerCAmelCase					=							name.replace('text_encoder.token_embedding'		,					'text_model.embeddings.token_embedding'      )
					if "text_encoder.positional_embedding" in name:
										__lowerCAmelCase					=							name.replace('text_encoder.positional_embedding'		,					'text_model.embeddings.position_embedding.weight'      )
					if "text_encoder.transformer.resblocks." in name:
										__lowerCAmelCase					=							name.replace('text_encoder.transformer.resblocks.'		,					'text_model.encoder.layers.'      )
					if "ln_1" in name:
										__lowerCAmelCase					=							name.replace('ln_1'		,					'layer_norm1'      )
					if "ln_2" in name:
										__lowerCAmelCase					=							name.replace('ln_2'		,					'layer_norm2'      )
					if "c_fc" in name:
										__lowerCAmelCase					=							name.replace('c_fc'		,					'fc1'      )
					if "c_proj" in name:
										__lowerCAmelCase					=							name.replace('c_proj'		,					'fc2'      )
					if "text_encoder" in name:
										__lowerCAmelCase					=							name.replace('text_encoder'		,					'text_model'      )
					if "ln_final" in name:
										__lowerCAmelCase					=							name.replace('ln_final'		,					'final_layer_norm'      )
					# projection layers
					if "img_projector.linear_hidden." in name:
										__lowerCAmelCase					=							name.replace('img_projector.linear_hidden.'		,					'visual_projection.'      )
					if "img_projector.linear_out." in name:
										__lowerCAmelCase					=							name.replace('img_projector.linear_out.'		,					'visual_projection.3.'      )
					if "text_projector.linear_hidden" in name:
										__lowerCAmelCase					=							name.replace('text_projector.linear_hidden'		,					'text_projection'      )
					if "text_projector.linear_out" in name:
										__lowerCAmelCase					=							name.replace('text_projector.linear_out'		,					'text_projection.3'      )

					return name


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase					=							orig_state_dict.pop(UpperCAmelCase__      )

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase,	__lowerCAmelCase					=							int(key_split[2]      ), int(key_split[4]      )
															__lowerCAmelCase					=							config.vision_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[dim : dim * 2, :]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase					=							int(key_split[3]      )
															__lowerCAmelCase					=							config.text_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[
																				    dim : dim * 2, :
																				]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										else:
															__lowerCAmelCase					=							rename_key(UpperCAmelCase__      )
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				__lowerCAmelCase					=							val.squeeze_()
															else:
																				__lowerCAmelCase					=							val

					return orig_state_dict


def        __lowercase    (  ):

					"""simple docstring"""






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


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__="groupvit-gcc-yfcc"		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							GroupViTConfig()
					__lowerCAmelCase					=							GroupViTModel(UpperCAmelCase__      ).eval()

					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location='cpu'      )['model']
					__lowerCAmelCase					=							convert_state_dict(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase,	__lowerCAmelCase					=							model.load_state_dict(UpperCAmelCase__		,					strict=UpperCAmelCase__      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase__      ) == 0)

					# verify result
					__lowerCAmelCase					=							CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'      )
					__lowerCAmelCase					=							prepare_img()
					__lowerCAmelCase					=							processor(text=['a photo of a cat', 'a photo of a dog']		,					images=UpperCAmelCase__		,					padding=UpperCAmelCase__		,					return_tensors='pt'      )

					with torch.no_grad():
										__lowerCAmelCase					=							model(**UpperCAmelCase__      )

					if model_name == "groupvit-gcc-yfcc":
										__lowerCAmelCase					=							torch.tensor([[13.3523, 6.3629]]      )
					elif model_name == "groupvit-gcc-redcaps":
										__lowerCAmelCase					=							torch.tensor([[16.1873, 8.6230]]      )
					else:
										raise ValueError(F"""Model name {model_name} not supported."""      )
					assert torch.allclose(outputs.logits_per_image		,					UpperCAmelCase__		,					atol=1E-3      )

					processor.save_pretrained(UpperCAmelCase__      )
					model.save_pretrained(UpperCAmelCase__      )
					print('Successfully saved processor and model to'		,					UpperCAmelCase__      )

					if push_to_hub:
										print('Pushing to the hub...'      )
										processor.push_to_hub(UpperCAmelCase__		,					organization='nielsr'      )
										model.push_to_hub(UpperCAmelCase__		,					organization='nielsr'      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
		)
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
		parser.add_argument(
		    '''--model_name''',
		    default='''groupvit-gccy-fcc''',
		    type=str,
		    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






