
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,
)


lowercase						:      str									= logging.getLogger(__name__)




@dataclass
class 				lowerCamelCase__							:




	'''simple docstring'''




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




@dataclass
class 				lowerCamelCase__							:




	'''simple docstring'''




	_A			=  field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
	_A			=  field(
	    default='summarization'  ,		metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}  ,		)
	_A			=  field(
	    default=1_0_2_4  ,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }  ,		)
	_A			=  field(
	    default=1_2_8  ,		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			=  field(
	    default=1_4_2  ,		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			=  field(
	    default=1_4_2  ,		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			=  field(default=-1  ,		metadata={'help': '# training examples. -1 means use all.'})
	_A			=  field(default=-1  ,		metadata={'help': '# validation examples. -1 means use all.'})
	_A			=  field(default=-1  ,		metadata={'help': '# test examples. -1 means use all.'})
	_A			=  field(default=__lowercase  ,		metadata={'help': 'Source language id for translation.'})
	_A			=  field(default=__lowercase  ,		metadata={'help': 'Target language id for translation.'})
	_A			=  field(default=__lowercase  ,		metadata={'help': '# num_beams to use for evaluation.'})
	_A			=  field(
	    default=__lowercase  ,		metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}  ,		)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Tuple)      ->					Optional[int]:






							'''simple docstring'''





							logger.info(F'***** {split} metrics *****')
							for key in sorted(metrics.keys()):
														logger.info(F'  {key} = {metrics[key]}')
							save_json(_lowerCamelCase		,						os.path.join(_lowerCamelCase		,						F'{split}_results.json'))




def 			_SCREAMING_SNAKE_CASE      (	)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      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.
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
							else:
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      parser.parse_args_into_dataclasses()

							check_output_dir(_lowerCamelCase)

							# 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"		,						_lowerCamelCase)

							# 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.

							__UpperCamelCase			:     List[str]         =      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		,						)

							__UpperCamelCase			:     List[str]         =      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
							for p in extra_model_params:
														if getattr(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase):
																					assert hasattr(_lowerCamelCase		,						_lowerCamelCase), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
																					setattr(_lowerCamelCase		,						_lowerCamelCase		,						getattr(_lowerCamelCase		,						_lowerCamelCase))

							__UpperCamelCase			:     Dict         =      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		,						)
							__UpperCamelCase			:     List[str]         =      AutoModelForSeqaSeqLM.from_pretrained(
							    model_args.model_name_or_path		,						from_tf=".ckpt" in model_args.model_name_or_path		,						config=_lowerCamelCase		,						cache_dir=model_args.cache_dir		,						)

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

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

							# set decoder_start_token_id for MBart
							if model.config.decoder_start_token_id is None and isinstance(_lowerCamelCase		,						(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(_lowerCamelCase		,						_lowerCamelCase):
																					__UpperCamelCase			:     Tuple         =      tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					__UpperCamelCase			:     Optional[Any]         =      tokenizer.convert_tokens_to_ids(data_args.tgt_lang)

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

							__UpperCamelCase			:     Union[str, Any]         =      SeqaSeqDataset

							# Get datasets
							__UpperCamelCase			:     Any         =      (
							    dataset_class(
							        _lowerCamelCase		,						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
							)
							__UpperCamelCase			:     List[str]         =      (
							    dataset_class(
							        _lowerCamelCase		,						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
							)
							__UpperCamelCase			:     Optional[int]         =      (
							    dataset_class(
							        _lowerCamelCase		,						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
							__UpperCamelCase			:     Any         =      (
							    build_compute_metrics_fn(data_args.task		,						_lowerCamelCase) if training_args.predict_with_generate else None
							)
							__UpperCamelCase			:     Tuple         =      SeqaSeqTrainer(
							    model=_lowerCamelCase		,						args=_lowerCamelCase		,						data_args=_lowerCamelCase		,						train_dataset=_lowerCamelCase		,						eval_dataset=_lowerCamelCase		,						data_collator=SeqaSeqDataCollator(
							        _lowerCamelCase		,						_lowerCamelCase		,						model.config.decoder_start_token_id		,						training_args.tpu_num_cores)		,						compute_metrics=_lowerCamelCase		,						tokenizer=_lowerCamelCase		,						)

							__UpperCamelCase			:     Dict         =      {}
							# Training
							if training_args.do_train:
														logger.info("*** Train ***")

														__UpperCamelCase			:     Optional[int]         =      trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
														__UpperCamelCase			:     Optional[Any]         =      train_result.metrics
														__UpperCamelCase			:     List[str]         =      data_args.n_train

														trainer.save_model()  # this also saves the tokenizer

														if trainer.is_world_process_zero():
																					handle_metrics("train"		,						_lowerCamelCase		,						training_args.output_dir)
																					all_metrics.update(_lowerCamelCase)

																					# 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 ***")

														__UpperCamelCase			:     Optional[int]         =      trainer.evaluate(metric_key_prefix="val")
														__UpperCamelCase			:     Dict         =      data_args.n_val
														__UpperCamelCase			:     List[str]         =      round(metrics["val_loss"]		,						4)

														if trainer.is_world_process_zero():
																					handle_metrics("val"		,						_lowerCamelCase		,						training_args.output_dir)
																					all_metrics.update(_lowerCamelCase)

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

														__UpperCamelCase			:     Any         =      trainer.predict(test_dataset=_lowerCamelCase		,						metric_key_prefix="test")
														__UpperCamelCase			:     int         =      test_output.metrics
														__UpperCamelCase			:     List[Any]         =      data_args.n_test

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

																					if training_args.predict_with_generate:
																												__UpperCamelCase			:     int         =      tokenizer.batch_decode(
																												    test_output.predictions		,						skip_special_tokens=_lowerCamelCase		,						clean_up_tokenization_spaces=_lowerCamelCase)
																												__UpperCamelCase			:     Optional[Any]         =      lmap(str.strip		,						_lowerCamelCase)
																												write_txt_file(_lowerCamelCase		,						os.path.join(training_args.output_dir		,						"test_generations.txt"))

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

							return all_metrics




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							main()


if __name__ == "__main__":
		main()

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:




		class 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      depth_estimator(
								    [
								        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
								        "http://images.cocodataset.org/val2017/000000039769.jpg",
								        # RGBA
								        dataset[0]["file"],
								        # LA
								        dataset[1]["file"],
								        # L
								        dataset[2]["file"],
								    ]		)
								self.assertEqual(
								    [
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								    ]  ,     a  ,     )







	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF"		)
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      hashimage(outputs["depth"]		)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)  ,     29.304		)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)  ,     2.662		)







	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)