


import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       List[str]				    = self.config_class(**self.inputs_dict)
            self.parent.assertTrue(hasattr(__lowerCamelCase ,						"width_multiplier"))







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=1_3 ,						__lowerCamelCase=6_4 ,						__lowerCamelCase=2 ,						__lowerCamelCase=3 ,						__lowerCamelCase="swish" ,						__lowerCamelCase=3 ,						__lowerCamelCase=3_2 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=1_0 ,						__lowerCamelCase=None ,						__lowerCamelCase=0.2_5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						)			->				int:
            _A      :       Optional[Any]				    = parent
            _A      :       List[str]				    = batch_size
            _A      :       Dict				    = image_size
            _A      :       Dict				    = patch_size
            _A      :       Optional[int]				    = num_channels
            _A      :       List[str]				    = make_divisible(5_1_2 * width_multiplier ,						divisor=8)
            _A      :       Any				    = hidden_act
            _A      :       Union[str, Any]				    = conv_kernel_size
            _A      :       Tuple				    = output_stride
            _A      :       List[Any]				    = classifier_dropout_prob
            _A      :       Any				    = use_labels
            _A      :       Union[str, Any]				    = is_training
            _A      :       Union[str, Any]				    = num_labels
            _A      :       str				    = initializer_range
            _A      :       Dict				    = scope
            _A      :       List[str]				    = width_multiplier
            _A      :       str				    = ffn_dropout
            _A      :       List[str]				    = attn_dropout


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[Any]				    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

            _A      :       Union[str, Any]				    = None
            _A      :       List[Any]				    = None
            if self.use_labels:
                   _A      :       List[str]				    = ids_tensor([self.batch_size] ,						self.num_labels)
                   _A      :       List[Any]				    = ids_tensor([self.batch_size, self.image_size, self.image_size] ,						self.num_labels)

            _A      :       Optional[Any]				    = self.get_config()

            return config, pixel_values, labels, pixel_labels


     def  _lowerCamelCase (						self)			->				int:
            return MobileViTVaConfig(
                image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						hidden_act=self.hidden_act ,						conv_kernel_size=self.conv_kernel_size ,						output_stride=self.output_stride ,						classifier_dropout_prob=self.classifier_dropout_prob ,						initializer_range=self.initializer_range ,						width_multiplier=self.width_multiplier ,						ffn_dropout=self.ffn_dropout_prob ,						attn_dropout=self.attn_dropout_prob ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Union[str, Any]				    = MobileViTVaModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       List[str]				    = model(__lowerCamelCase)
            self.parent.assertEqual(
                result.last_hidden_state.shape ,						(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       List[Any]				    = self.num_labels
            _A      :       Union[str, Any]				    = MobileViTVaForImageClassification(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       List[Any]				    = model(__lowerCamelCase ,						labels=__lowerCamelCase)
            self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels))


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       Tuple				    = self.num_labels
            _A      :       Tuple				    = MobileViTVaForSemanticSegmentation(__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            _A      :       str				    = model(__lowerCamelCase)
            self.parent.assertEqual(
                result.logits.shape ,						(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,						)
            _A      :       Optional[int]				    = model(__lowerCamelCase ,						labels=__lowerCamelCase)
            self.parent.assertEqual(
                result.logits.shape ,						(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                ) ,						)


     def  _lowerCamelCase (						self)			->				Tuple:
            _A      :       str				    = self.prepare_config_and_inputs()
            _A    ,			_A    ,			_A    ,			_A      :       List[Any]				    = config_and_inputs
            _A      :       List[str]				    = {"pixel_values": pixel_values}
            return config, inputs_dict







@require_torch
class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= (
         (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
         if is_torch_available()
         else ()
     )

     __SCREAMING_SNAKE_CASE  			= (
         {
             "feature-extraction": MobileViTVaModel,
             "image-classification": MobileViTVaForImageClassification,
             "image-segmentation": MobileViTVaForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False
     __SCREAMING_SNAKE_CASE  			= False


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       int				    = MobileViTVaModelTester(self)
            _A      :       int				    = MobileViTVaConfigTester(self ,						config_class=__lowerCamelCase ,						has_text_modality=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            self.config_tester.run_common_tests()


     @unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
     def  _lowerCamelCase (						self)			->				Dict:
            pass


     @unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
     def  _lowerCamelCase (						self)			->				Tuple:
            pass


     @unittest.skip(reason="MobileViTV2 does not output attentions")
     def  _lowerCamelCase (						self)			->				Dict:
            pass


     @require_torch_multi_gpu
     @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
     def  _lowerCamelCase (						self)			->				int:
            pass


     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            pass


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A    ,			_A      :       Tuple				    = self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A      :       Any				    = model_class(__lowerCamelCase)
                   _A      :       Optional[Any]				    = inspect.signature(model.forward)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   _A      :       Optional[Any]				    = [*signature.parameters.keys()]

                   _A      :       Optional[Any]				    = ["pixel_values"]
                   self.assertListEqual(arg_names[:1] ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Optional[int]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            def check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase):
                   _A      :       str				    = model_class(__lowerCamelCase)
                   model.to(__lowerCamelCase)
                   model.eval()

                   with torch.no_grad():
                          _A      :       int				    = model(**self._prepare_for_class(__lowerCamelCase ,						__lowerCamelCase))

                   _A      :       Any				    = outputs.hidden_states

                   _A      :       str				    = 5
                   self.assertEqual(len(__lowerCamelCase) ,						__lowerCamelCase)

                   # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                   # with the width and height being successively divided by 2.
                   _A      :       List[Any]				    = 2
                   for i in range(len(__lowerCamelCase)):
                          self.assertListEqual(
                              list(hidden_states[i].shape[-2:]) ,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] ,						)
                          divisor *= 2

                   self.assertEqual(self.model_tester.output_stride ,						divisor // 2)

            _A    ,			_A      :       Dict				    = self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A      :       str				    = True
                   check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _A      :       str				    = True

                   check_hidden_states_output(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       int				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       List[str]				    = self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase)


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A      :       Optional[int]				    = MobileViTVaModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)




def 							_UpperCAmelCase				():
       _A      :       List[str]				    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
       return image







@require_torch
@require_vision
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @cached_property
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return (
                MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
                if is_vision_available()
                else None
            )


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       str				    = MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
                __lowerCamelCase)

            _A      :       int				    = self.default_image_processor
            _A      :       Any				    = prepare_img()
            _A      :       Dict				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Union[str, Any]				    = model(**__lowerCamelCase)

            # verify the logits
            _A      :       Dict				    = torch.Size((1, 1_0_0_0))
            self.assertEqual(outputs.logits.shape ,						__lowerCamelCase)

            _A      :       str				    = torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]).to(__lowerCamelCase)

            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						__lowerCamelCase ,						atol=1e-4))


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Union[str, Any]				    = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
            _A      :       List[str]				    = model.to(__lowerCamelCase)

            _A      :       int				    = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

            _A      :       Union[str, Any]				    = prepare_img()
            _A      :       Tuple				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Any				    = model(**__lowerCamelCase)
            _A      :       Tuple				    = outputs.logits

            # verify the logits
            _A      :       str				    = torch.Size((1, 2_1, 3_2, 3_2))
            self.assertEqual(logits.shape ,						__lowerCamelCase)

            _A      :       Any				    = torch.tensor(
                [
                    [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
                    [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
                    [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
                ] ,						device=__lowerCamelCase ,						)

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,						__lowerCamelCase ,						atol=1e-4))


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       str				    = MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
            _A      :       Optional[Any]				    = model.to(__lowerCamelCase)

            _A      :       int				    = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

            _A      :       Dict				    = prepare_img()
            _A      :       int				    = image_processor(images=__lowerCamelCase ,						return_tensors="pt").to(__lowerCamelCase)

            # forward pass
            with torch.no_grad():
                   _A      :       Tuple				    = model(**__lowerCamelCase)

            _A      :       Dict				    = outputs.logits.detach().cpu()

            _A      :       List[Any]				    = image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase ,						target_sizes=[(5_0, 6_0)])
            _A      :       Dict				    = torch.Size((5_0, 6_0))
            self.assertEqual(segmentation[0].shape ,						__lowerCamelCase)

            _A      :       List[Any]				    = image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase)
            _A      :       Optional[Any]				    = torch.Size((3_2, 3_2))
            self.assertEqual(segmentation[0].shape ,						__lowerCamelCase)




"""simple docstring"""
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE :    List[str]									=				getLogger(__name__)






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      str     ,   _snake_case						:      int = 8     ,   _snake_case						:      int = 1_024     ,   _snake_case						:      Any="val"     ,   _snake_case						:      Tuple=None     ,   _snake_case						:      Any=False     ,   _snake_case						:      str="summarization"     ,   _snake_case						:      Dict=None     ,   _snake_case						:      Optional[Any]=1     ,   _snake_case						:      Dict = None     ,   _snake_case						:      List[Any]=""     ,   **_snake_case						:      int     ,   )     ->Dict:
	"""simple docstring"""





	__snake_case	:						int						=				str(_snake_case     )
	assert local_rank is not None
	torch.distributed.init_process_group(backend='''nccl'''     ,   rank=_snake_case     )

	__snake_case	:						Optional[Any]						=				Path(_snake_case     )
	__snake_case	:						str						=				save_dir.joinpath(f"""rank_{local_rank}_output.json"""     )
	torch.cuda.set_device(_snake_case     )
	__snake_case	:						Tuple						=				AutoModelForSeqaSeqLM.from_pretrained(_snake_case     ).cuda()
	if fpaa:
		__snake_case	:						List[str]						=				model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(_snake_case     ,   _snake_case     )  # update config with task specific params
	__snake_case	:						Dict						=				generate_kwargs.pop('''num_beams'''     ,   model.config.num_beams     )  # AttributeError risk?
	if num_return_sequences > num_beams:
		__snake_case	:						Optional[Any]						=				num_return_sequences

	__snake_case	:						Dict						=				AutoTokenizer.from_pretrained(_snake_case     )
	logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

	if max_source_length is None:
		__snake_case	:						List[str]						=				tokenizer.model_max_length
	if prefix is None:
		__snake_case	:						List[str]						=				prefix or getattr(model.config     ,   '''prefix'''     ,   ''''''     ) or ''''''
	__snake_case	:						List[str]						=				SeqaSeqDataset(
	    _snake_case     ,   _snake_case     ,   _snake_case     ,   max_target_length=1_024     ,   type_path=_snake_case     ,   n_obs=_snake_case     ,   prefix=_snake_case     ,   **_snake_case     ,   )
	# I set shuffle=True for a more accurate progress bar.
	# If all the longest samples are first, the prog bar estimate is too high at the beginning.
	__snake_case	:						Union[str, Any]						=				ds.make_sortish_sampler(_snake_case     ,   distributed=_snake_case     ,   add_extra_examples=_snake_case     ,   shuffle=_snake_case     )
	__snake_case	:						List[Any]						=				DataLoader(_snake_case     ,   sampler=_snake_case     ,   batch_size=_snake_case     ,   collate_fn=ds.collate_fn     )
	__snake_case	:						Union[str, Any]						=				[]
	for batch in tqdm(_snake_case     ):
		__snake_case	:						Tuple						=				model.generate(
		    input_ids=batch['''input_ids'''].to(model.device     )     ,   attention_mask=batch['''attention_mask'''].to(model.device     )     ,   num_return_sequences=_snake_case     ,   num_beams=_snake_case     ,   **_snake_case     ,   )
		__snake_case	:						List[Any]						=				tokenizer.batch_decode(_snake_case     ,   skip_special_tokens=_snake_case     ,   clean_up_tokenization_spaces=_snake_case     )
		__snake_case	:						List[str]						=				batch['''ids''']
		if num_return_sequences > 1:
			__snake_case	:						Dict						=				chunks(_snake_case     ,   _snake_case     )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_snake_case     ):
			results.append({'''pred''': pred, '''id''': ids[i].item()}     )
	save_json(_snake_case     ,   _snake_case     )
	return results, sampler.num_replicas






def 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						Any						=				argparse.ArgumentParser(
	    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''     )
	parser.add_argument('''--data_dir'''     ,   type=_snake_case     ,   help='''like cnn_dm/test.source'''     )
	parser.add_argument(
	    '''--model_name'''     ,   type=_snake_case     ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''     ,   default='''sshleifer/distilbart-xsum-12-3'''     ,   )
	parser.add_argument('''--save_dir'''     ,   type=_snake_case     ,   help='''where to save'''     ,   default='''tmp_gen'''     )
	parser.add_argument('''--max_source_length'''     ,   type=_snake_case     ,   default=_snake_case     )
	parser.add_argument(
	    '''--type_path'''     ,   type=_snake_case     ,   default='''test'''     ,   help='''which subset to evaluate typically train/val/test'''     )
	parser.add_argument('''--task'''     ,   type=_snake_case     ,   default='''summarization'''     ,   help='''used for task_specific_params + metrics'''     )
	parser.add_argument('''--bs'''     ,   type=_snake_case     ,   default=8     ,   required=_snake_case     ,   help='''batch size'''     )
	parser.add_argument(
	    '''--local_rank'''     ,   type=_snake_case     ,   default=-1     ,   required=_snake_case     ,   help='''should be passed by distributed.launch'''     )

	parser.add_argument(
	    '''--n_obs'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     ,   help='''How many observations. Defaults to all.'''     )
	parser.add_argument(
	    '''--num_return_sequences'''     ,   type=_snake_case     ,   default=1     ,   required=_snake_case     ,   help='''How many sequences to return'''     )
	parser.add_argument(
	    '''--sync_timeout'''     ,   type=_snake_case     ,   default=600     ,   required=_snake_case     ,   help='''How long should master process wait for other processes to finish.'''     ,   )
	parser.add_argument('''--src_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument('''--tgt_lang'''     ,   type=_snake_case     ,   default=_snake_case     ,   required=_snake_case     )
	parser.add_argument(
	    '''--prefix'''     ,   type=_snake_case     ,   required=_snake_case     ,   default=_snake_case     ,   help='''will be added to the begininng of src examples'''     )
	parser.add_argument('''--fp16'''     ,   action='''store_true'''     )
	parser.add_argument('''--debug'''     ,   action='''store_true'''     )
	__snake_case	:						str						=				time.time()
	__snake_case	,							__snake_case	:						Any						=				parser.parse_known_args()
	__snake_case	:						List[Any]						=				parse_numeric_n_bool_cl_kwargs(_snake_case     )
	if generate_kwargs and args.local_rank <= 0:
		print(f"""parsed the following generate kwargs: {generate_kwargs}"""     )
	__snake_case	:						List[Any]						=				Path(args.save_dir + '''_tmp'''     )
	Path(_snake_case     ).mkdir(exist_ok=_snake_case     )  # this handles locking.
	__snake_case	:						Optional[int]						=				list(json_save_dir.glob('''rank_*.json'''     )     )
	if intermediate_files:
		raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""     )
		# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
	__snake_case	:						Dict						=				{}
	if args.src_lang is not None:
		__snake_case	:						Dict						=				args.src_lang
	if args.tgt_lang is not None:
		__snake_case	:						Dict						=				args.tgt_lang

	Path(args.save_dir     ).mkdir(exist_ok=_snake_case     )
	__snake_case	,							__snake_case	:						List[Any]						=				eval_data_dir(
	    args.data_dir     ,   _snake_case     ,   args.model_name     ,   type_path=args.type_path     ,   bs=args.bs     ,   fpaa=args.fpaa     ,   task=args.task     ,   local_rank=args.local_rank     ,   n_obs=args.n_obs     ,   max_source_length=args.max_source_length     ,   num_return_sequences=args.num_return_sequences     ,   prefix=args.prefix     ,   dataset_kwargs=_snake_case     ,   **_snake_case     ,   )

	if args.local_rank <= 0:
		__snake_case	:						int						=				Path(args.save_dir     )
		save_dir.mkdir(exist_ok=_snake_case     )
		__snake_case	:						Optional[Any]						=				gather_results_from_each_node(_snake_case     ,   _snake_case     ,   args.sync_timeout     )
		__snake_case	:						str						=				combine_partial_results(_snake_case     )
		if args.num_return_sequences > 1:
			__snake_case	:						List[Any]						=				save_dir.joinpath('''pseudolabel_results.json'''     )
			print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""     )
			save_json(_snake_case     ,   _snake_case     )
			return
		__snake_case	:						Tuple						=				Path(args.data_dir     ).joinpath(args.type_path + '''.target'''     )
		with open(_snake_case     ) as f:
			__snake_case	:						Optional[Any]						=				[x.rstrip() for x in f.readlines()][: len(_snake_case     )]

		# Calculate metrics, save metrics,  and save _generations.txt
		__snake_case	:						List[str]						=				'''translation''' in args.task
		__snake_case	:						List[Any]						=				calculate_bleu if calc_bleu else calculate_rouge
		__snake_case	:						Dict						=				'''bleu''' if calc_bleu else '''rouge'''
		__snake_case	:						Dict						=				score_fn(_snake_case     ,   _snake_case     )
		__snake_case	:						int						=				len(_snake_case     )
		__snake_case	:						Dict						=				time.time() - start_time
		__snake_case	:						Optional[Any]						=				round(runtime / metrics['''n_obs''']     ,   4     )
		__snake_case	:						List[Any]						=				num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		__snake_case	:						int						=				save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""     )
		save_json(_snake_case     ,   _snake_case     ,   indent=_snake_case     )
		print(_snake_case     )
		write_txt_file(_snake_case     ,   save_dir.joinpath(f"""{args.type_path}_generations.txt"""     )     )
		if args.debug:
			write_txt_file(_snake_case     ,   save_dir.joinpath(f"""{args.type_path}.target"""     )     )
		else:
			shutil.rmtree(_snake_case     )






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->List:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				[]
	for partial_result in partial_results:
		records.extend(_snake_case     )
	__snake_case	:						List[str]						=				sorted(_snake_case     ,   key=lambda _snake_case     : x["id"]     )
	__snake_case	:						Tuple						=				[x['''pred'''] for x in records]
	return preds






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[Any]     )     ->List[Dict[str, List]]:
	"""simple docstring"""





	__snake_case	:						List[str]						=				time.time()
	logger.info('''waiting for all nodes to finish'''     )
	__snake_case	:						List[str]						=				None
	while (time.time() - start_wait) < timeout:
		__snake_case	:						Any						=				list(save_dir.glob('''rank_*.json'''     )     )
		if len(_snake_case     ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			__snake_case	:						Tuple						=				lmap(_snake_case     ,   _snake_case     )
			return json_data
		except JSONDecodeError:
			continue
	else:
		raise TimeoutError('''Rank 0 gave up on waiting for other processes'''     )
	# Unreachable


if __name__ == "__main__":
			# Usage for MT:
			run_generate()





