





import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_UpperCAmelCase										=					getLogger(__name__)


_UpperCAmelCase										=					'cuda' if torch.cuda.is_available() else 'cpu'




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_ = 8       ,		UpperCamelCase_ = DEFAULT_DEVICE       ,		UpperCamelCase_=False       ,		UpperCamelCase_="summarization"       ,		UpperCamelCase_=None       ,		**UpperCamelCase_       ,		)  ->		Dict:
       UpperCamelCase_ 							=	Path(UpperCamelCase_	).open("w"       ,		encoding="utf-8"	)
       UpperCamelCase_ 							=	str(UpperCamelCase_	)
       UpperCamelCase_ 							=	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_	).to(UpperCamelCase_	)
       if fpaa:
              UpperCamelCase_ 							=	model.half()

       UpperCamelCase_ 							=	AutoTokenizer.from_pretrained(UpperCamelCase_	)
       logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.

       UpperCamelCase_ 							=	time.time()
       # update config with task specific params
       use_task_specific_params(UpperCamelCase_       ,		UpperCamelCase_	)
       if prefix is None:
              UpperCamelCase_ 							=	prefix or getattr(model.config       ,		"prefix"       ,		""	) or ""
       for examples_chunk in tqdm(list(chunks(UpperCamelCase_       ,		UpperCamelCase_	)	)	):
              UpperCamelCase_ 							=	[prefix + text for text in examples_chunk]
              UpperCamelCase_ 							=	tokenizer(UpperCamelCase_       ,		return_tensors="pt"       ,		truncation=UpperCamelCase_       ,		padding="longest"	).to(UpperCamelCase_	)
              UpperCamelCase_ 							=	model.generate(
                  input_ids=batch.input_ids       ,		attention_mask=batch.attention_mask       ,		**UpperCamelCase_       ,		)
              UpperCamelCase_ 							=	tokenizer.batch_decode(UpperCamelCase_       ,		skip_special_tokens=UpperCamelCase_       ,		clean_up_tokenization_spaces=UpperCamelCase_	)
              for hypothesis in dec:
                     fout.write(hypothesis + "\n"	)
                     fout.flush()
       fout.close()
       UpperCamelCase_ 							=	int(time.time() - start_time	)  # seconds
       UpperCamelCase_ 							=	len(UpperCamelCase_	)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs       ,		4	)}




def 			lowerCAmelCase_			(				)  ->		List[Any]:
       return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"	)




def 			lowerCAmelCase_			(				UpperCamelCase_=True	)  ->		str:
       UpperCamelCase_ 							=	argparse.ArgumentParser()
       parser.add_argument("model_name"       ,		type=UpperCamelCase_       ,		help="like facebook/bart-large-cnn,t5-base, etc."	)
       parser.add_argument("input_path"       ,		type=UpperCamelCase_       ,		help="like cnn_dm/test.source"	)
       parser.add_argument("save_path"       ,		type=UpperCamelCase_       ,		help="where to save summaries"	)
       parser.add_argument("--reference_path"       ,		type=UpperCamelCase_       ,		required=UpperCamelCase_       ,		help="like cnn_dm/test.target"	)
       parser.add_argument("--score_path"       ,		type=UpperCamelCase_       ,		required=UpperCamelCase_       ,		default="metrics.json"       ,		help="where to save metrics"	)
       parser.add_argument("--device"       ,		type=UpperCamelCase_       ,		required=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="cuda, cuda:1, cpu etc."	)
       parser.add_argument(
           "--prefix"       ,		type=UpperCamelCase_       ,		required=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="will be added to the begininng of src examples"	)
       parser.add_argument("--task"       ,		type=UpperCamelCase_       ,		default="summarization"       ,		help="used for task_specific_params + metrics"	)
       parser.add_argument("--bs"       ,		type=UpperCamelCase_       ,		default=8       ,		required=UpperCamelCase_       ,		help="batch size"	)
       parser.add_argument(
           "--n_obs"       ,		type=UpperCamelCase_       ,		default=-1       ,		required=UpperCamelCase_       ,		help="How many observations. Defaults to all."	)
       parser.add_argument("--fp16"       ,		action="store_true"	)
       parser.add_argument("--dump-args"       ,		action="store_true"       ,		help="print the custom hparams with the results"	)
       parser.add_argument(
           "--info"       ,		nargs="?"       ,		type=UpperCamelCase_       ,		const=datetime_now()       ,		help=(
               "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
               " lang=en-ru. If no value is passed, the current datetime string will be used."
           )       ,		)
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       UpperCamelCase_							,			UpperCamelCase_ 							=	parser.parse_known_args()
       UpperCamelCase_ 							=	parse_numeric_n_bool_cl_kwargs(UpperCamelCase_	)
       if parsed_args and verbose:
              print(F'''parsed the following generate kwargs: {parsed_args}'''	)
       with open(args.input_path	) as f:
              UpperCamelCase_ 							=	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              UpperCamelCase_ 							=	examples[: args.n_obs]
       Path(args.save_path	).parent.mkdir(exist_ok=UpperCamelCase_	)
       if args.reference_path is None and Path(args.score_path	).exists():
              warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''	)
       UpperCamelCase_ 							=	generate_summaries_or_translations(
           UpperCamelCase_       ,		args.save_path       ,		args.model_name       ,		batch_size=args.bs       ,		device=args.device       ,		fpaa=args.fpaa       ,		task=args.task       ,		prefix=args.prefix       ,		**UpperCamelCase_       ,		)

       if args.reference_path is None:
              return {}

       # Compute scores
       UpperCamelCase_ 							=	calculate_bleu if "translation" in args.task else calculate_rouge
       UpperCamelCase_ 							=	[x.rstrip() for x in open(args.save_path	).readlines()]
       UpperCamelCase_ 							=	[x.rstrip() for x in open(args.reference_path	).readlines()][: len(UpperCamelCase_	)]
       UpperCamelCase_ 							=	score_fn(UpperCamelCase_       ,		UpperCamelCase_	)
       scores.update(UpperCamelCase_	)

       if args.dump_args:
              scores.update(UpperCamelCase_	)
       if args.info:
              UpperCamelCase_ 							=	args.info

       if verbose:
              print(UpperCamelCase_	)

       if args.score_path is not None:
              json.dump(UpperCamelCase_       ,		open(args.score_path       ,		"w"	)	)

       return scores


if __name__ == "__main__":
     # Usage for MT:
     # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
     run_generate(verbose=True)









"""simple docstring"""




import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
     from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor




class  lowercase_     :






      def __init__(     self  ,  a_  ,  a_=1_3  ,  a_=3_2  ,  a_=1  ,  a_=3  ,  a_=1_6  ,  a_=[1, 2, 1]  ,  a_=[2, 2, 4]  ,  a_=2  ,  a_=2.0  ,  a_=True  ,  a_=0.0  ,  a_=0.0  ,  a_=0.1  ,  a_="gelu"  ,  a_=False  ,  a_=True  ,  a_=0.02  ,  a_=1E-5  ,  a_=True  ,  a_=None  ,  a_=False  ,  a_=2  ,  )						->str:







        '''simple docstring'''


        _a					      =						parent
        _a					      =						batch_size
        _a					      =						image_size
        _a					      =						patch_size
        _a					      =						num_channels
        _a					      =						embed_dim
        _a					      =						depths
        _a					      =						num_heads
        _a					      =						window_size
        _a					      =						mlp_ratio
        _a					      =						qkv_bias
        _a					      =						hidden_dropout_prob
        _a					      =						attention_probs_dropout_prob
        _a					      =						drop_path_rate
        _a					      =						hidden_act
        _a					      =						use_absolute_embeddings
        _a					      =						patch_norm
        _a					      =						layer_norm_eps
        _a					      =						initializer_range
        _a					      =						is_training
        _a					      =						scope
        _a					      =						use_labels
        _a					      =						upscale

        # here we set some attributes to make tests pass
        _a					      =						len(a_						)
        _a					      =						embed_dim
        _a					      =						(image_size // patch_size) ** 2






      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        _a					      =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        _a					      =						None
        if self.use_labels:
          _a					      =						ids_tensor([self.batch_size]  ,  self.type_sequence_label_size						)

        _a					      =						self.get_config()

        return config, pixel_values, labels






      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        return SwinaSRConfig(
            image_size=self.image_size  ,  patch_size=self.patch_size  ,  num_channels=self.num_channels  ,  embed_dim=self.embed_dim  ,  depths=self.depths  ,  num_heads=self.num_heads  ,  window_size=self.window_size  ,  mlp_ratio=self.mlp_ratio  ,  qkv_bias=self.qkv_bias  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,  drop_path_rate=self.drop_path_rate  ,  hidden_act=self.hidden_act  ,  use_absolute_embeddings=self.use_absolute_embeddings  ,  path_norm=self.patch_norm  ,  layer_norm_eps=self.layer_norm_eps  ,  initializer_range=self.initializer_range  ,  upscale=self.upscale  ,  )






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->Union[str, Any]:







        '''simple docstring'''


        _a					      =						SwinaSRModel(config=a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        self.parent.assertEqual(
            result.last_hidden_state.shape  ,  (self.batch_size, self.embed_dim, self.image_size, self.image_size)						)






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						SwinaSRForImageSuperResolution(a_						)
        model.to(a_						)
        model.eval()
        _a					      =						model(a_						)

        _a					      =						self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape  ,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)						)






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						self.prepare_config_and_inputs()
        _a      ,						_a      ,						_a					      =						config_and_inputs
        _a					      =						{"pixel_values": pixel_values}
        return config, inputs_dict







@require_torch
class  lowercase_     (_UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase  ):
      A__						:		str							      =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      A__						:		Optional[Any]							      =					{'''feature-extraction''': SwinaSRModel} if is_torch_available() else {}

      A__						:		List[str]							      =					False
      A__						:		Optional[Any]							      =					False
      A__						:		str							      =					False
      A__						:		Optional[int]							      =					False
      A__						:		List[Any]							      =					False






      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        _a					      =						SwinaSRModelTester(self						)
        _a					      =						ConfigTester(self  ,  config_class=a_  ,  embed_dim=3_7						)






      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a_						)






      def       lowerCamelCase__     (     self						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_super_resolution(*a_						)






      @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."						)
      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not use inputs_embeds"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Union[str, Any]:







        '''simple docstring'''


        pass






      @unittest.skip(reason="Swin2SR does not support training yet"						)
      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        pass






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _a					      =						model_class(a_						)
          self.assertIsInstance(model.get_input_embeddings()  ,  (nn.Module)						)
          _a					      =						model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(a_  ,  nn.Linear						)						)






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

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

          _a					      =						["pixel_values"]
          self.assertListEqual(arg_names[:1]  ,  a_						)






      @slow
      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a					      =						SwinaSRModel.from_pretrained(a_						)
          self.assertIsNotNone(a_						)






      def       lowerCamelCase__     (     self						)						->Dict:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()

        _a					      =						_config_zero_init(a_						)
        for model_class in self.all_model_classes:
          _a					      =						model_class(config=a_						)
          for name, param in model.named_parameters():
            if "logit_scale" in name:
              continue
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()  ,  [0.0, 1.0]  ,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,  )






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a      ,						_a					      =						self.model_tester.prepare_config_and_inputs_for_common()
        _a					      =						True

        for model_class in self.all_model_classes:
          _a					      =						True
          _a					      =						False
          _a					      =						True
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)
          _a					      =						outputs.attentions
          _a					      =						len(self.model_tester.depths						)
          self.assertEqual(len(a_						)  ,  a_						)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a					      =						True
          _a					      =						config.window_size**2
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)
          _a					      =						outputs.attentions
          self.assertEqual(len(a_						)  ,  a_						)

          self.assertListEqual(
              list(attentions[0].shape[-3:]						)  ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,  )
          _a					      =						len(a_						)

          # Check attention is always last and order is fine
          _a					      =						True
          _a					      =						True
          _a					      =						model_class(a_						)
          model.to(a_						)
          model.eval()
          with torch.no_grad():
            _a					      =						model(**self._prepare_for_class(a_  ,  a_						)						)

          self.assertEqual(out_len + 1  ,  len(a_						)						)

          _a					      =						outputs.attentions

          self.assertEqual(len(a_						)  ,  a_						)

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]						)  ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,  )







@require_vision
@require_torch
@slow
class  lowercase_     (unittest.TestCase  ):






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a					      =						SwinaSRImageProcessor()
        _a					      =						SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"						).to(a_						)

        _a					      =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
        _a					      =						processor(images=a_  ,  return_tensors="pt"						).to(a_						)

        # forward pass
        with torch.no_grad():
          _a					      =						model(**a_						)

        # verify the logits
        _a					      =						torch.Size([1, 3, 9_7_6, 1_2_9_6]						)
        self.assertEqual(outputs.reconstruction.shape  ,  a_						)
        _a					      =						torch.tensor(
            [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]						).to(a_						)
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,  a_  ,  atol=1E-4						)						)


