





import collections
import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
   from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class 			_UpperCAmelCase  :



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_=1_3 ,				snake_case_=3_2 ,				snake_case_=2 ,				snake_case_=3 ,				snake_case_=1_6 ,				snake_case_=[1, 2, 1] ,				snake_case_=[2, 2, 4] ,				snake_case_=2 ,				snake_case_=2.0 ,				snake_case_=True ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.1 ,				snake_case_="gelu" ,				snake_case_=False ,				snake_case_=True ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=1_0 ,				snake_case_=8 ,				snake_case_=["stage1", "stage2"] ,				snake_case_=[1, 2] ,				):

        """simple docstring"""

        A_  :	Tuple    						=  parent
        A_  :	int    						=  batch_size
        A_  :	str    						=  image_size
        A_  :	Optional[int]    						=  patch_size
        A_  :	Optional[Any]    						=  num_channels
        A_  :	List[str]    						=  embed_dim
        A_  :	Dict    						=  depths
        A_  :	Dict    						=  num_heads
        A_  :	Any    						=  window_size
        A_  :	Tuple    						=  mlp_ratio
        A_  :	Tuple    						=  qkv_bias
        A_  :	List[str]    						=  hidden_dropout_prob
        A_  :	Any    						=  attention_probs_dropout_prob
        A_  :	Union[str, Any]    						=  drop_path_rate
        A_  :	Dict    						=  hidden_act
        A_  :	Optional[int]    						=  use_absolute_embeddings
        A_  :	List[str]    						=  patch_norm
        A_  :	Union[str, Any]    						=  layer_norm_eps
        A_  :	Tuple    						=  initializer_range
        A_  :	List[Any]    						=  is_training
        A_  :	Union[str, Any]    						=  scope
        A_  :	Union[str, Any]    						=  use_labels
        A_  :	int    						=  type_sequence_label_size
        A_  :	List[Any]    						=  encoder_stride
        A_  :	Dict    						=  out_features
        A_  :	List[str]    						=  out_indices

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        A_  :	Any    						=  None
        if self.use_labels:
          A_  :	Dict    						=  ids_tensor([self.batch_size] ,				self.type_sequence_label_size  )

        A_  :	Dict    						=  self.get_config()

        return config, pixel_values, labels

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return SwinConfig(
            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 ,				encoder_stride=self.encoder_stride ,				out_features=self.out_features ,				out_indices=self.out_indices ,				)

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[Any]    						=  SwinModel(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :	Tuple    						=  model(snake_case_  )

        A_  :	int    						=  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
        A_  :	List[str]    						=  int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

        self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, expected_seq_len, expected_dim)  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Tuple    						=  SwinBackbone(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :	Optional[Any]    						=  model(snake_case_  )

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps  ) ,				len(config.out_features  )  )
        self.parent.assertListEqual(list(result.feature_maps[0].shape  ) ,				[self.batch_size, model.channels[0], 1_6, 1_6]  )

        # verify channels
        self.parent.assertEqual(len(model.channels  ) ,				len(config.out_features  )  )

        # verify backbone works with out_features=None
        A_  :	Optional[int]    						=  None
        A_  :	Union[str, Any]    						=  SwinBackbone(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :	Dict    						=  model(snake_case_  )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps  ) ,				1  )
        self.parent.assertListEqual(list(result.feature_maps[0].shape  ) ,				[self.batch_size, model.channels[-1], 4, 4]  )

        # verify channels
        self.parent.assertEqual(len(model.channels  ) ,				1  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Tuple    						=  SwinForMaskedImageModeling(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :	List[str]    						=  model(snake_case_  )
        self.parent.assertEqual(
            result.logits.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

        # test greyscale images
        A_  :	Union[str, Any]    						=  1
        A_  :	Optional[Any]    						=  SwinForMaskedImageModeling(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :	str    						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        A_  :	Any    						=  model(snake_case_  )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, 1, self.image_size, self.image_size)  )

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[str]    						=  self.type_sequence_label_size
        A_  :	List[Any]    						=  SwinForImageClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :	List[str]    						=  model(snake_case_ ,				labels=snake_case_  )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)  )

        # test greyscale images
        A_  :	Optional[Any]    						=  1
        A_  :	Optional[int]    						=  SwinForImageClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :	Dict    						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        A_  :	Dict    						=  model(snake_case_  )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  self.prepare_config_and_inputs()
        (
            A_	
        )  :	List[str]    						=  config_and_inputs
        A_  :	Optional[int]    						=  {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class 			_UpperCAmelCase  (       UpperCAmelCase__      ,       UpperCAmelCase__      ,       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Any        =       (
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      lowercase_				:			Optional[Any]        =       (
          {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      lowercase_				:			Optional[int]        =       True

      lowercase_				:			List[Any]        =       False
      lowercase_				:			Union[str, Any]        =       False
      lowercase_				:			str        =       False

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  SwinModelTester(self  )
        A_  :	Optional[Any]    						=  ConfigTester(self ,				config_class=snake_case_ ,				embed_dim=3_7  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self.create_and_test_config_common_properties()
        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  ):

        """simple docstring"""

        return

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_  )

      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'  )
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        super().test_training_gradient_checkpointing()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[str]    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case_  )

      @unittest.skip(reason='Swin does not use inputs_embeds'  )
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      @unittest.skip(reason='Swin Transformer does not use feedforward chunking'  )
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        pass

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :	Tuple    						=  model_class(snake_case_  )
          self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)  )
          A_  :	List[Any]    						=  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(snake_case_ ,				nn.Linear  )  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :	Any    						=  model_class(snake_case_  )
          A_  :	Union[str, Any]    						=  inspect.signature(model.forward  )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          A_  :	Optional[int]    						=  [*signature.parameters.keys()]

          A_  :	Optional[Any]    						=  ['pixel_values']
          self.assertListEqual(arg_names[:1] ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  self.model_tester.prepare_config_and_inputs_for_common()
        A_  :	str    						=  True

        for model_class in self.all_model_classes:
          A_  :	Union[str, Any]    						=  True
          A_  :	List[str]    						=  False
          A_  :	List[str]    						=  True
          A_  :	Optional[Any]    						=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          with torch.no_grad():
            A_  :	Tuple    						=  model(**self._prepare_for_class(snake_case_ ,				snake_case_  )  )
          A_  :	str    						=  outputs.attentions
          A_  :	Union[str, Any]    						=  len(self.model_tester.depths  )
          self.assertEqual(len(snake_case_  ) ,				snake_case_  )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          A_  :	Any    						=  True
          A_  :	Union[str, Any]    						=  config.window_size**2
          A_  :	List[str]    						=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          with torch.no_grad():
            A_  :	Optional[Any]    						=  model(**self._prepare_for_class(snake_case_ ,				snake_case_  )  )
          A_  :	int    						=  outputs.attentions
          self.assertEqual(len(snake_case_  ) ,				snake_case_  )

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

          # Check attention is always last and order is fine
          A_  :	Optional[Any]    						=  True
          A_  :	Optional[Any]    						=  True
          A_  :	Optional[Any]    						=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          with torch.no_grad():
            A_  :	Optional[Any]    						=  model(**self._prepare_for_class(snake_case_ ,				snake_case_  )  )

          # also another +1 for reshaped_hidden_states
          A_  :	List[str]    						=  1 if model_class.__name__ == 'SwinBackbone' else 2
          self.assertEqual(out_len + added_hidden_states ,				len(snake_case_  )  )

          A_  :	int    						=  outputs.attentions

          self.assertEqual(len(snake_case_  ) ,				snake_case_  )

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[Any]    						=  model_class(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        with torch.no_grad():
          A_  :	Union[str, Any]    						=  model(**self._prepare_for_class(snake_case_ ,				snake_case_  )  )

        A_  :	str    						=  outputs.hidden_states

        A_  :	int    						=  getattr(
            self.model_tester ,				'expected_num_hidden_layers' ,				len(self.model_tester.depths  ) + 1  )
        self.assertEqual(len(snake_case_  ) ,				snake_case_  )

        # Swin has a different seq_length
        A_  :	Optional[Any]    						=  (
            config.patch_size
            if isinstance(config.patch_size ,				collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        A_  :	Union[str, Any]    						=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]  ) ,				[num_patches, self.model_tester.embed_dim] ,				)

        if not model_class.__name__ == "SwinBackbone":
          A_  :	Any    						=  outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case_  ) ,				snake_case_  )

          A_  :	Dict    						=  reshaped_hidden_states[0].shape
          A_  :	Optional[int]    						=  (
              reshaped_hidden_states[0].view(snake_case_ ,				snake_case_ ,				height * width  ).permute(0 ,				2 ,				1  )
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]  ) ,				[num_patches, self.model_tester.embed_dim] ,				)

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  self.model_tester.prepare_config_and_inputs_for_common()

        A_  :	Dict    						=  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size ,				collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
          A_  :	int    						=  True
          self.check_hidden_states_output(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          A_  :	Union[str, Any]    						=  True

          self.check_hidden_states_output(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.model_tester.prepare_config_and_inputs_for_common()
        A_  :	str    						=  3

        A_  :	Tuple    						=  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size ,				collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        A_  :	str    						=  (
            config.patch_size
            if isinstance(config.patch_size ,				collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        A_  :	Optional[Any]    						=  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        A_  :	List[Any]    						=  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
          A_  :	Union[str, Any]    						=  True
          self.check_hidden_states_output(snake_case_ ,				snake_case_ ,				snake_case_ ,				(padded_height, padded_width)  )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          A_  :	str    						=  True
          self.check_hidden_states_output(snake_case_ ,				snake_case_ ,				snake_case_ ,				(padded_height, padded_width)  )

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_  :	Any    						=  SwinModel.from_pretrained(snake_case_  )
          self.assertIsNotNone(snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	int    						=  self.model_tester.prepare_config_and_inputs_for_common()

        A_  :	Optional[int]    						=  _config_zero_init(snake_case_  )
        for model_class in self.all_model_classes:
          A_  :	List[str]    						=  model_class(config=snake_case_  )
          for name, param in model.named_parameters():
            if "embeddings" not in name and 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""" ,				)


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



      '''simple docstring'''

      @cached_property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return (
            AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'  )
            if is_vision_available()
            else None
        )

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[str]    						=  SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'  ).to(snake_case_  )
        A_  :	int    						=  self.default_image_processor

        A_  :	Optional[int]    						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
        A_  :	Tuple    						=  image_processor(images=snake_case_ ,				return_tensors='pt'  ).to(snake_case_  )

        # forward pass
        with torch.no_grad():
          A_  :	List[Any]    						=  model(**snake_case_  )

        # verify the logits
        A_  :	List[str]    						=  torch.Size((1, 1_0_0_0)  )
        self.assertEqual(outputs.logits.shape ,				snake_case_  )
        A_  :	int    						=  torch.tensor([-0.09_48, -0.64_54, -0.09_21]  ).to(snake_case_  )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				snake_case_ ,				atol=1E-4  )  )


@require_torch
class 			_UpperCAmelCase  (       unittest.TestCase      ,       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Union[str, Any]        =       (SwinBackbone,) if is_torch_available() else ()
      lowercase_				:			Union[str, Any]        =       SwinConfig

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  SwinModelTester(self  )






"""simple docstring"""




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


lowerCamelCase_					:						List[str]      = getLogger(__name__)


lowerCamelCase_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

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

  A_  :	Optional[int]    						=  time.time()
  # update config with task specific params
  use_task_specific_params(_UpperCAmelCase			, _UpperCAmelCase )
  if prefix is None:
    A_  :	Optional[int]    						=  prefix or getattr(model.config			, 'prefix'			, '' ) or ''
  for examples_chunk in tqdm(list(chunks(_UpperCAmelCase			, _UpperCAmelCase ) ) ):
    A_  :	Any    						=  [prefix + text for text in examples_chunk]
    A_  :	List[Any]    						=  tokenizer(_UpperCAmelCase			, return_tensors='pt'			, truncation=_UpperCAmelCase			, padding='longest' ).to(_UpperCAmelCase )
    A_  :	Dict    						=  model.generate(
        input_ids=batch.input_ids			, attention_mask=batch.attention_mask			, **_UpperCAmelCase			, )
    A_  :	Dict    						=  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()
  A_  :	Dict    						=  int(time.time() - start_time )  # seconds
  A_  :	Any    						=  len(_UpperCAmelCase )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			, 4 )}





def 						UpperCAmelCase__		(					):






  """simple docstring"""





  return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )





def 						UpperCAmelCase__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  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
  A_						,	A_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  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:
    A_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  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.""" )
  A_  :	Optional[Any]    						=  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
  A_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	Any    						=  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)