



import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     self.config_class(**self.inputs_dict       )
								self.parent.assertTrue(hasattr(_snake_case					,       'tf_padding'       )       )
								self.parent.assertTrue(hasattr(_snake_case					,       'depth_multiplier'       )       )







class   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							Optional[Any]					,       _snake_case       :							Optional[int]					,       _snake_case       :							Tuple=13					,       _snake_case       :							int=3					,       _snake_case       :							Optional[Any]=32					,       _snake_case       :							List[str]=0.25					,       _snake_case       :							Optional[int]=8					,       _snake_case       :							List[Any]=8					,       _snake_case       :							Dict=6					,       _snake_case       :							List[str]=32					,       _snake_case       :							Tuple=True					,       _snake_case       :							Union[str, Any]=True					,       _snake_case       :							Any=True					,       _snake_case       :							List[str]="relu6"					,       _snake_case       :							Optional[int]=12_80					,       _snake_case       :							List[Any]=0.1					,       _snake_case       :							Optional[int]=0.02					,       _snake_case       :							List[Any]=True					,       _snake_case       :							List[Any]=True					,       _snake_case       :							List[str]=10					,       _snake_case       :							str=None					,       ):






								"""simple docstring"""


								A__	  =     parent
								A__	  =     batch_size
								A__	  =     num_channels
								A__	  =     image_size
								A__	  =     depth_multiplier
								A__	  =     depth_divisible_by
								A__	  =     min_depth
								A__	  =     expand_ratio
								A__	  =     tf_padding
								A__	  =     output_stride
								A__	  =     first_layer_is_expansion
								A__	  =     finegrained_output
								A__	  =     hidden_act
								A__	  =     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier       )
								A__	  =     classifier_dropout_prob
								A__	  =     use_labels
								A__	  =     is_training
								A__	  =     num_labels
								A__	  =     initializer_range
								A__	  =     scope



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


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

								A__	  =     None
								A__	  =     None
								if self.use_labels:
											A__	  =     ids_tensor([self.batch_size]					,       self.num_labels       )
											A__	  =     ids_tensor([self.batch_size, self.image_size, self.image_size]					,       self.num_labels       )

								A__	  =     self.get_config()

								return config, pixel_values, labels, pixel_labels



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								return MobileNetVaConfig(
								    num_channels=self.num_channels					,       image_size=self.image_size					,       depth_multiplier=self.depth_multiplier					,       depth_divisible_by=self.depth_divisible_by					,       min_depth=self.min_depth					,       expand_ratio=self.expand_ratio					,       output_stride=self.output_stride					,       first_layer_is_expansion=self.first_layer_is_expansion					,       finegrained_output=self.finegrained_output					,       hidden_act=self.hidden_act					,       tf_padding=self.tf_padding					,       classifier_dropout_prob=self.classifier_dropout_prob					,       initializer_range=self.initializer_range					,       )



					def      _a				(  self       :							Tuple					,       _snake_case       :							List[Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Optional[int]					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     MobileNetVaModel(config=_snake_case       )
								model.to(_snake_case       )
								model.eval()
								A__	  =     model(_snake_case       )
								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,
								    )					,       )
								self.parent.assertEqual(
								    result.pooler_output.shape					,       (self.batch_size, self.last_hidden_size)					,       )



					def      _a				(  self       :							List[str]					,       _snake_case       :							Dict					,       _snake_case       :							Optional[Any]					,       _snake_case       :							Optional[Any]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.num_labels
								A__	  =     MobileNetVaForImageClassification(_snake_case       )
								model.to(_snake_case       )
								model.eval()
								A__	  =     model(_snake_case					,       labels=_snake_case       )
								self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)       )



					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							List[str]					,       _snake_case       :							Optional[int]					,       _snake_case       :							Tuple					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     self.num_labels
								A__	  =     MobileNetVaForSemanticSegmentation(_snake_case       )
								model.to(_snake_case       )
								model.eval()
								A__	  =     model(_snake_case       )
								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__	  =     model(_snake_case					,       labels=_snake_case       )
								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      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     self.prepare_config_and_inputs()
								A__  ,			A__  ,			A__  ,			A__	  =     config_and_inputs
								A__	  =     {'pixel_values': pixel_values}
								return config, inputs_dict







@require_torch
class   __lowerCAmelCase  (		UpperCAmelCase_				,  UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Optional[Any]      							=     (
					    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					A__					:		str      							=     (
					    {
					        "feature-extraction": MobileNetVaModel,
					        "image-classification": MobileNetVaForImageClassification,
					        "image-segmentation": MobileNetVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

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



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     MobileNetVaModelTester(self       )
								A__	  =     MobileNetVaConfigTester(self					,       config_class=_snake_case					,       has_text_modality=_snake_case       )



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'       )
					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'       )
					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								pass



					@unittest.skip(reason='MobileNetV2 does not output attentions'       )
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								pass



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__	  =     model_class(_snake_case       )
											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]					,       _snake_case       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case       )



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case       :							Any					,       _snake_case       :							List[str]					,       _snake_case       :							Any       ):
											A__	  =     model_class(_snake_case       )
											model.to(_snake_case       )
											model.eval()

											with torch.no_grad():
														A__	  =     model(**self._prepare_for_class(_snake_case					,       _snake_case       )       )

											A__	  =     outputs.hidden_states

											A__	  =     16
											self.assertEqual(len(_snake_case       )					,       _snake_case       )

								A__  ,			A__	  =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__	  =     True
											check_hidden_states_output(_snake_case					,       _snake_case					,       _snake_case       )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											A__	  =     True

											check_hidden_states_output(_snake_case					,       _snake_case					,       _snake_case       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case       )



					@slow
					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__	  =     MobileNetVaModel.from_pretrained(_snake_case       )
											self.assertIsNotNone(_snake_case       )







def A    (							)    ->   Tuple:
			A__	  =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
			return image






@require_torch
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					@cached_property
					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								return (
								    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'       ) if is_vision_available() else None
								)



					@slow
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'       ).to(_snake_case       )

								A__	  =     self.default_image_processor
								A__	  =     prepare_img()
								A__	  =     image_processor(images=_snake_case					,       return_tensors='pt'       ).to(_snake_case       )

								# forward pass
								with torch.no_grad():
											A__	  =     model(**_snake_case       )

								# verify the logits
								A__	  =     torch.Size((1, 10_01)       )
								self.assertEqual(outputs.logits.shape					,       _snake_case       )

								A__	  =     torch.tensor([0.2445, -1.1993, 0.1905]       ).to(_snake_case       )

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



					@slow
					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'       )
								A__	  =     model.to(_snake_case       )

								A__	  =     MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'       )

								A__	  =     prepare_img()
								A__	  =     image_processor(images=_snake_case					,       return_tensors='pt'       ).to(_snake_case       )

								# forward pass
								with torch.no_grad():
											A__	  =     model(**_snake_case       )
								A__	  =     outputs.logits

								# verify the logits
								A__	  =     torch.Size((1, 21, 65, 65)       )
								self.assertEqual(logits.shape					,       _snake_case       )

								A__	  =     torch.tensor(
								    [
								        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
								        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
								        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
								    ]					,       device=_snake_case					,       )

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








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


SCREAMING_SNAKE_CASE							    =							getLogger(__name__)


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




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

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

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




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




def  a   (lowerCAmelCase__=True   ):
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""model_name"""		,				type=lowerCAmelCase__		,				help="""like facebook/bart-large-cnn,t5-base, etc."""   )
   parser.add_argument("""input_path"""		,				type=lowerCAmelCase__		,				help="""like cnn_dm/test.source"""   )
   parser.add_argument("""save_path"""		,				type=lowerCAmelCase__		,				help="""where to save summaries"""   )
   parser.add_argument("""--reference_path"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				help="""like cnn_dm/test.target"""   )
   parser.add_argument("""--score_path"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default="""metrics.json"""		,				help="""where to save metrics"""   )
   parser.add_argument("""--device"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default=lowerCAmelCase__		,				help="""cuda, cuda:1, cpu etc."""   )
   parser.add_argument(
       """--prefix"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default=lowerCAmelCase__		,				help="""will be added to the begininng of src examples"""   )
   parser.add_argument("""--task"""		,				type=lowerCAmelCase__		,				default="""summarization"""		,				help="""used for task_specific_params + metrics"""   )
   parser.add_argument("""--bs"""		,				type=lowerCAmelCase__		,				default=8		,				required=lowerCAmelCase__		,				help="""batch size"""   )
   parser.add_argument(
       """--n_obs"""		,				type=lowerCAmelCase__		,				default=-1		,				required=lowerCAmelCase__		,				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=lowerCAmelCase__		,				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					=  parser.parse_known_args()
   __a					=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__   )
   if parsed_args and verbose:
      print(f'''parsed the following generate kwargs: {parsed_args}'''   )
   __a					=  [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
   if args.n_obs > 0:
      __a					=  examples[: args.n_obs]
   Path(args.save_path   ).parent.mkdir(exist_ok=lowerCAmelCase__   )

   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.'''   )

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError("""Can't mix --fp16 and --device cpu"""   )

   __a					=  generate_summaries_or_translations(
       lowerCAmelCase__		,				args.save_path		,				args.model_name		,				batch_size=args.bs		,				device=args.device		,				fpaa=args.fpaa		,				task=args.task		,				prefix=args.prefix		,				**lowerCAmelCase__		,				)

   if args.reference_path is None:
      return {}

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

   if args.dump_args:
      scores.update(lowerCAmelCase__   )
   if args.info:
      __a					=  args.info

   if verbose:
      print(lowerCAmelCase__   )

   if args.score_path is not None:
      json.dump(lowerCAmelCase__		,				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)



