code: 
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import itertools
  import operator
  import sys
  from collections import OrderedDict
  
  from run_eval import datetime_now, run_generate
  
  from utils import ROUGE_KEYS
  
  
  # A table of supported tasks and the list of scores in the order of importance to be sorted by.
  # To add a new task, simply list the score names that `run_eval.run_generate()` returns
  A           =   {
      "translation": ["bleu"],
      "summarization": ROUGE_KEYS,
  }
  def   snake_case_       (							_a     :							Union[str, Any]			):
        snake_case_						       =							search.split()
        snake_case_						       =							dict((g.split("="			) for g in groups)			)
        snake_case_						       =							list(entries.keys()			)
        snake_case_						       =							[[F'''--{k} {v}''' for v in vs.split(":"			)] for k, vs in entries.items()]
        snake_case_						       =							[list(_a			) for x in itertools.product(*_a			)]
        return matrix, entry_names
  def   snake_case_       (							):
        snake_case_						       =							sys.argv[0]
  
        snake_case_						       =							argparse.ArgumentParser(
            usage=(
                "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
                " refer to `run_eval.py -h` for the complete list."
            )			)
        parser.add_argument(
            "--search" ,					type=_a ,					required=_a ,					help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\"" ,					)
        parser.add_argument(
            "--bs" ,					type=_a ,					default=8 ,					required=_a ,					help="initial batch size (may get reduced if it's too big)"			)
        parser.add_argument("--task" ,					type=_a ,					help="used for task_specific_params + metrics"			)
        parser.add_argument(
            "--info" ,					nargs="?" ,					type=_a ,					const=datetime_now() ,					help=(
                "add custom notes to be printed before the results table. If no value is passed, the current datetime"
                " string will be used."
            ) ,					)
        snake_case_       ,	snake_case_						       =							parser.parse_known_args()
        # we share some of the args
        args_main.extend(["--task", args.task]			)
        snake_case_						       =							[prog] + args_main
  
        # to support variations like translation_en_to_de"
        snake_case_						       =							"translation" if "translation" in args.task else "summarization"
  
        snake_case_       ,	snake_case_						       =							parse_search_arg(args.search			)
        snake_case_						       =							task_score_names[task]  # score cols first
        snake_case_						       =							{col: len(str(_a			)			) for col in col_names}
        snake_case_						       =							[]
        for r in matrix:
              snake_case_						       =							dict((x.replace("--" ,					""			).split() for x in r)			)
              snake_case_						       =							" ".join(_a			).split()
              args_exp.extend(["--bs", str(args.bs			)]			)  # in case we need to reduce its size due to CUDA OOM
              snake_case_						       =							args_normal + args_exp
  
              # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
              snake_case_						       =							run_generate(verbose=_a			)
              # make sure scores are first in the table
              snake_case_						       =							OrderedDict()
              for score in task_score_names[task]:
                    snake_case_						       =							scores[score]
              result.update(_a			)
              results.append(_a			)
  
              # find widest entries
              for k, v in result.items():
                    snake_case_						       =							len(str(_a			)			)
                    if l > col_widths[k]:
                          snake_case_						       =							l
  
        snake_case_						       =							sorted(_a ,					key=operator.itemgetter(*task_score_names[task]			) ,					reverse=_a			)
        print(" | ".join([F'''{col:{col_widths[col]}}''' for col in col_names]			)			)
        print(" | ".join([F'''{'-'*col_widths[col]}''' for col in col_names]			)			)
        for row in results_sorted:
              print(" | ".join([F'''{row[col]:{col_widths[col]}}''' for col in col_names]			)			)
  
        snake_case_						       =							results_sorted[0]
        for score in task_score_names[task]:
              del best[score]
        snake_case_						       =							[F'''--{k} {v}''' for k, v in best.items()]
        snake_case_						       =							["--bs", str(args.bs			)]
        if args.info:
              print(F'''\nInfo: {args.info}'''			)
        print("\nBest score args:"			)
        print(" ".join(args_main + best_args + dyn_args			)			)
  
        return results_sorted
  
  
  if __name__ == "__main__":
       # Usage:
       # [normal-run_eval_search.py cmd plus] \
       # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
       #
       # Example:
       # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
       # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
       # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
       # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
       run_search()
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
       import tensorflow as tf
  
       from transformers.models.swin.modeling_tf_swin import (
           TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
           TFSwinModel,
       )
  
  
  if is_vision_available():
       from PIL import Image
  
       from transformers import AutoImageProcessor
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Any=13	, lowerCAmelCase_  : int=32	, lowerCAmelCase_  : int=2	, lowerCAmelCase_  : List[str]=3	, lowerCAmelCase_  : Any=16	, lowerCAmelCase_  : Tuple=[1, 2, 1]	, lowerCAmelCase_  : List[str]=[2, 2, 4]	, lowerCAmelCase_  : Dict=2	, lowerCAmelCase_  : str=2.0	, lowerCAmelCase_  : int=True	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.1	, lowerCAmelCase_  : Optional[int]="gelu"	, lowerCAmelCase_  : Dict=False	, lowerCAmelCase_  : str=True	, lowerCAmelCase_  : Optional[int]=0.02	, lowerCAmelCase_  : List[str]=1E-5	, lowerCAmelCase_  : Dict=True	, lowerCAmelCase_  : Tuple=None	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : str=10	, lowerCAmelCase_  : Tuple=8	, )   ->       None:
          snake_case_						       =							parent
          snake_case_						       =							batch_size
          snake_case_						       =							image_size
          snake_case_						       =							patch_size
          snake_case_						       =							num_channels
          snake_case_						       =							embed_dim
          snake_case_						       =							depths
          snake_case_						       =							num_heads
          snake_case_						       =							window_size
          snake_case_						       =							mlp_ratio
          snake_case_						       =							qkv_bias
          snake_case_						       =							hidden_dropout_prob
          snake_case_						       =							attention_probs_dropout_prob
          snake_case_						       =							drop_path_rate
          snake_case_						       =							hidden_act
          snake_case_						       =							use_absolute_embeddings
          snake_case_						       =							patch_norm
          snake_case_						       =							layer_norm_eps
          snake_case_						       =							initializer_range
          snake_case_						       =							is_training
          snake_case_						       =							scope
          snake_case_						       =							use_labels
          snake_case_						       =							type_sequence_label_size
          snake_case_						       =							encoder_stride
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Dict:
          snake_case_						       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
  
          snake_case_						       =							None
          if self.use_labels:
                snake_case_						       =							ids_tensor([self.batch_size]	, self.type_sequence_label_size    )
  
          snake_case_						       =							self.get_config()
  
          return config, pixel_values, labels
  
    def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
          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	, )
  
    def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Optional[int]    )   ->       Any:
          snake_case_						       =							TFSwinModel(config=lowerCAmelCase_    )
          snake_case_						       =							model(lowerCAmelCase_    )
  
          snake_case_						       =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
          snake_case_						       =							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  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : Optional[int]    )   ->       Optional[int]:
          snake_case_						       =							TFSwinForMaskedImageModeling(config=lowerCAmelCase_    )
          snake_case_						       =							model(lowerCAmelCase_    )
          self.parent.assertEqual(
              result.logits.shape	, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )
  
          # test greyscale images
          snake_case_						       =							1
          snake_case_						       =							TFSwinForMaskedImageModeling(lowerCAmelCase_    )
  
          snake_case_						       =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          snake_case_						       =							model(lowerCAmelCase_    )
          self.parent.assertEqual(result.logits.shape	, (self.batch_size, 1, self.image_size, self.image_size)    )
  
    def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[Any]    )   ->       Tuple:
          snake_case_						       =							self.type_sequence_label_size
          snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
          snake_case_						       =							model(lowerCAmelCase_	, labels=lowerCAmelCase_    )
          self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )
  
          # test greyscale images
          snake_case_						       =							1
          snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
          snake_case_						       =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          snake_case_						       =							model(lowerCAmelCase_    )
          self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )
  
  
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       str:
          snake_case_						       =							self.prepare_config_and_inputs()
          snake_case_       ,	snake_case_       ,	snake_case_						       =							config_and_inputs
          snake_case_						       =							{"pixel_values": pixel_values}
          return config, inputs_dict
  
  
  
  @require_tf
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= (
        (
            TFSwinModel,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    _UpperCamelCase						= (
        {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
        if is_tf_available()
        else {}
    )
  
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
  
    def 		_lowerCamelCase	(self  : Any    )   ->       List[Any]:
          snake_case_						       =							TFSwinModelTester(self    )
          snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, embed_dim=37    )
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Optional[Any]:
          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  : List[Any]    )   ->       List[str]:
          return
  
    def 		_lowerCamelCase	(self  : Any    )   ->       Dict:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : int    )   ->       Any:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Tuple    )   ->       Optional[Any]:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_    )
  
    @unittest.skip(reason="Swin does not use inputs_embeds"    )
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Union[str, Any]:
          pass
  
    def 		_lowerCamelCase	(self  : List[str]    )   ->       Any:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(lowerCAmelCase_    )
                self.assertIsInstance(model.get_input_embeddings()	, tf.keras.layers.Layer    )
                snake_case_						       =							model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCAmelCase_	, tf.keras.layers.Dense    )    )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Dict:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							inspect.signature(model.call    )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case_						       =							[*signature.parameters.keys()]
  
                snake_case_						       =							["pixel_values"]
                self.assertListEqual(arg_names[:1]	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Any:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
          snake_case_						       =							True
  
          for model_class in self.all_model_classes:
                snake_case_						       =							True
                snake_case_						       =							False
                snake_case_						       =							True
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
                snake_case_						       =							outputs.attentions
                snake_case_						       =							len(self.model_tester.depths    )
                self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                snake_case_						       =							True
                snake_case_						       =							config.window_size**2
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
                snake_case_						       =							outputs.attentions
                self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
                self.assertListEqual(
                    list(attentions[0].shape[-3:]    )	, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	, )
                snake_case_						       =							len(lowerCAmelCase_    )
  
                # Check attention is always last and order is fine
                snake_case_						       =							True
                snake_case_						       =							True
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
  
                if hasattr(self.model_tester	, "num_hidden_states_types"    ):
                      snake_case_						       =							self.model_tester.num_hidden_states_types
                else:
                      # also another +1 for reshaped_hidden_states
                      snake_case_						       =							2
                self.assertEqual(out_len + added_hidden_states	, len(lowerCAmelCase_    )    )
  
                snake_case_						       =							outputs.attentions
  
                self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]    )	, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	, )
  
    def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : List[Any]    )   ->       Optional[int]:
          snake_case_						       =							model_class(lowerCAmelCase_    )
          snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
          snake_case_						       =							outputs.hidden_states
  
          snake_case_						       =							getattr(
              self.model_tester	, "expected_num_hidden_layers"	, len(self.model_tester.depths    ) + 1    )
          self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
          # Swin has a different seq_length
          snake_case_						       =							to_atuple(config.patch_size    )
  
          snake_case_						       =							(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]	, )
  
          snake_case_						       =							outputs.reshaped_hidden_states
          self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
          snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_						       =							reshaped_hidden_states[0].shape
  
          snake_case_						       =							tf.reshape(reshaped_hidden_states[0]	, (batch_size, num_channels, height * width)    )
          snake_case_						       =							tf.transpose(lowerCAmelCase_	, (0, 2, 1)    )
  
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]    )	, [num_patches, self.model_tester.embed_dim]	, )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       str:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          snake_case_						       =							to_atuple(self.model_tester.image_size    )
  
          for model_class in self.all_model_classes:
                snake_case_						       =							True
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case_						       =							True
  
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Tuple    )   ->       List[Any]:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
          snake_case_						       =							3
  
          snake_case_						       =							to_atuple(self.model_tester.image_size    )
          snake_case_						       =							to_atuple(config.patch_size    )
  
          snake_case_						       =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          snake_case_						       =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
          for model_class in self.all_model_classes:
                snake_case_						       =							True
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, (padded_height, padded_width)    )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case_						       =							True
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, (padded_height, padded_width)    )
  
  
    @slow
    def 		_lowerCamelCase	(self  : Any    )   ->       List[str]:
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case_						       =							TFSwinModel.from_pretrained(lowerCAmelCase_    )
                self.assertIsNotNone(lowerCAmelCase_    )
  
  
  
  @require_vision
  @require_tf
  class  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @cached_property
    def 		_lowerCamelCase	(self  : Dict    )   ->       str:
          return (
              AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
              if is_vision_available()
              else None
          )
  
  
    @slow
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
          snake_case_						       =							TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
          snake_case_						       =							self.default_image_processor
  
          snake_case_						       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
          snake_case_						       =							image_processor(images=lowerCAmelCase_	, return_tensors="tf"    )
  
          # forward pass
          snake_case_						       =							model(lowerCAmelCase_    )
  
          # verify the logits
          snake_case_						       =							tf.TensorShape((1, 10_00)    )
          self.assertEqual(outputs.logits.shape	, lowerCAmelCase_    )
          snake_case_						       =							tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
          self.assertTrue(np.allclose(outputs.logits[0, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
  