

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
  from PIL import Image

  from transformers import ConvNextImageProcessor





class A__     :



 """simple docstring"""







 def __init__(		self			:						Optional[int]  ,    A_			:						Any  ,    A_			:						List[str]=1_3  ,    A_			:						Any=3_2  ,    A_			:						Any=3  ,    A_			:						int=4  ,    A_			:						Optional[Any]=[1_0, 2_0, 3_0, 4_0]  ,    A_			:						Union[str, Any]=[2, 2, 3, 2]  ,    A_			:						int=True  ,    A_			:						List[str]=True  ,    A_			:						Any=3_7  ,    A_			:						Optional[Any]="gelu"  ,    A_			:						Dict=1_0  ,    A_			:						Union[str, Any]=0.02  ,    A_			:						Tuple=3  ,    A_			:						str=None  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     parent
      _lowerCAmelCase :       Any	   =     batch_size
      _lowerCAmelCase :       Dict	   =     image_size
      _lowerCAmelCase :       Optional[int]	   =     num_channels
      _lowerCAmelCase :       Dict	   =     num_stages
      _lowerCAmelCase :       str	   =     hidden_sizes
      _lowerCAmelCase :       str	   =     depths
      _lowerCAmelCase :       int	   =     is_training
      _lowerCAmelCase :       str	   =     use_labels
      _lowerCAmelCase :       Optional[Any]	   =     intermediate_size
      _lowerCAmelCase :       Any	   =     hidden_act
      _lowerCAmelCase :       List[str]	   =     type_sequence_label_size
      _lowerCAmelCase :       str	   =     initializer_range
      _lowerCAmelCase :       Union[str, Any]	   =     scope

 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




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

      _lowerCAmelCase :       str	   =     None
      if self.use_labels:
           _lowerCAmelCase :       int	   =     ids_tensor([self.batch_size]  ,    self.type_sequence_label_size							)

      _lowerCAmelCase :       List[Any]	   =     self.get_config()

      return config, pixel_values, labels

 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      return ConvNextConfig(
          num_channels=self.num_channels  ,    hidden_sizes=self.hidden_sizes  ,    depths=self.depths  ,    num_stages=self.num_stages  ,    hidden_act=self.hidden_act  ,    is_decoder=A_  ,    initializer_range=self.initializer_range  ,    )

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						List[Any]  ,    A_			:						str  ,    A_			:						Optional[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     TFConvNextModel(config=A_							)
      _lowerCAmelCase :       Optional[Any]	   =     model(A_  ,    training=A_							)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape  ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)  ,    )

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						Optional[int]  ,    A_			:						List[Any]  ,    A_			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     self.type_sequence_label_size
      _lowerCAmelCase :       List[Any]	   =     TFConvNextForImageClassification(A_							)
      _lowerCAmelCase :       int	   =     model(A_  ,    labels=A_  ,    training=A_							)
      self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)							)


 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     self.prepare_config_and_inputs()
      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase :       Dict	   =     config_and_inputs
      _lowerCAmelCase :       List[str]	   =     {"pixel_values": pixel_values}
      return config, inputs_dict





@require_tf
class A__     (			A	,		A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Any  			=    (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
 _lowercase  :       int  			=    (
     {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
     if is_tf_available()
     else {}
 )

 _lowercase  :       List[Any]  			=    False
 _lowercase  :       Dict  			=    False
 _lowercase  :       Optional[Any]  			=    False
 _lowercase  :       Optional[Any]  			=    False
 _lowercase  :       Dict  			=    False

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     TFConvNextModelTester(self							)
      _lowerCAmelCase :       Any	   =     ConfigTester(
          self  ,    config_class=A_  ,    has_text_modality=A_  ,    hidden_size=3_7  ,    )

 @unittest.skip(reason="ConvNext does not use inputs_embeds"							)
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      pass

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"							)							) == 0  ,    reason="TF does not support backprop for grouped convolutions on CPU."  ,    )
 @slow
 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      super().test_keras_fit()

 @unittest.skip(reason="ConvNext does not support input and output embeddings"							)
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Union[str, Any]	   =     self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowerCAmelCase :       int	   =     ["pixel_values"]
           self.assertListEqual(arg_names[:1]  ,    A_							)

 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




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

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"							)							) == 0  ,    reason="TF does not support backprop for grouped convolutions on CPU."  ,    )
 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      super().test_dataset_conversion()

 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      def check_hidden_states_output(A_			:						str  ,    A_			:						Tuple  ,    A_			:						List[Any]							):
           _lowerCAmelCase :       Dict	   =     model_class(A_							)

           _lowerCAmelCase :       Dict	   =     model(**self._prepare_for_class(A_  ,    A_							)							)
           _lowerCAmelCase :       Optional[Any]	   =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowerCAmelCase :       List[str]	   =     self.model_tester.num_stages
           self.assertEqual(len(A_							)  ,    expected_num_stages + 1							)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)  ,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,    )

      _lowerCAmelCase   ,      _lowerCAmelCase :       Union[str, Any]	   =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

           check_hidden_states_output(A_  ,    A_  ,    A_							)

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






      '''simple docstring'''




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

      def check_equivalence(A_			:						Tuple  ,    A_			:						str  ,    A_			:						int  ,    A_			:						Any={}							):
           _lowerCAmelCase :       Any	   =     model(A_  ,    return_dict=A_  ,    **A_							)
           _lowerCAmelCase :       Tuple	   =     model(A_  ,    return_dict=A_  ,    **A_							).to_tuple()

           def recursive_check(A_			:						int  ,    A_			:						Any							):
                if isinstance(A_  ,    (List, Tuple)							):
                     for tuple_iterable_value, dict_iterable_value in zip(A_  ,    A_							):
                          recursive_check(A_  ,    A_							)
                elif tuple_object is None:
                     return
                else:
                     self.assertTrue(
                         all(tf.equal(A_  ,    A_							)							)  ,    msg=(
                             "Tuple and dict output are not equal. Difference:"
                             F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}'''
                         )  ,    )

                recursive_check(A_  ,    A_							)

      for model_class in self.all_model_classes:
           _lowerCAmelCase :       Dict	   =     model_class(A_							)

           _lowerCAmelCase :       Dict	   =     self._prepare_for_class(A_  ,    A_							)
           _lowerCAmelCase :       int	   =     self._prepare_for_class(A_  ,    A_							)
           check_equivalence(A_  ,    A_  ,    A_							)

           _lowerCAmelCase :       List[str]	   =     self._prepare_for_class(A_  ,    A_  ,    return_labels=A_							)
           _lowerCAmelCase :       List[Any]	   =     self._prepare_for_class(A_  ,    A_  ,    return_labels=A_							)
           check_equivalence(A_  ,    A_  ,    A_							)

           _lowerCAmelCase :       Tuple	   =     self._prepare_for_class(A_  ,    A_							)
           _lowerCAmelCase :       int	   =     self._prepare_for_class(A_  ,    A_							)
           check_equivalence(A_  ,    A_  ,    A_  ,    {"output_hidden_states": True}							)

           _lowerCAmelCase :       Optional[Any]	   =     self._prepare_for_class(A_  ,    A_  ,    return_labels=A_							)
           _lowerCAmelCase :       Tuple	   =     self._prepare_for_class(A_  ,    A_  ,    return_labels=A_							)
           check_equivalence(A_  ,    A_  ,    A_  ,    {"output_hidden_states": True}							)

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A_							)


 @slow
 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"							)
      self.assertIsNotNone(A_							)







def 						_snake_case		(     )							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Union[str, Any]	   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
     return image





@require_tf
@require_vision
class A__     (			unittest.TestCase			):



 """simple docstring"""







 @cached_property
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"							) if is_vision_available() else None


 @slow
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"							)

      _lowerCAmelCase :       Optional[Any]	   =     self.default_image_processor
      _lowerCAmelCase :       Dict	   =     prepare_img()
      _lowerCAmelCase :       int	   =     image_processor(images=A_  ,    return_tensors="tf"							)

      # forward pass
      _lowerCAmelCase :       int	   =     model(**A_							)

      # verify the logits
      _lowerCAmelCase :       Dict	   =     tf.TensorShape((1, 1_0_0_0)							)
      self.assertEqual(outputs.logits.shape  ,    A_							)

      _lowerCAmelCase :       List[Any]	   =     tf.constant([-0.0260, -0.4739, 0.1911]							)

      tf.debugging.assert_near(outputs.logits[0, :3]  ,    A_  ,    atol=1E-4							)









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
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     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=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""		,				)
     parser.add_argument(
         "--bs"		,				type=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_SNAKE_CASE		,				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."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     parse_search_arg(args.search					)
     _lowerCAmelCase :       Optional[Any]	   =     task_score_names[task]  # score cols first
     _lowerCAmelCase :       int	   =     {col: len(str(SCREAMING_SNAKE_CASE					)					) for col in col_names}
     _lowerCAmelCase :       Optional[int]	   =     []
     for r in matrix:
          _lowerCAmelCase :       List[str]	   =     dict((x.replace("--"		,				""					).split() for x in r)					)
          _lowerCAmelCase :       Any	   =     " ".join(SCREAMING_SNAKE_CASE					).split()
          args_exp.extend(["--bs", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
          _lowerCAmelCase :       List[str]	   =     args_normal + args_exp

          # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

          # find widest entries
          for k, v in result.items():
               _lowerCAmelCase :       Any	   =     len(str(SCREAMING_SNAKE_CASE					)					)
               if l > col_widths[k]:
                    _lowerCAmelCase :       str	   =     l

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_SNAKE_CASE					)
     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]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--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()






