



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 			_snake_case     :




				def __init__(  self   :  int,   __lowercase   :  Any,   __lowercase   :  Dict=3,   __lowercase   :  Tuple=32,   __lowercase   :  Tuple=3,   __lowercase   :  str=10,   __lowercase   :  Any=[10, 20, 30, 40],   __lowercase   :  List[Any]=[1, 1, 2, 1],   __lowercase   :  List[str]=True,   __lowercase   :  str=True,   __lowercase   :  Dict="relu",   __lowercase   :  Union[str, Any]=3,   __lowercase   :  str=None,   ):
							lowercase__   					=			parent
							lowercase__   					=			batch_size
							lowercase__   					=			image_size
							lowercase__   					=			num_channels
							lowercase__   					=			embeddings_size
							lowercase__   					=			hidden_sizes
							lowercase__   					=			depths
							lowercase__   					=			is_training
							lowercase__   					=			use_labels
							lowercase__   					=			hidden_act
							lowercase__   					=			num_labels
							lowercase__   					=			scope
							lowercase__   					=			len(__lowercase  )




				def 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

							lowercase__   					=			self.get_config()
							return config, pixel_values, labels




				def 							A__ (  self   :  str  ):
							return RegNetConfig(
							    num_channels=self.num_channels,   embeddings_size=self.embeddings_size,   hidden_sizes=self.hidden_sizes,   depths=self.depths,   hidden_act=self.hidden_act,   num_labels=self.num_labels,   )




				def 							A__ (  self   :  List[str],   __lowercase   :  int,   __lowercase   :  List[Any],   __lowercase   :  int  ):
							lowercase__   					=			TFRegNetModel(config=__lowercase  )
							lowercase__   					=			model(__lowercase,   training=__lowercase  )
							# 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 // 32, self.image_size // 32),   )




				def 							A__ (  self   :  str,   __lowercase   :  List[Any],   __lowercase   :  List[Any],   __lowercase   :  Optional[Any]  ):
							lowercase__   					=			self.num_labels
							lowercase__   					=			TFRegNetForImageClassification(__lowercase  )
							lowercase__   					=			model(__lowercase,   labels=__lowercase,   training=__lowercase  )
							self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)  )




				def 							A__ (  self   :  Optional[int]  ):
							lowercase__   					=			self.prepare_config_and_inputs()
							lowercase__					,    lowercase__					,    lowercase__   					=			config_and_inputs
							lowercase__   					=			{"pixel_values": pixel_values}
							return config, inputs_dict
@require_tf
class 			_snake_case     (   lowercase__      ,		lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   int       						=(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
				UpperCamelCase__			:   List[Any]       						=(
				    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				UpperCamelCase__			:   Tuple       						=False
				UpperCamelCase__			:   Optional[int]       						=False
				UpperCamelCase__			:   List[Any]       						=False
				UpperCamelCase__			:   List[Any]       						=False
				UpperCamelCase__			:   Dict       						=False




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			TFRegNetModelTester(self  )
							lowercase__   					=			ConfigTester(self,   config_class=__lowercase,   has_text_modality=__lowercase  )




				def 							A__ (  self   :  List[str]  ):
							return




				@unittest.skip(reason="RegNet does not use inputs_embeds"  )
				def 							A__ (  self   :  Tuple  ):
							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 							A__ (  self   :  Any  ):
							super().test_keras_fit()




				@unittest.skip(reason="RegNet does not support input and output embeddings"  )
				def 							A__ (  self   :  int  ):
							pass




				def 							A__ (  self   :  int  ):
							lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase__   					=			["pixel_values"]
										self.assertListEqual(arg_names[:1],   __lowercase  )




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase  )




				def 							A__ (  self   :  Dict  ):
							def check_hidden_states_output(__lowercase   :  str,   __lowercase   :  Any,   __lowercase   :  Optional[int]  ):
										lowercase__   					=			model_class(__lowercase  )
										lowercase__   					=			model(**self._prepare_for_class(__lowercase,   __lowercase  ),   training=__lowercase  )

										lowercase__   					=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowercase__   					=			self.model_tester.num_stages
										self.assertEqual(len(__lowercase  ),   expected_num_stages + 1  )

										# RegNet'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 // 2, self.model_tester.image_size // 2],   )

							lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__   					=			["basic", "bottleneck"]
							for model_class in self.all_model_classes:
										for layer_type in layers_type:
													lowercase__   					=			layer_type
													lowercase__   					=			True
													check_hidden_states_output(__lowercase,   __lowercase,   __lowercase  )

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

													check_hidden_states_output(__lowercase,   __lowercase,   __lowercase  )




				def 							A__ (  self   :  List[Any]  ):
							lowercase__					,    lowercase__   					=			self.model_tester.prepare_config_and_inputs_for_common()

							def check_equivalence(__lowercase   :  Dict,   __lowercase   :  List[str],   __lowercase   :  Optional[int],   __lowercase   :  List[str]={}  ):
										lowercase__   					=			model(__lowercase,   return_dict=__lowercase,   **__lowercase  )
										lowercase__   					=			model(__lowercase,   return_dict=__lowercase,   **__lowercase  ).to_tuple()

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

													recursive_check(__lowercase,   __lowercase  )

							for model_class in self.all_model_classes:
										lowercase__   					=			model_class(__lowercase  )

										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase  )
										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase  )
										check_equivalence(__lowercase,   __lowercase,   __lowercase  )

										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase,   return_labels=__lowercase  )
										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase,   return_labels=__lowercase  )
										check_equivalence(__lowercase,   __lowercase,   __lowercase  )

										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase  )
										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase  )
										check_equivalence(__lowercase,   __lowercase,   __lowercase,   {"output_hidden_states": True}  )

										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase,   return_labels=__lowercase  )
										lowercase__   					=			self._prepare_for_class(__lowercase,   __lowercase,   return_labels=__lowercase  )
										check_equivalence(__lowercase,   __lowercase,   __lowercase,   {"output_hidden_states": True}  )




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase  )




				@slow
				def 							A__ (  self   :  List[Any]  ):
							for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__   					=			TFRegNetModel.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )



def __lowerCAmelCase	(       ):
			lowercase__   					=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
			return image




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




				@cached_property
				def 							A__ (  self   :  Union[str, Any]  ):
							return (
							    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
							    if is_vision_available()
							    else None
							)




				@slow
				def 							A__ (  self   :  List[Any]  ):
							lowercase__   					=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

							lowercase__   					=			self.default_image_processor
							lowercase__   					=			prepare_img()
							lowercase__   					=			image_processor(images=__lowercase,   return_tensors="tf"  )

							# forward pass
							lowercase__   					=			model(**__lowercase,   training=__lowercase  )

							# verify the logits
							lowercase__   					=			tf.TensorShape((1, 1000)  )
							self.assertEqual(outputs.logits.shape,   __lowercase  )

							lowercase__   					=			tf.constant([-0.4180, -1.5051, -3.4836]  )

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




import fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			calculate_rouge(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					)
			if save_path is not None:
						save_json(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)
			return metrics  # these print nicely


if __name__ == "__main__":
							fire.Fire(calculate_rouge_path)




