



"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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 numpy as np
	import tensorflow as tf

	from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
	from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor

class       UpperCamelCase  (					a				):





					"""simple docstring"""






					def 						A__  (					self :							Optional[Any] ):
										A__				=   self.config_class(**self.inputs_dict )
										self.parent.assertTrue(hasattr(_lowerCamelCase	,      '''hidden_sizes''' ) )
										self.parent.assertTrue(hasattr(_lowerCamelCase	,      '''neck_hidden_sizes''' ) )
										self.parent.assertTrue(hasattr(_lowerCamelCase	,      '''num_attention_heads''' ) )


class       UpperCamelCase  :





					"""simple docstring"""






					def __init__(					self :							int	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							List[Any]=1_3	,      _lowerCamelCase :							Union[str, Any]=3_2	,      _lowerCamelCase :							Optional[int]=2	,      _lowerCamelCase :							int=3	,      _lowerCamelCase :							str=6_4_0	,      _lowerCamelCase :							int=4	,      _lowerCamelCase :							Union[str, Any]="silu"	,      _lowerCamelCase :							str=3	,      _lowerCamelCase :							Tuple=3_2	,      _lowerCamelCase :							Optional[int]=0.1	,      _lowerCamelCase :							int=0.1	,      _lowerCamelCase :							Any=0.1	,      _lowerCamelCase :							List[str]=0.02	,      _lowerCamelCase :							List[str]=True	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							Any=1_0	,      _lowerCamelCase :							Union[str, Any]=None	,      ):
										A__				=   parent
										A__				=   batch_size
										A__				=   image_size
										A__				=   patch_size
										A__				=   num_channels
										A__				=   last_hidden_size
										A__				=   num_attention_heads
										A__				=   hidden_act
										A__				=   conv_kernel_size
										A__				=   output_stride
										A__				=   hidden_dropout_prob
										A__				=   attention_probs_dropout_prob
										A__				=   classifier_dropout_prob
										A__				=   use_labels
										A__				=   is_training
										A__				=   num_labels
										A__				=   initializer_range
										A__				=   scope





					def 						A__  (					self :							Union[str, Any] ):
										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 :							Dict ):
										return MobileViTConfig(
										    image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      num_attention_heads=self.num_attention_heads	,      hidden_act=self.hidden_act	,      conv_kernel_size=self.conv_kernel_size	,      output_stride=self.output_stride	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      classifier_dropout_prob=self.classifier_dropout_prob	,      initializer_range=self.initializer_range	,      )





					def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Optional[int] ):
										A__				=   TFMobileViTModel(config=_lowerCamelCase )
										A__				=   model(_lowerCamelCase	,      training=_lowerCamelCase )
										A__				=   A__				=   self.image_size // self.output_stride
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,      (self.batch_size, self.last_hidden_size, expected_height, expected_width) )





					def 						A__  (					self :							Dict	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							Tuple ):
										A__				=   self.num_labels
										A__				=   TFMobileViTForImageClassification(_lowerCamelCase )
										A__				=   model(_lowerCamelCase	,      labels=_lowerCamelCase	,      training=_lowerCamelCase )
										self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels) )





					def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							int ):
										A__				=   self.num_labels
										A__				=   TFMobileViTForSemanticSegmentation(_lowerCamelCase )
										A__				=   A__				=   self.image_size // self.output_stride

										A__				=   model(_lowerCamelCase	,      training=_lowerCamelCase )
										self.parent.assertEqual(
										    result.logits.shape	,      (self.batch_size, self.num_labels, expected_height, expected_width) )

										A__				=   model(_lowerCamelCase	,      labels=_lowerCamelCase	,      training=_lowerCamelCase )
										self.parent.assertEqual(
										    result.logits.shape	,      (self.batch_size, self.num_labels, expected_height, expected_width) )





					def 						A__  (					self :							Union[str, Any] ):
										A__				=   self.prepare_config_and_inputs()
										A__	,     A__	,     A__	,     A__				=   config_and_inputs
										A__				=   {'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_tf
class       UpperCamelCase  (					a      , a      , unittest.TestCase				):





					"""simple docstring"""






					_lowerCamelCase					: Tuple				   =(
					    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase					: Optional[int]				   =(
					    {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
					    if is_tf_available()
					    else {}
					)

					_lowerCamelCase					: Dict				   =False
					_lowerCamelCase					: Tuple				   =False
					_lowerCamelCase					: Any				   =False
					_lowerCamelCase					: Dict				   =False
					_lowerCamelCase					: Optional[int]				   =False





					def 						A__  (					self :							List[str] ):
										A__				=   TFMobileViTModelTester(self )
										A__				=   TFMobileViTConfigTester(self	,      config_class=_lowerCamelCase	,      has_text_modality=_lowerCamelCase )





					def 						A__  (					self :							int ):
										self.config_tester.run_common_tests()





					@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
					def 						A__  (					self :							str ):
										pass





					@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
					def 						A__  (					self :							Tuple ):
										pass





					@unittest.skip(reason='''MobileViT does not output attentions''' )
					def 						A__  (					self :							int ):
										pass





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

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

															A__				=   ['''pixel_values''']
															self.assertListEqual(arg_names[:1]	,      _lowerCamelCase )





					def 						A__  (					self :							Tuple ):
										A__				=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase )





					def 						A__  (					self :							Dict ):
										def check_hidden_states_output(_lowerCamelCase :							Any	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							int ):
															A__				=   model_class(_lowerCamelCase )

															A__				=   model(**self._prepare_for_class(_lowerCamelCase	,      _lowerCamelCase ) )

															A__				=   outputs.hidden_states

															A__				=   5
															self.assertEqual(len(_lowerCamelCase )	,      _lowerCamelCase )

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															A__				=   2
															for i in range(len(_lowerCamelCase ) ):
																				self.assertListEqual(
																				    list(hidden_states[i].shape[-2:] )	,      [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]	,      )
																				divisor *= 2

															self.assertEqual(self.model_tester.output_stride	,      divisor // 2 )

										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(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )

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

															check_hidden_states_output(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )





					def 						A__  (					self :							List[Any] ):
										A__				=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )





					def 						A__  (					self :							int ):
										A__				=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase )





					@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 						A__  (					self :							Optional[int] ):
										super().test_dataset_conversion()





					def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Optional[Any]=2E-1	,      _lowerCamelCase :							Optional[int]=2E-1 ):
										self.assertTrue(np.allclose(_lowerCamelCase	,      _lowerCamelCase	,      atol=_lowerCamelCase	,      rtol=_lowerCamelCase ) )





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

										for model_class in self.all_model_classes:
															# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
															if model_class.__name__ != "TFMobileViTModel":
																				A__				=   model_class(_lowerCamelCase )
																				if getattr(_lowerCamelCase	,      '''hf_compute_loss'''	,      _lowerCamelCase ):
																									super().test_keras_fit()





					def 						A__  (					self :							List[str] ):
										A__	,     A__				=   self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
															# set an ignore index to correctly test the masked loss used in
															# `TFMobileViTForSemanticSegmentation`.
															if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																				A__				=   5

															A__				=   model_class(_lowerCamelCase )
															if getattr(_lowerCamelCase	,      '''hf_compute_loss'''	,      _lowerCamelCase ):
																				# The number of elements in the loss should be the same as the number of elements in the label
																				A__				=   self._prepare_for_class(inputs_dict.copy()	,      _lowerCamelCase	,      return_labels=_lowerCamelCase )
																				A__				=   prepared_for_class[
																				    sorted(prepared_for_class.keys() - inputs_dict.keys()	,      reverse=_lowerCamelCase )[0]
																				]
																				A__				=   added_label.shape.as_list()[:1]

																				# Test that model correctly compute the loss with kwargs
																				A__				=   self._prepare_for_class(inputs_dict.copy()	,      _lowerCamelCase	,      return_labels=_lowerCamelCase )
																				A__				=   {'''input_ids''', '''pixel_values''', '''input_features'''}
																				A__				=   possible_input_names.intersection(set(_lowerCamelCase ) ).pop()
																				A__				=   prepared_for_class.pop(_lowerCamelCase )

																				A__				=   model(_lowerCamelCase	,      **_lowerCamelCase )[0]
																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

																				# Test that model correctly compute the loss when we mask some positions
																				A__				=   self._prepare_for_class(inputs_dict.copy()	,      _lowerCamelCase	,      return_labels=_lowerCamelCase )
																				A__				=   {'''input_ids''', '''pixel_values''', '''input_features'''}
																				A__				=   possible_input_names.intersection(set(_lowerCamelCase ) ).pop()
																				A__				=   prepared_for_class.pop(_lowerCamelCase )
																				if "labels" in prepared_for_class:
																									A__				=   prepared_for_class['''labels'''].numpy()
																									if len(labels.shape ) > 1 and labels.shape[1] != 1:
																														# labels[0] = -100
																														A__				=   tf.convert_to_tensor(_lowerCamelCase )
																														A__				=   model(_lowerCamelCase	,      **_lowerCamelCase )[0]
																														self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
																														self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )

                # Test that model correctly compute the loss with a dict
																				A__				=   self._prepare_for_class(inputs_dict.copy()	,      _lowerCamelCase	,      return_labels=_lowerCamelCase )
																				A__				=   model(_lowerCamelCase )[0]
																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

																				# Test that model correctly compute the loss with a tuple
																				A__				=   self._prepare_for_class(inputs_dict.copy()	,      _lowerCamelCase	,      return_labels=_lowerCamelCase )

																				# Get keys that were added with the _prepare_for_class function
																				A__				=   prepared_for_class.keys() - inputs_dict.keys()
																				A__				=   inspect.signature(model.call ).parameters
																				A__				=   list(signature.keys() )

																				# Create a dictionary holding the location of the tensors in the tuple
																				A__				=   {0: input_name}
																				for label_key in label_keys:
																									A__				=   signature_names.index(_lowerCamelCase )
																									A__				=   label_key
																				A__				=   sorted(tuple_index_mapping.items() )
																				# Initialize a list with their default values, update the values and convert to a tuple
																				A__				=   []

																				for name in signature_names:
																									if name != "kwargs":
																														list_input.append(signature[name].default )

																				for index, value in sorted_tuple_index_mapping:
																									A__				=   prepared_for_class[value]

																				A__				=   tuple(_lowerCamelCase )

																				# Send to model
																				A__				=   model(tuple_input[:-1] )[0]

																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )





					@slow
					def 						A__  (					self :							List[str] ):
										for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															A__				=   TFMobileViTModel.from_pretrained(_lowerCamelCase )
															self.assertIsNotNone(_lowerCamelCase )



def   a_	(			):
					A__				=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image

@require_tf
class       UpperCamelCase  (					unittest.TestCase				):





					"""simple docstring"""






					@slow
					def 						A__  (					self :							List[Any] ):
										A__				=   TFMobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' )

										A__				=   MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' )
										A__				=   prepare_img()
										A__				=   image_processor(images=_lowerCamelCase	,      return_tensors='''tf''' )

										# forward pass
										A__				=   model(**_lowerCamelCase	,      training=_lowerCamelCase )

										# verify the logits
										A__				=   tf.TensorShape((1, 1_0_0_0) )
										self.assertEqual(outputs.logits.shape	,      _lowerCamelCase )

										A__				=   tf.constant([-1.9_364, -1.2_327, -0.4_653] )

										tf.debugging.assert_near(outputs.logits[0, :3]	,      _lowerCamelCase	,      atol=1E-4	,      rtol=1E-04 )





					@slow
					def 						A__  (					self :							Tuple ):
										# `from_pt` will be removed
										A__				=   TFMobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )

										A__				=   MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )

										A__				=   prepare_img()
										A__				=   image_processor(images=_lowerCamelCase	,      return_tensors='''tf''' )

										# forward pass
										A__				=   model(inputs.pixel_values	,      training=_lowerCamelCase )
										A__				=   outputs.logits

										# verify the logits
										A__				=   tf.TensorShape((1, 2_1, 3_2, 3_2) )
										self.assertEqual(logits.shape	,      _lowerCamelCase )

										A__				=   tf.constant(
										    [
										        [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
										        [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
										        [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
										    ] )

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








"""simple docstring"""







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case	:				List[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          with patch.object(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



