




'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMModel,
		    TFXLMWithLMHeadModel,
		    XLMConfig,
		)




class     UpperCamelCase					:



		def __init__( self		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       List[str]       					=			parent
						_a							:       List[Any]       					=			13
						_a							:       Any       					=			7
						_a							:       Tuple       					=			True
						_a							:       Any       					=			True
						_a							:       Union[str, Any]       					=			True
						_a							:       Tuple       					=			True
						_a							:       Optional[int]       					=			True
						_a							:       Tuple       					=			False
						_a							:       Union[str, Any]       					=			False
						_a							:       List[Any]       					=			False
						_a							:       Union[str, Any]       					=			2
						_a							:       Any       					=			99
						_a							:       str       					=			0
						_a							:       Optional[Any]       					=			32
						_a							:       Optional[Any]       					=			2
						_a							:       int       					=			4
						_a							:       Union[str, Any]       					=			0.1
						_a							:       List[Any]       					=			0.1
						_a							:       List[Any]       					=			512
						_a							:       Tuple       					=			16
						_a							:       Tuple       					=			2
						_a							:       List[Any]       					=			0.02
						_a							:       Optional[int]       					=			3
						_a							:       Optional[int]       					=			4
						_a							:       List[Any]       					=			"""last"""
						_a							:       List[str]       					=			True
						_a							:       Any       					=			None
						_a							:       Any       					=			0



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       int       					=			ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size   )
						_a							:       Tuple       					=			random_attention_mask([self.batch_size, self.seq_length]		,    dtype=tf.floataa   )

						_a							:       Dict       					=			None
						if self.use_input_lengths:
										_a							:       Tuple       					=			(
										    ids_tensor([self.batch_size]		,    vocab_size=2   ) + self.seq_length - 2
										)  # small variation of seq_length

						_a							:       int       					=			None
						if self.use_token_type_ids:
										_a							:       str       					=			ids_tensor([self.batch_size, self.seq_length]		,    self.n_langs   )

						_a							:       Tuple       					=			None
						_a							:       Dict       					=			None
						_a							:       List[Any]       					=			None
						if self.use_labels:
										_a							:       Optional[int]       					=			ids_tensor([self.batch_size]		,    self.type_sequence_label_size   )
										_a							:       int       					=			ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels   )
										_a							:       Dict       					=			ids_tensor([self.batch_size]		,    2		,    dtype=tf.floataa   )
										_a							:       List[str]       					=			ids_tensor([self.batch_size]		,    self.num_choices   )

						_a							:       Tuple       					=			XLMConfig(
						    vocab_size=self.vocab_size		,    n_special=self.n_special		,    emb_dim=self.hidden_size		,    n_layers=self.num_hidden_layers		,    n_heads=self.num_attention_heads		,    dropout=self.hidden_dropout_prob		,    attention_dropout=self.attention_probs_dropout_prob		,    gelu_activation=self.gelu_activation		,    sinusoidal_embeddings=self.sinusoidal_embeddings		,    asm=self.asm		,    causal=self.causal		,    n_langs=self.n_langs		,    max_position_embeddings=self.max_position_embeddings		,    initializer_range=self.initializer_range		,    summary_type=self.summary_type		,    use_proj=self.use_proj		,    bos_token_id=self.bos_token_id		,    )

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       Union[str, Any]       					=			TFXLMModel(config=_lowerCAmelCase   )
						_a							:       Optional[Any]       					=			{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
						_a							:       List[Any]       					=			model(_lowerCAmelCase   )

						_a							:       str       					=			[input_ids, input_mask]
						_a							:       str       					=			model(_lowerCAmelCase   )
						self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)   )



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       Optional[Any]       					=			TFXLMWithLMHeadModel(_lowerCAmelCase   )

						_a							:       int       					=			{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
						_a							:       List[str]       					=			model(_lowerCAmelCase   )

						_a							:       str       					=			outputs

						self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.vocab_size)   )



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       Tuple       					=			TFXLMForQuestionAnsweringSimple(_lowerCAmelCase   )

						_a							:       Dict       					=			{"""input_ids""": input_ids, """lengths""": input_lengths}

						_a							:       Union[str, Any]       					=			model(_lowerCAmelCase   )

						self.parent.assertEqual(result.start_logits.shape		,    (self.batch_size, self.seq_length)   )
						self.parent.assertEqual(result.end_logits.shape		,    (self.batch_size, self.seq_length)   )



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       List[Any]       					=			TFXLMForSequenceClassification(_lowerCAmelCase   )

						_a							:       Tuple       					=			{"""input_ids""": input_ids, """lengths""": input_lengths}

						_a							:       Tuple       					=			model(_lowerCAmelCase   )

						self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)   )



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       Union[str, Any]       					=			self.num_labels
						_a							:       Optional[Any]       					=			TFXLMForTokenClassification(config=_lowerCAmelCase   )
						_a							:       str       					=			{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						_a							:       Union[str, Any]       					=			model(_lowerCAmelCase   )
						self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)   )



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    ):







						'''simple docstring'''


						_a							:       List[Any]       					=			self.num_choices
						_a							:       Optional[int]       					=			TFXLMForMultipleChoice(config=_lowerCAmelCase   )
						_a							:       Tuple       					=			tf.tile(tf.expand_dims(_lowerCAmelCase		,    1   )		,    (1, self.num_choices, 1)   )
						_a							:       List[Any]       					=			tf.tile(tf.expand_dims(_lowerCAmelCase		,    1   )		,    (1, self.num_choices, 1)   )
						_a							:       Optional[Any]       					=			tf.tile(tf.expand_dims(_lowerCAmelCase		,    1   )		,    (1, self.num_choices, 1)   )
						_a							:       Union[str, Any]       					=			{
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						    """token_type_ids""": multiple_choice_token_type_ids,
						}
						_a							:       Optional[Any]       					=			model(_lowerCAmelCase   )
						self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       int       					=			self.prepare_config_and_inputs()
						(
						    _a		
						)							:       Dict       					=			config_and_inputs
						_a							:       str       					=			{
						    """input_ids""": input_ids,
						    """token_type_ids""": token_type_ids,
						    """langs""": token_type_ids,
						    """lengths""": input_lengths,
						}
						return config, inputs_dict






@require_tf
class     UpperCamelCase					(  __lowerCamelCase			,		__lowerCamelCase			,		unittest.TestCase					):
		a    :Dict      					=				(
		    (
		        TFXLMModel,
		        TFXLMWithLMHeadModel,
		        TFXLMForSequenceClassification,
		        TFXLMForQuestionAnsweringSimple,
		        TFXLMForTokenClassification,
		        TFXLMForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		a    :Dict      					=				(
		    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
		)  # TODO (PVP): Check other models whether language generation is also applicable
		a    :Optional[int]      					=				(
		    {
		        'feature-extraction': TFXLMModel,
		        'fill-mask': TFXLMWithLMHeadModel,
		        'question-answering': TFXLMForQuestionAnsweringSimple,
		        'text-classification': TFXLMForSequenceClassification,
		        'text-generation': TFXLMWithLMHeadModel,
		        'token-classification': TFXLMForTokenClassification,
		        'zero-shot': TFXLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		a    :Optional[int]      					=				False
		a    :Union[str, Any]      					=				False



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase   ):







						'''simple docstring'''


						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("""Fast"""   )
						):
										# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
										# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
										# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
										return True

						return False



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       List[Any]       					=			TFXLMModelTester(self   )
						_a							:       Optional[Any]       					=			ConfigTester(self		,    config_class=_lowerCAmelCase		,    emb_dim=37   )



		def 						a_       ( self   ):







						'''simple docstring'''


						self.config_tester.run_common_tests()



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Tuple       					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*_lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       str       					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*_lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Optional[Any]       					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*_lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Tuple       					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*_lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       int       					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_token_classification(*_lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Dict       					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowerCAmelCase   )



		@slow
		def 						a_       ( self   ):







						'''simple docstring'''


						for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_a							:       int       					=			TFXLMModel.from_pretrained(_lowerCAmelCase   )
										self.assertIsNotNone(_lowerCAmelCase   )






@require_tf
class     UpperCamelCase					(  unittest.TestCase					):



		@slow
		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Optional[int]       					=			TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""   )
						_a							:       Dict       					=			tf.convert_to_tensor([[14, 447]]		,    dtype=tf.intaa   )  # the president
						_a							:       str       					=			[
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						_a							:       List[str]       					=			model.generate(_lowerCAmelCase		,    do_sample=_lowerCAmelCase   )
						self.assertListEqual(output_ids[0].numpy().tolist()		,    _lowerCAmelCase   )








'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


