
from __future__ import annotations

import unittest

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

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 (
         TFFunnelBaseModel,
         TFFunnelForMaskedLM,
         TFFunnelForMultipleChoice,
         TFFunnelForPreTraining,
         TFFunnelForQuestionAnswering,
         TFFunnelForSequenceClassification,
         TFFunnelForTokenClassification,
         TFFunnelModel,
     )




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=7		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=99		,	lowerCamelCase=[1, 1, 2]		,	lowerCamelCase=1		,	lowerCamelCase=32		,	lowerCamelCase=4		,	lowerCamelCase=8		,	lowerCamelCase=37		,	lowerCamelCase="gelu_new"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=0.0		,	lowerCamelCase=5_12		,	lowerCamelCase=3		,	lowerCamelCase=0.0_2		,	lowerCamelCase=3		,	lowerCamelCase=4		,	lowerCamelCase=None		,	lowerCamelCase=False		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          snake_case__						=				use_input_mask
          snake_case__						=				use_token_type_ids
          snake_case__						=				use_labels
          snake_case__						=				vocab_size
          snake_case__						=				block_sizes
          snake_case__						=				num_decoder_layers
          snake_case__						=				d_model
          snake_case__						=				n_head
          snake_case__						=				d_head
          snake_case__						=				d_inner
          snake_case__						=				hidden_act
          snake_case__						=				hidden_dropout
          snake_case__						=				attention_dropout
          snake_case__						=				activation_dropout
          snake_case__						=				max_position_embeddings
          snake_case__						=				type_vocab_size
          snake_case__						=				2
          snake_case__						=				num_labels
          snake_case__						=				num_choices
          snake_case__						=				scope
          snake_case__						=				initializer_std

          # Used in the tests to check the size of the first attention layer
          snake_case__						=				n_head
          # Used in the tests to check the size of the first hidden state
          snake_case__						=				self.d_model
          # Used in the tests to check the number of output hidden states/attentions
          snake_case__						=				sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
          # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
          # the last hidden state of the first block (which is the first hidden state of the decoder).
          if not base:
              snake_case__						=				self.num_hidden_layers + 2
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				None
          if self.use_input_mask:
              snake_case__						=				random_attention_mask([self.batch_size, self.seq_length] )

          snake_case__						=				None
          if self.use_token_type_ids:
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size )

          snake_case__						=				None
          snake_case__						=				None
          snake_case__						=				None
          if self.use_labels:
              snake_case__						=				ids_tensor([self.batch_size]		,	self.type_sequence_label_size )
              snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels )
              snake_case__						=				ids_tensor([self.batch_size]		,	self.num_choices )

          snake_case__						=				FunnelConfig(
              vocab_size=self.vocab_size		,	block_sizes=self.block_sizes		,	num_decoder_layers=self.num_decoder_layers		,	d_model=self.d_model		,	n_head=self.n_head		,	d_head=self.d_head		,	d_inner=self.d_inner		,	hidden_act=self.hidden_act		,	hidden_dropout=self.hidden_dropout		,	attention_dropout=self.attention_dropout		,	activation_dropout=self.activation_dropout		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	initializer_std=self.initializer_std		,	)

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				TFFunnelModel(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				model(lowerCamelCase )

          snake_case__						=				[input_ids, input_mask]
          snake_case__						=				model(lowerCamelCase )

          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model) )

          snake_case__						=				False
          snake_case__						=				TFFunnelModel(config=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model) )

          snake_case__						=				False
          snake_case__						=				TFFunnelModel(config=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.d_model) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				TFFunnelBaseModel(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				model(lowerCamelCase )

          snake_case__						=				[input_ids, input_mask]
          snake_case__						=				model(lowerCamelCase )

          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 2, self.d_model) )

          snake_case__						=				False
          snake_case__						=				TFFunnelBaseModel(config=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 3, self.d_model) )

          snake_case__						=				False
          snake_case__						=				TFFunnelBaseModel(config=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, 2, self.d_model) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				TFFunnelForPreTraining(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				TFFunnelForMaskedLM(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				model(lowerCamelCase )
          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		,	):
          snake_case__						=				self.num_labels
          snake_case__						=				TFFunnelForSequenceClassification(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				self.num_choices
          snake_case__						=				TFFunnelForMultipleChoice(config=lowerCamelCase )
          snake_case__						=				tf.tile(tf.expand_dims(lowerCamelCase		,	1 )		,	(1, self.num_choices, 1) )
          snake_case__						=				tf.tile(tf.expand_dims(lowerCamelCase		,	1 )		,	(1, self.num_choices, 1) )
          snake_case__						=				tf.tile(tf.expand_dims(lowerCamelCase		,	1 )		,	(1, self.num_choices, 1) )
          snake_case__						=				{
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          snake_case__						=				model(lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_choices) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				self.num_labels
          snake_case__						=				TFFunnelForTokenClassification(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				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		,	):
          snake_case__						=				TFFunnelForQuestionAnswering(config=lowerCamelCase )
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          snake_case__						=				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 ):
          snake_case__						=				self.prepare_config_and_inputs()
          (
              (
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					
          )						=				config_and_inputs
          snake_case__						=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict





@require_tf
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		str    	=     (
          (
              TFFunnelModel,
              TFFunnelForMaskedLM,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      _A						:		Optional[int]    	=     (
          {
              'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
              'fill-mask': TFFunnelForMaskedLM,
              'question-answering': TFFunnelForQuestionAnswering,
              'text-classification': TFFunnelForSequenceClassification,
              'token-classification': TFFunnelForTokenClassification,
              'zero-shot': TFFunnelForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _A						:		Optional[Any]    	=     False
      _A						:		Optional[Any]    	=     False
      def A_			(			self ):
          snake_case__						=				TFFunnelModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )



      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )





@require_tf
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Tuple    	=     (
          (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
      )
      _A						:		Optional[Any]    	=     False
      _A						:		List[Any]    	=     False
      def A_			(			self ):
          snake_case__						=				TFFunnelModelTester(self		,	base=lowerCamelCase )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_base_model(*lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )



      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase )





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

    # each value
    for y in range(__lowerCAmelCase							):
        for x in range(__lowerCAmelCase							):
            # distance from center
            snake_case__						=				x - ksize // 2
            snake_case__						=				y - ksize // 2

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

            # get kernel x
            snake_case__						=				cos_theta * px + sin_theta * py

            # get kernel y
            snake_case__						=				-sin_theta * px + cos_theta * py

            # fill kernel
            snake_case__						=				np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)							) * np.cos(2 * np.pi * _x / lambd + psi							)

    return gabor


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # read original image
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     __magic_name__            =  np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          __magic_name__            =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     __magic_name__            =  out / out.max() * 255
     __magic_name__            =  out.astype(np.uinta)

     imshow('''Original''', gray)
     imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

     waitKey(0)



