


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, 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.models.openai.modeling_tf_openai import (
     TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFOpenAIGPTDoubleHeadsModel,
     TFOpenAIGPTForSequenceClassification,
     TFOpenAIGPTLMHeadModel,
     TFOpenAIGPTModel,
 )




class A  :






      def __init__( self						:					Any      ,       lowercase_						:					Dict      ,       ) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =parent
             _lowerCamelCase	:		List[str]          =13
             _lowerCamelCase	:		Union[str, Any]          =7
             _lowerCamelCase	:		Optional[Any]          =True
             _lowerCamelCase	:		str          =True
             _lowerCamelCase	:		List[str]          =True
             _lowerCamelCase	:		int          =True
             _lowerCamelCase	:		Tuple          =True
             _lowerCamelCase	:		int          =99
             _lowerCamelCase	:		Union[str, Any]          =32
             _lowerCamelCase	:		Union[str, Any]          =2
             _lowerCamelCase	:		Optional[Any]          =4
             _lowerCamelCase	:		Optional[Any]          =37
             _lowerCamelCase	:		Dict          ='gelu'
             _lowerCamelCase	:		List[Any]          =0.1
             _lowerCamelCase	:		Dict          =0.1
             _lowerCamelCase	:		str          =512
             _lowerCamelCase	:		Tuple          =16
             _lowerCamelCase	:		Union[str, Any]          =2
             _lowerCamelCase	:		Tuple          =0.02
             _lowerCamelCase	:		str          =3
             _lowerCamelCase	:		List[str]          =4
             _lowerCamelCase	:		Dict          =None
             _lowerCamelCase	:		str          =self.vocab_size - 1






      def 	lowerCamelCase	( self						:					Dict			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Any          =ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

             _lowerCamelCase	:		Any          =None
             if self.use_input_mask:
                    _lowerCamelCase	:		List[str]          =random_attention_mask([self.batch_size, self.seq_length]			)

             _lowerCamelCase	:		Tuple          =None
             if self.use_token_type_ids:
                    _lowerCamelCase	:		List[Any]          =ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size			)

             _lowerCamelCase	:		Dict          =None
             if self.use_mc_token_ids:
                    _lowerCamelCase	:		Union[str, Any]          =ids_tensor([self.batch_size, self.num_choices]      ,       self.seq_length			)

             _lowerCamelCase	:		str          =None
             _lowerCamelCase	:		Any          =None
             _lowerCamelCase	:		List[Any]          =None
             if self.use_labels:
                    _lowerCamelCase	:		Dict          =ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)
                    _lowerCamelCase	:		Dict          =ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels			)
                    _lowerCamelCase	:		List[str]          =ids_tensor([self.batch_size]      ,       self.num_choices			)

             _lowerCamelCase	:		str          =OpenAIGPTConfig(
                 vocab_size=self.vocab_size      ,       n_embd=self.hidden_size      ,       n_layer=self.num_hidden_layers      ,       n_head=self.num_attention_heads      ,       n_positions=self.max_position_embeddings      ,       pad_token_id=self.pad_token_id      ,       )

             _lowerCamelCase	:		Any          =ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,       2			)

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
                 token_type_ids,
                 mc_token_ids,
                 sequence_labels,
                 token_labels,
                 choice_labels,
             )






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					int      ,       lowercase_						:					Dict      ,       lowercase_						:					Dict      ,       lowercase_						:					List[Any]      ,       lowercase_						:					str      ,       *lowercase_						:					int			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =TFOpenAIGPTModel(config=lowercase_			)
             _lowerCamelCase	:		Tuple          ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             _lowerCamelCase	:		List[str]          =model(lowercase_			)

             _lowerCamelCase	:		str          =[input_ids, input_mask]
             _lowerCamelCase	:		str          =model(lowercase_			)

             _lowerCamelCase	:		List[Any]          =model(lowercase_			)

             self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)			)






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Dict      ,       lowercase_						:					List[Any]      ,       lowercase_						:					Tuple      ,       lowercase_						:					Union[str, Any]      ,       *lowercase_						:					List[str]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Any          =TFOpenAIGPTLMHeadModel(config=lowercase_			)
             _lowerCamelCase	:		Tuple          ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             _lowerCamelCase	:		Union[str, Any]          =model(lowercase_			)
             self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)			)






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					int      ,       lowercase_						:					List[str]      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Dict      ,       lowercase_						:					int      ,       lowercase_						:					List[Any]      ,       *lowercase_						:					List[str]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =TFOpenAIGPTDoubleHeadsModel(config=lowercase_			)

             _lowerCamelCase	:		Union[str, Any]          =tf.tile(tf.expand_dims(lowercase_      ,       1			)      ,       (1, self.num_choices, 1)			)
             _lowerCamelCase	:		Dict          =tf.tile(tf.expand_dims(lowercase_      ,       1			)      ,       (1, self.num_choices, 1)			)
             _lowerCamelCase	:		Union[str, Any]          =tf.tile(tf.expand_dims(lowercase_      ,       1			)      ,       (1, self.num_choices, 1)			)

             _lowerCamelCase	:		Optional[int]          ={
                 'input_ids': multiple_choice_inputs_ids,
                 'mc_token_ids': mc_token_ids,
                 'attention_mask': multiple_choice_input_mask,
                 'token_type_ids': multiple_choice_token_type_ids,
             }
             _lowerCamelCase	:		Optional[Any]          =model(lowercase_			)
             self.parent.assertEqual(
                 result.logits.shape      ,       (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)			)
             self.parent.assertEqual(result.mc_logits.shape      ,       (self.batch_size, self.num_choices)			)






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int      ,       lowercase_						:					List[Any]      ,       *lowercase_						:					List[Any]			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.num_labels
             _lowerCamelCase	:		Optional[int]          =ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)
             _lowerCamelCase	:		List[str]          ={
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
                 'labels': sequence_labels,
             }
             _lowerCamelCase	:		Union[str, Any]          =TFOpenAIGPTForSequenceClassification(lowercase_			)
             _lowerCamelCase	:		Dict          =model(lowercase_			)
             self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)			)






      def 	lowerCamelCase	( self						:					List[str]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.prepare_config_and_inputs()

             (
                 (
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					(
                 _lowerCamelCase
             )     ,					
             )	:		Tuple          =config_and_inputs

             _lowerCamelCase	:		List[Any]          ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_tf
class A  (       UpperCamelCase_   ,							UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  Union[str, Any]				=(
          (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
          if is_tf_available()
          else ()
      )
      UpperCamelCase__							:  Tuple				=(
          (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
      UpperCamelCase__							:  str				=(
          {
              'feature-extraction': TFOpenAIGPTModel,
              'text-classification': TFOpenAIGPTForSequenceClassification,
              'text-generation': TFOpenAIGPTLMHeadModel,
              'zero-shot': TFOpenAIGPTForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase__							:  Union[str, Any]				=False
      UpperCamelCase__							:  Dict				=False






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					List[str]      ,       lowercase_						:					List[Any]      ,       lowercase_						:					Any			) ->						Optional[Any]:
             """simple docstring"""




             if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                    # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                    # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
                    # tiny config could not be created.
                    return True

             return False






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =TFOpenAIGPTModelTester(self			)
             _lowerCamelCase	:		int          =ConfigTester(self      ,       config_class=lowercase_      ,       n_embd=37			)






      def 	lowerCamelCase	( self						:					Any			) ->						List[str]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def 	lowerCamelCase	( self						:					str			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_model(*lowercase_			)






      def 	lowerCamelCase	( self						:					str			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_lm_head(*lowercase_			)






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_double_head(*lowercase_			)






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_			)






      @slow
      def 	lowerCamelCase	( self						:					List[str]			) ->						Any:
             """simple docstring"""




             for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase	:		Union[str, Any]          =TFOpenAIGPTModel.from_pretrained(lowercase_			)
                    self.assertIsNotNone(lowercase_			)




@require_tf
class A  (       unittest.TestCase					):






      @slow
      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt'			)
             _lowerCamelCase	:		Optional[int]          =tf.convert_to_tensor([[481, 4735, 544]]      ,       dtype=tf.intaa			)  # the president is
             _lowerCamelCase	:		Optional[int]          =[
                 481,
                 4735,
                 544,
                 246,
                 963,
                 870,
                 762,
                 239,
                 244,
                 4_0477,
                 244,
                 249,
                 719,
                 881,
                 487,
                 544,
                 240,
                 244,
                 603,
                 481,
             ]  # the president is a very good man. " \n " i\'m sure he is, " said the

             _lowerCamelCase	:		int          =model.generate(lowercase_      ,       do_sample=lowercase_			)
             self.assertListEqual(output_ids[0].numpy().tolist()      ,       lowercase_			)






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

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


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

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


