


'''simple docstring'''




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  _lowercase   :
   def __init__(	self					:			str					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      )   ->       Tuple:
        __snake_case    			=	parent
        __snake_case    			=	13
        __snake_case    			=	7
        __snake_case    			=	True
        __snake_case    			=	True
        __snake_case    			=	True
        __snake_case    			=	True
        __snake_case    			=	True
        __snake_case    			=	99
        __snake_case    			=	32
        __snake_case    			=	2
        __snake_case    			=	4
        __snake_case    			=	37
        __snake_case    			=	'gelu'
        __snake_case    			=	0.1
        __snake_case    			=	0.1
        __snake_case    			=	512
        __snake_case    			=	16
        __snake_case    			=	2
        __snake_case    			=	0.0_2
        __snake_case    			=	3
        __snake_case    			=	4
        __snake_case    			=	None
        __snake_case    			=	self.vocab_size - 1
   def a     (	self					:			Optional[int]			)   ->       int:
        __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
        if self.use_mc_token_ids:
             __snake_case    			=	ids_tensor([self.batch_size, self.num_choices]					,      self.seq_length			)

        __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    			=	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					,      )

        __snake_case    			=	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 a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Dict					,      *SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       int:
        __snake_case    			=	TFOpenAIGPTModel(config=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	[input_ids, input_mask]
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)

        self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)			)
   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			Dict					,      *SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       Union[str, Any]:
        __snake_case    			=	TFOpenAIGPTLMHeadModel(config=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      *SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       Union[str, Any]:
        __snake_case    			=	TFOpenAIGPTDoubleHeadsModel(config=SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,      1			)					,      (1, self.num_choices, 1)			)
        __snake_case    			=	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,      1			)					,      (1, self.num_choices, 1)			)
        __snake_case    			=	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,      1			)					,      (1, self.num_choices, 1)			)

        __snake_case    			=	{
            '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,
        }
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)
        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 a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Any					,      *SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       Dict:
        __snake_case    			=	self.num_labels
        __snake_case    			=	ids_tensor([self.batch_size]					,      self.type_sequence_label_size			)
        __snake_case    			=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
            'labels': sequence_labels,
        }
        __snake_case    			=	TFOpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)			)





   def a     (	self					:			str			)   ->       Tuple:
        __snake_case    			=	self.prepare_config_and_inputs()

        (
            (
            __snake_case					
        )			,					(
            __snake_case					
        )			,					(
            __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  _lowercase   (  __lowercase	,				__lowercase	,				unittest.TestCase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[Any]									=  (
       (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
       if is_tf_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE     :     Tuple									=  (
       (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  (
       {
           "feature-extraction": TFOpenAIGPTModel,
           "text-classification": TFOpenAIGPTForSequenceClassification,
           "text-generation": TFOpenAIGPTLMHeadModel,
           "zero-shot": TFOpenAIGPTForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE     :     Any									=  False
   _SCREAMING_SNAKE_CASE     :     Tuple									=  False
   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			str			)   ->       Dict:
        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 a     (	self					:			Union[str, Any]			)   ->       Optional[int]:
        __snake_case    			=	TFOpenAIGPTModelTester(self			)
        __snake_case    			=	ConfigTester(self					,      config_class=SCREAMING_SNAKE_CASE_					,      n_embd=37			)
   def a     (	self					:			Tuple			)   ->       str:
        self.config_tester.run_common_tests()
   def a     (	self					:			Any			)   ->       Dict:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			int			)   ->       Optional[int]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_lm_head(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Tuple			)   ->       List[str]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_double_head(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Tuple			)   ->       Union[str, Any]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)





   @slow
   def a     (	self					:			Tuple			)   ->       Union[str, Any]:
        for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __snake_case    			=	TFOpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)




@require_tf
class  _lowercase   (  unittest.TestCase       ):
   @slow
   def a     (	self					:			Optional[int]			)   ->       str:
        __snake_case    			=	TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt'			)
        __snake_case    			=	tf.convert_to_tensor([[481, 4735, 544]]					,      dtype=tf.intaa			)  # the president is
        __snake_case    			=	[
            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

        __snake_case    			=	model.generate(SCREAMING_SNAKE_CASE_					,      do_sample=SCREAMING_SNAKE_CASE_			)
        self.assertListEqual(output_ids[0].numpy().tolist()					,      SCREAMING_SNAKE_CASE_			)








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




