


'''simple docstring'''




import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
           YosoModel,
       )
       from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class  _lowercase   :
   def __init__(	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=13					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=7					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=True					,      SCREAMING_SNAKE_CASE_					:			Any=True					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=99					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=32					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=5					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=4					,      SCREAMING_SNAKE_CASE_					:			Any=37					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			List[Any]=0.1					,      SCREAMING_SNAKE_CASE_					:			Dict=0.1					,      SCREAMING_SNAKE_CASE_					:			str=512					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=16					,      SCREAMING_SNAKE_CASE_					:			Dict=2					,      SCREAMING_SNAKE_CASE_					:			str=0.0_2					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=3					,      SCREAMING_SNAKE_CASE_					:			Any=4					,      SCREAMING_SNAKE_CASE_					:			str=None					,      )   ->       Tuple:
        __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    			=	hidden_size
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	num_attention_heads
        __snake_case    			=	intermediate_size
        __snake_case    			=	hidden_act
        __snake_case    			=	hidden_dropout_prob
        __snake_case    			=	attention_probs_dropout_prob
        __snake_case    			=	max_position_embeddings
        __snake_case    			=	type_vocab_size
        __snake_case    			=	type_sequence_label_size
        __snake_case    			=	initializer_range
        __snake_case    			=	num_labels
        __snake_case    			=	num_choices
        __snake_case    			=	scope
   def a     (	self					:			List[Any]			)   ->       str:
        __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    			=	self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def a     (	self					:			List[Any]			)   ->       Union[str, Any]:
        return YosoConfig(
            vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      is_decoder=SCREAMING_SNAKE_CASE_					,      initializer_range=self.initializer_range					,      )
   def a     (	self					:			Tuple			)   ->       Any:
        __snake_case    			=	self.get_config()
        __snake_case    			=	300
        return config
   def a     (	self					:			int			)   ->       Dict:
        (
            (
            __snake_case					
        )			,					(
            __snake_case					
        )			,					(
            __snake_case					
        )			,					(
            __snake_case					
        )			,					(
            __snake_case					
        )			,					(
            __snake_case					
        )			,					(
            __snake_case					
        )			,										
        )    			=	self.prepare_config_and_inputs()

        __snake_case    			=	True
        __snake_case    			=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
        __snake_case    			=	ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2			)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )
   def a     (	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       Dict:
        __snake_case    			=	YosoModel(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      token_type_ids=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					:			Any					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      )   ->       Any:
        __snake_case    			=	True
        __snake_case    			=	YosoModel(SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	model(
            SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      encoder_hidden_states=SCREAMING_SNAKE_CASE_					,      encoder_attention_mask=SCREAMING_SNAKE_CASE_					,      )
        __snake_case    			=	model(
            SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      encoder_hidden_states=SCREAMING_SNAKE_CASE_					,      )
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)			)
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]			)   ->       List[Any]:
        __snake_case    			=	YosoForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      labels=SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)			)
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       Optional[int]:
        __snake_case    			=	YosoForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	model(
            SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      start_positions=SCREAMING_SNAKE_CASE_					,      end_positions=SCREAMING_SNAKE_CASE_					,      )
        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					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       Tuple:
        __snake_case    			=	self.num_labels
        __snake_case    			=	YosoForSequenceClassification(SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      labels=SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)			)
   def a     (	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Tuple			)   ->       List[Any]:
        __snake_case    			=	self.num_labels
        __snake_case    			=	YosoForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      labels=SCREAMING_SNAKE_CASE_			)
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)			)
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Any					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Tuple			)   ->       Dict:
        __snake_case    			=	self.num_choices
        __snake_case    			=	YosoForMultipleChoice(config=SCREAMING_SNAKE_CASE_			)
        model.to(SCREAMING_SNAKE_CASE_			)
        model.eval()
        __snake_case    			=	input_ids.unsqueeze(1			).expand(-1					,      self.num_choices					,      -1			).contiguous()
        __snake_case    			=	token_type_ids.unsqueeze(1			).expand(-1					,      self.num_choices					,      -1			).contiguous()
        __snake_case    			=	input_mask.unsqueeze(1			).expand(-1					,      self.num_choices					,      -1			).contiguous()
        __snake_case    			=	model(
            SCREAMING_SNAKE_CASE_					,      attention_mask=SCREAMING_SNAKE_CASE_					,      token_type_ids=SCREAMING_SNAKE_CASE_					,      labels=SCREAMING_SNAKE_CASE_					,      )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)			)





   def a     (	self					:			Dict			)   ->       Dict:
        __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_torch
class  _lowercase   (  __lowercase	,				__lowercase	,				unittest.TestCase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  (
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE     :     List[str]									=  False
   _SCREAMING_SNAKE_CASE     :     Dict									=  False
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  False

   _SCREAMING_SNAKE_CASE     :     int									=  ()
   _SCREAMING_SNAKE_CASE     :     str									=  (
       {
           "feature-extraction": YosoModel,
           "fill-mask": YosoForMaskedLM,
           "question-answering": YosoForQuestionAnswering,
           "text-classification": YosoForSequenceClassification,
           "token-classification": YosoForTokenClassification,
           "zero-shot": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   def a     (	self					:			Any			)   ->       Tuple:
        __snake_case    			=	YosoModelTester(self			)
        __snake_case    			=	ConfigTester(self					,      config_class=SCREAMING_SNAKE_CASE_					,      hidden_size=37			)
   def a     (	self					:			Dict			)   ->       Optional[int]:
        self.config_tester.run_common_tests()
   def a     (	self					:			Any			)   ->       Optional[int]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             __snake_case    			=	type
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Tuple			)   ->       int:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[int]			)   ->       List[Any]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[int]			)   ->       Optional[int]:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[Any]			)   ->       Tuple:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Tuple			)   ->       int:
        __snake_case    			=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_			)
   @slow
   def a     (	self					:			List[str]			)   ->       List[str]:
        for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __snake_case    			=	YosoModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)





   def a     (	self					:			Any			)   ->       Dict:
        return




@require_torch
class  _lowercase   (  unittest.TestCase       ):
   @slow
   def a     (	self					:			Optional[int]			)   ->       List[Any]:
        __snake_case    			=	YosoModel.from_pretrained('uw-madison/yoso-4096'			)
        __snake_case    			=	torch.tensor([[0, 1, 2, 3, 4, 5]]			)

        with torch.no_grad():
             __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)[0]

        __snake_case    			=	torch.Size((1, 6, 768)			)
        self.assertEqual(output.shape					,      SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	torch.tensor(
            [[[-0.0_6_1_1, 0.1_2_4_2, 0.0_8_4_0], [0.0_2_8_0, -0.0_0_4_8, 0.1_1_2_5], [0.0_1_0_6, 0.0_2_2_6, 0.0_7_5_1]]]			)

        self.assertTrue(torch.allclose(output[:, :3, :3]					,      SCREAMING_SNAKE_CASE_					,      atol=1e-4			)			)
   @slow
   def a     (	self					:			int			)   ->       Any:
        __snake_case    			=	YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'			)
        __snake_case    			=	torch.tensor([[0, 1, 2, 3, 4, 5]]			)

        with torch.no_grad():
             __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)[0]

        __snake_case    			=	5_0265

        __snake_case    			=	torch.Size((1, 6, vocab_size)			)
        self.assertEqual(output.shape					,      SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	torch.tensor(
            [[[-2.1_3_1_3, -3.7_2_8_5, -2.2_4_0_7], [-2.7_0_4_7, -3.3_3_1_4, -2.6_4_0_8], [0.0_6_2_9, -2.5_1_6_6, -0.3_3_5_6]]]			)

        self.assertTrue(torch.allclose(output[:, :3, :3]					,      SCREAMING_SNAKE_CASE_					,      atol=1e-4			)			)





   @slow
   def a     (	self					:			Optional[Any]			)   ->       str:
        __snake_case    			=	YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096'			)
        __snake_case    			=	torch.arange(4096			).unsqueeze(0			)

        with torch.no_grad():
             __snake_case    			=	model(SCREAMING_SNAKE_CASE_			)[0]

        __snake_case    			=	5_0265

        __snake_case    			=	torch.Size((1, 4096, vocab_size)			)
        self.assertEqual(output.shape					,      SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	torch.tensor(
            [[[-2.3_9_1_4, -4.3_7_4_2, -5.0_9_5_6], [-4.0_9_8_8, -4.2_3_8_4, -7.0_4_0_6], [-3.1_4_2_7, -3.7_1_9_2, -6.6_8_0_0]]]			)

        self.assertTrue(torch.allclose(output[:, :3, :3]					,      SCREAMING_SNAKE_CASE_					,      atol=1e-4			)			)








'''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")




