
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__magic_name__            =  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		Dict    	=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _A						:		str    	=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _A						:		Optional[int]    	=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _A						:		Any    	=     {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline(
              task="text-classification"		,	model="hf-internal-testing/tiny-random-distilbert"		,	framework="pt" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )

          snake_case__						=				text_classifier("This is great !"		,	top_k=2 )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}] )

          snake_case__						=				text_classifier(["This is great !", "This is bad"]		,	top_k=2 )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
              ]		,	)

          snake_case__						=				text_classifier("This is great !"		,	top_k=1 )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )

          # Legacy behavior
          snake_case__						=				text_classifier("This is great !"		,	return_all_scores=lowerCamelCase )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )

          snake_case__						=				text_classifier("This is great !"		,	return_all_scores=lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]] )

          snake_case__						=				text_classifier(["This is great !", "Something else"]		,	return_all_scores=lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
                  [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
              ]		,	)

          snake_case__						=				text_classifier(["This is great !", "Something else"]		,	return_all_scores=lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[
                  {"label": "LABEL_0", "score": 0.5_0_4},
                  {"label": "LABEL_0", "score": 0.5_0_4},
              ]		,	)
      @require_torch
      def A_			(			self ):
          import torch

          snake_case__						=				pipeline(
              task="text-classification"		,	model="hf-internal-testing/tiny-random-distilbert"		,	framework="pt"		,	device=torch.device("cpu" )		,	)

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline(
              task="text-classification"		,	model="hf-internal-testing/tiny-random-distilbert"		,	framework="tf" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "LABEL_0", "score": 0.5_0_4}] )
      @slow
      @require_torch
      def A_			(			self ):
          snake_case__						=				pipeline("text-classification" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("This is bad !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "NEGATIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("Birds are a type of animal" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 0.9_8_8}] )
      @slow
      @require_tf
      def A_			(			self ):
          snake_case__						=				pipeline("text-classification"		,	framework="tf" )

          snake_case__						=				text_classifier("This is great !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("This is bad !" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "NEGATIVE", "score": 1.0}] )
          snake_case__						=				text_classifier("Birds are a type of animal" )
          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": "POSITIVE", "score": 0.9_8_8}] )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TextClassificationPipeline(model=lowerCamelCase		,	tokenizer=lowerCamelCase )
          return text_classifier, ["HuggingFace is in", "This is another test"]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				text_classifier.model
          # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
          snake_case__						=				"HuggingFace is in"
          snake_case__						=				text_classifier(lowerCamelCase )

          self.assertEqual(nested_simplify(lowerCamelCase )		,	[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}] )
          self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )

          snake_case__						=				["HuggingFace is in ", "Paris is in France"]
          snake_case__						=				text_classifier(lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}, {"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}]		,	)
          self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
          self.assertTrue(outputs[1]["label"] in model.config.idalabel.values() )

          # Forcing to get all results with `top_k=None`
          # This is NOT the legacy format
          snake_case__						=				text_classifier(lowerCamelCase		,	top_k=lowerCamelCase )
          snake_case__						=				len(model.config.idalabel.values() )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}] * N, [{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}] * N]		,	)

          snake_case__						=				{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
          snake_case__						=				text_classifier(lowerCamelCase )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}		,	)
          self.assertTrue(outputs["label"] in model.config.idalabel.values() )

          # This might be used a text pair, but tokenizer + pipe interaction
          # makes it hard to understand that it's not using the pair properly
          # https://github.com/huggingface/transformers/issues/17305
          # We disabled this usage instead as it was outputting wrong outputs.
          snake_case__						=				[["HuggingFace is in ", "Paris is in France"]]
          with self.assertRaises(lowerCamelCase ):
              text_classifier(lowerCamelCase )

          # This used to be valid for doing text pairs
          # We're keeping it working because of backward compatibility
          snake_case__						=				text_classifier([[["HuggingFace is in ", "Paris is in France"]]] )
          self.assertEqual(
              nested_simplify(lowerCamelCase )		,	[{"label": ANY(lowerCamelCase ), "score": ANY(lowerCamelCase )}]		,	)
          self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )





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)



