






"""simple docstring"""
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch



class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_,	lowerCamelCase_=1_4,	lowerCamelCase_=7,	lowerCamelCase_=True,	lowerCamelCase_=True,	lowerCamelCase_=False,	lowerCamelCase_=True,	lowerCamelCase_=9_9,	lowerCamelCase_=3_2,	lowerCamelCase_=4,	lowerCamelCase_=4,	lowerCamelCase_=[[["global", "local"], 2]],	lowerCamelCase_=3_7,	lowerCamelCase_="gelu",	lowerCamelCase_=0.1,	lowerCamelCase_=0.1,	lowerCamelCase_=5_1_2,	lowerCamelCase_=7,	lowerCamelCase_=0.02,	):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		parent
       lowerCamelCase__	:     Tuple								=		batch_size
       lowerCamelCase__	:     Dict								=		seq_length
       lowerCamelCase__	:     Tuple								=		is_training
       lowerCamelCase__	:     Optional[Any]								=		use_input_mask
       lowerCamelCase__	:     Union[str, Any]								=		use_token_type_ids
       lowerCamelCase__	:     Optional[Any]								=		use_labels
       lowerCamelCase__	:     Optional[Any]								=		vocab_size
       lowerCamelCase__	:     Optional[int]								=		hidden_size
       lowerCamelCase__	:     Union[str, Any]								=		num_hidden_layers
       lowerCamelCase__	:     Any								=		num_attention_heads
       lowerCamelCase__	:     Dict								=		attention_types
       lowerCamelCase__	:     Optional[Any]								=		intermediate_size
       lowerCamelCase__	:     List[Any]								=		hidden_act
       lowerCamelCase__	:     List[Any]								=		hidden_dropout_prob
       lowerCamelCase__	:     Any								=		attention_probs_dropout_prob
       lowerCamelCase__	:     Optional[Any]								=		max_position_embeddings
       lowerCamelCase__	:     List[str]								=		window_size
       lowerCamelCase__	:     Optional[int]								=		initializer_range
       lowerCamelCase__	:     Any								=		None
       lowerCamelCase__	:     Union[str, Any]								=		vocab_size - 1
       lowerCamelCase__	:     Optional[int]								=		vocab_size - 1
       lowerCamelCase__	:     int								=		vocab_size - 1




      def      a__				(self   ):

       '''simple docstring'''



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

       lowerCamelCase__	:     Optional[int]								=		None
       if self.use_input_mask:
        lowerCamelCase__	:     str								=		random_attention_mask([self.batch_size, self.seq_length]   )

       lowerCamelCase__	:     str								=		GPTNeoConfig(
           vocab_size=self.vocab_size,	hidden_size=self.hidden_size,	num_layers=self.num_hidden_layers,	num_heads=self.num_attention_heads,	max_position_embeddings=self.max_position_embeddings,	use_cache=_A,	bos_token_id=self.bos_token_id,	eos_token_id=self.eos_token_id,	pad_token_id=self.pad_token_id,	window_size=self.window_size,	attention_types=self.attention_types,	)

       return (config, input_ids, input_mask)




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		self.prepare_config_and_inputs()
       lowerCamelCase__	:     Tuple								=		config_and_inputs
       lowerCamelCase__	:     Optional[int]								=		{'input_ids': input_ids, 'attention_mask': attention_mask}
       return config, inputs_dict




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		2_0
       lowerCamelCase__	:     List[str]								=		model_class_name(_A   )

       lowerCamelCase__	:     Union[str, Any]								=		model.init_cache(input_ids.shape[0],	_A   )
       lowerCamelCase__	:     List[str]								=		jnp.ones((input_ids.shape[0], max_decoder_length),	dtype='i4'   )

       lowerCamelCase__	:     str								=		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :],	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
       lowerCamelCase__	:     Dict								=		model(
           input_ids[:, :-1],	attention_mask=_A,	past_key_values=_A,	position_ids=_A,	)

       lowerCamelCase__	:     List[str]								=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],	dtype='i4'   )
       lowerCamelCase__	:     List[str]								=		model(
           input_ids[:, -1:],	attention_mask=_A,	past_key_values=outputs_cache.past_key_values,	position_ids=_A,	)

       lowerCamelCase__	:     Dict								=		model(_A   )

       lowerCamelCase__	:     List[str]								=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1e-3,	msg=f'''Max diff is {diff}'''   )





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		2_0
       lowerCamelCase__	:     str								=		model_class_name(_A   )

       lowerCamelCase__	:     Optional[Any]								=		jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )],	axis=-1,	)

       lowerCamelCase__	:     Dict								=		model.init_cache(input_ids.shape[0],	_A   )
       lowerCamelCase__	:     Any								=		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :],	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       lowerCamelCase__	:     Union[str, Any]								=		model(
           input_ids[:, :-1],	attention_mask=_A,	past_key_values=_A,	position_ids=_A,	)
       lowerCamelCase__	:     Tuple								=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],	dtype='i4'   )
       lowerCamelCase__	:     List[str]								=		model(
           input_ids[:, -1:],	past_key_values=outputs_cache.past_key_values,	attention_mask=_A,	position_ids=_A,	)

       lowerCamelCase__	:     str								=		model(_A,	attention_mask=_A   )

       lowerCamelCase__	:     List[str]								=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1e-3,	msg=f'''Max diff is {diff}'''   )



@require_flax
class 						a_ (      snake_case_    ,		snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Any					=      (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCamelCase__			:							Dict					=      (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		FlaxGPTNeoModelTester(self   )




      def      a__				(self   ):

       '''simple docstring'''



       for model_class_name in self.all_model_classes:
        lowerCamelCase__	:     Union[str, Any]								=		self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(_A,	_A,	_A,	_A   )




      def      a__				(self   ):

       '''simple docstring'''



       for model_class_name in self.all_model_classes:
        lowerCamelCase__	:     List[str]								=		self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            _A,	_A,	_A,	_A   )




      @slow
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		GPTaTokenizer.from_pretrained('gpt2',	pad_token='<|endoftext|>',	padding_side='left'   )
       lowerCamelCase__	:     str								=		tokenizer(['Hello this is a long string', 'Hey'],	return_tensors='np',	padding=_A,	truncation=_A   )

       lowerCamelCase__	:     List[str]								=		FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'   )
       lowerCamelCase__	:     List[Any]								=		False
       lowerCamelCase__	:     Union[str, Any]								=		model.config.eos_token_id

       lowerCamelCase__	:     Optional[Any]								=		jax.jit(model.generate   )

       lowerCamelCase__	:     List[Any]								=		jit_generate(
           inputs['input_ids'],	attention_mask=inputs['attention_mask'],	pad_token_id=tokenizer.pad_token_id   ).sequences

       lowerCamelCase__	:     Union[str, Any]								=		tokenizer.batch_decode(_A,	skip_special_tokens=_A   )

       lowerCamelCase__	:     Union[str, Any]								=		[
           'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
           'Hey, I\'m a little late to the party. I\'m going to',
       ]

       self.assertListEqual(_A,	_A   )




      @is_pt_flax_cross_test
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
         # prepare inputs
         lowerCamelCase__	:     Any								=		self._prepare_for_class(_A,	_A   )
         lowerCamelCase__	:     int								=		{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         lowerCamelCase__	:     Union[str, Any]								=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowerCamelCase__	:     List[Any]								=		getattr(_A,	_A   )

         lowerCamelCase__	:     List[str]								=		pt_inputs['input_ids'].shape
         lowerCamelCase__	:     int								=		np.random.randint(0,	seq_length - 1,	size=(batch_size,)   )
         for batch_idx, start_index in enumerate(_A   ):
          lowerCamelCase__	:     List[Any]								=		0
          lowerCamelCase__	:     Optional[Any]								=		1
          lowerCamelCase__	:     Any								=		0
          lowerCamelCase__	:     Optional[int]								=		1
         lowerCamelCase__	:     Tuple								=		pt_model_class(_A   ).eval()
         lowerCamelCase__	:     List[Any]								=		model_class(_A,	dtype=jnp.floataa   )

         lowerCamelCase__	:     Optional[Any]								=		convert_pytorch_state_dict_to_flax(pt_model.state_dict(),	_A   )
         lowerCamelCase__	:     Tuple								=		fx_state

         with torch.no_grad():
          lowerCamelCase__	:     Optional[Any]								=		pt_model(**_A   ).to_tuple()

         lowerCamelCase__	:     str								=		fx_model(**_A   ).to_tuple()
         self.assertEqual(len(_A   ),	len(_A   ),	'Output lengths differ between Flax and PyTorch'   )
         for fx_output, pt_output in zip(_A,	_A   ):
          self.assert_almost_equals(fx_output[:, -1],	pt_output[:, -1].numpy(),	4e-2   )

         with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(_A   )
          lowerCamelCase__	:     List[str]								=		model_class.from_pretrained(_A,	from_pt=_A   )

         lowerCamelCase__	:     Union[str, Any]								=		fx_model_loaded(**_A   ).to_tuple()
         self.assertEqual(
             len(_A   ),	len(_A   ),	'Output lengths differ between Flax and PyTorch'   )
         for fx_output_loaded, pt_output in zip(_A,	_A   ):
          self.assert_almost_equals(fx_output_loaded[:, -1],	pt_output[:, -1].numpy(),	4e-2   )




      @is_pt_flax_cross_test
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
         # prepare inputs
         lowerCamelCase__	:     Optional[int]								=		self._prepare_for_class(_A,	_A   )
         lowerCamelCase__	:     Dict								=		{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

         # load corresponding PyTorch class
         lowerCamelCase__	:     List[Any]								=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
         lowerCamelCase__	:     Tuple								=		getattr(_A,	_A   )

         lowerCamelCase__	:     str								=		pt_model_class(_A   ).eval()
         lowerCamelCase__	:     List[Any]								=		model_class(_A,	dtype=jnp.floataa   )

         lowerCamelCase__	:     Optional[Any]								=		load_flax_weights_in_pytorch_model(_A,	fx_model.params   )
         lowerCamelCase__	:     Union[str, Any]								=		pt_inputs['input_ids'].shape
         lowerCamelCase__	:     Dict								=		np.random.randint(0,	seq_length - 1,	size=(batch_size,)   )
         for batch_idx, start_index in enumerate(_A   ):
          lowerCamelCase__	:     int								=		0
          lowerCamelCase__	:     List[str]								=		1
          lowerCamelCase__	:     Optional[int]								=		0
          lowerCamelCase__	:     Tuple								=		1

         # make sure weights are tied in PyTorch
         pt_model.tie_weights()

         with torch.no_grad():
          lowerCamelCase__	:     Optional[Any]								=		pt_model(**_A   ).to_tuple()

         lowerCamelCase__	:     Dict								=		fx_model(**_A   ).to_tuple()
         self.assertEqual(len(_A   ),	len(_A   ),	'Output lengths differ between Flax and PyTorch'   )
         for fx_output, pt_output in zip(_A,	_A   ):
          self.assert_almost_equals(fx_output[:, -1],	pt_output[:, -1].numpy(),	4e-2   )

         with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(_A   )
          lowerCamelCase__	:     str								=		pt_model_class.from_pretrained(_A,	from_flax=_A   )

         with torch.no_grad():
          lowerCamelCase__	:     List[str]								=		pt_model_loaded(**_A   ).to_tuple()

         self.assertEqual(
             len(_A   ),	len(_A   ),	'Output lengths differ between Flax and PyTorch'   )
         for fx_output, pt_output in zip(_A,	_A   ):
          self.assert_almost_equals(fx_output[:, -1],	pt_output[:, -1].numpy(),	4e-2   )





      @slow
      def      a__				(self   ):

       '''simple docstring'''



       for model_class_name in self.all_model_classes:
        lowerCamelCase__	:     List[str]								=		model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'   )
        lowerCamelCase__	:     Union[str, Any]								=		model(np.ones((1, 1)   )   )
        self.assertIsNotNone(_A   )


"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_a	:	int=					180
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							Tuple    )				->     Union[str, Any]:

	'''simple docstring'''





	__snake_case     :				Optional[Any] 						=					k_size // 2
	__snake_case						,							__snake_case     :				Union[str, Any] 						=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	__snake_case     :				List[Any] 						=					(
	    1
	    / (2 * np.pi * sigma)
	    * np.exp(-(np.square(UpperCAmelCase_    ) + np.square(UpperCAmelCase_    )) / (2 * np.square(UpperCAmelCase_    ))    )
	)
	return g
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							Optional[int]    )				->     List[str]:

	'''simple docstring'''





	__snake_case     :				List[Any] 						=					np.zeros(UpperCAmelCase_    )

	for row in range(1     ,				image_shape[0] - 1    ):
		for col in range(1     ,				image_shape[1] - 1    ):
			__snake_case     :				Any 						=					gradient_direction[row, col]

			if (
			    0 <= direction < PI / 8
			    or 15 * PI / 8 <= direction <= 2 * PI
			    or 7 * PI / 8 <= direction <= 9 * PI / 8
			):
				__snake_case     :				str 						=					sobel_grad[row, col - 1]
				__snake_case     :				str 						=					sobel_grad[row, col + 1]
				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
					__snake_case     :				Union[str, Any] 						=					sobel_grad[row, col]

			elif (
			    PI / 8 <= direction < 3 * PI / 8
			    or 9 * PI / 8 <= direction < 11 * PI / 8
			):
				__snake_case     :				Optional[int] 						=					sobel_grad[row + 1, col - 1]
				__snake_case     :				Optional[Any] 						=					sobel_grad[row - 1, col + 1]
				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
					__snake_case     :				Union[str, Any] 						=					sobel_grad[row, col]

			elif (
			    3 * PI / 8 <= direction < 5 * PI / 8
			    or 11 * PI / 8 <= direction < 13 * PI / 8
			):
				__snake_case     :				Union[str, Any] 						=					sobel_grad[row - 1, col]
				__snake_case     :				Optional[int] 						=					sobel_grad[row + 1, col]
				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
					__snake_case     :				int 						=					sobel_grad[row, col]

			elif (
			    5 * PI / 8 <= direction < 7 * PI / 8
			    or 13 * PI / 8 <= direction < 15 * PI / 8
			):
				__snake_case     :				Optional[int] 						=					sobel_grad[row - 1, col - 1]
				__snake_case     :				Union[str, Any] 						=					sobel_grad[row + 1, col + 1]
				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
					__snake_case     :				List[str] 						=					sobel_grad[row, col]

	return destination
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							Optional[Any]     ,				UpperCAmelCase_       :							List[str]     ,				UpperCAmelCase_       :							List[Any]    )				->     Any:

	'''simple docstring'''





	for row in range(1     ,				image_shape[0] - 1    ):
		for col in range(1     ,				image_shape[1] - 1    ):
			if destination[row, col] >= threshold_high:
				__snake_case     :				str 						=					strong
			elif destination[row, col] <= threshold_low:
				__snake_case     :				int 						=					0
			else:
				__snake_case     :				Tuple 						=					weak
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Tuple     ,				UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	for row in range(1     ,				image_shape[0]    ):
		for col in range(1     ,				image_shape[1]    ):
			if destination[row, col] == weak:
				if 2_55 in (
				    destination[row, col + 1],
				    destination[row, col - 1],
				    destination[row - 1, col],
				    destination[row + 1, col],
				    destination[row - 1, col - 1],
				    destination[row + 1, col - 1],
				    destination[row - 1, col + 1],
				    destination[row + 1, col + 1],
				):
					__snake_case     :				Tuple 						=					strong
				else:
					__snake_case     :				Tuple 						=					0
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Any=15     ,				UpperCAmelCase_       :							int=30     ,				UpperCAmelCase_       :							Optional[Any]=1_28     ,				UpperCAmelCase_       :							Dict=2_55    )				->     Optional[Any]:

	'''simple docstring'''





	__snake_case     :				Tuple 						=					img_convolve(UpperCAmelCase_     ,				gen_gaussian_kernel(9     ,				sigma=1.4    )    )
	# get the gradient and degree by sobel_filter
	__snake_case						,							__snake_case     :				str 						=					sobel_filter(UpperCAmelCase_    )
	__snake_case     :				str 						=					PI + np.radadeg(UpperCAmelCase_    )

	__snake_case     :				int 						=					suppress_non_maximum(image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	detect_high_low_threshold(
	    image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	track_edge(image.shape     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	return destination


if __name__ == "__main__":
	# read original image in gray mode
	_a	:	List[Any]=					cva.imread(R"../image_data/lena.jpg", 0)
	# canny edge detection
	_a	:	Optional[int]=					canny(lena)
	cva.imshow("canny", canny_destination)
	cva.waitKey(0)





