

"""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        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					def __init__( self    :       List[Any]     ,lowercase_    :       List[str]     ,lowercase_    :       Optional[int]=1_4     ,lowercase_    :       str=7     ,lowercase_    :       List[Any]=True     ,lowercase_    :       List[str]=True     ,lowercase_    :       Any=False     ,lowercase_    :       Any=True     ,lowercase_    :       List[str]=9_9     ,lowercase_    :       Optional[Any]=3_2     ,lowercase_    :       int=4     ,lowercase_    :       List[str]=4     ,lowercase_    :       List[str]=[[["global", "local"], 2]]     ,lowercase_    :       Optional[int]=3_7     ,lowercase_    :       List[Any]="gelu"     ,lowercase_    :       str=0.1     ,lowercase_    :       Tuple=0.1     ,lowercase_    :       Dict=5_1_2     ,lowercase_    :       Union[str, Any]=7     ,lowercase_    :       Tuple=0.02     ,):
						lowerCAmelCase__			:		List[str]						       =			parent
						lowerCAmelCase__			:		Optional[Any]						       =			batch_size
						lowerCAmelCase__			:		Union[str, Any]						       =			seq_length
						lowerCAmelCase__			:		Optional[int]						       =			is_training
						lowerCAmelCase__			:		Union[str, Any]						       =			use_input_mask
						lowerCAmelCase__			:		List[Any]						       =			use_token_type_ids
						lowerCAmelCase__			:		int						       =			use_labels
						lowerCAmelCase__			:		Tuple						       =			vocab_size
						lowerCAmelCase__			:		Optional[Any]						       =			hidden_size
						lowerCAmelCase__			:		List[Any]						       =			num_hidden_layers
						lowerCAmelCase__			:		Tuple						       =			num_attention_heads
						lowerCAmelCase__			:		Tuple						       =			attention_types
						lowerCAmelCase__			:		Tuple						       =			intermediate_size
						lowerCAmelCase__			:		Tuple						       =			hidden_act
						lowerCAmelCase__			:		Any						       =			hidden_dropout_prob
						lowerCAmelCase__			:		Optional[Any]						       =			attention_probs_dropout_prob
						lowerCAmelCase__			:		Any						       =			max_position_embeddings
						lowerCAmelCase__			:		Dict						       =			window_size
						lowerCAmelCase__			:		Tuple						       =			initializer_range
						lowerCAmelCase__			:		Dict						       =			None
						lowerCAmelCase__			:		int						       =			vocab_size - 1
						lowerCAmelCase__			:		List[Any]						       =			vocab_size - 1
						lowerCAmelCase__			:		Optional[int]						       =			vocab_size - 1







					def  __lowerCAmelCase				( self    :       Tuple	):
						lowerCAmelCase__			:		List[Any]						       =			ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size	)

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

						lowerCAmelCase__			:		Dict						       =			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=lowercase_     ,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  __lowerCAmelCase				( self    :       Union[str, Any]	):
						lowerCAmelCase__			:		List[str]						       =			self.prepare_config_and_inputs()
						lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__			:		Union[str, Any]						       =			config_and_inputs
						lowerCAmelCase__			:		int						       =			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       Optional[int]     ,lowercase_    :       str     ,lowercase_    :       Any     ,lowercase_    :       Tuple	):
						lowerCAmelCase__			:		str						       =			2_0
						lowerCAmelCase__			:		Dict						       =			model_class_name(lowercase_	)

						lowerCAmelCase__			:		Optional[Any]						       =			model.init_cache(input_ids.shape[0]     ,lowercase_	)
						lowerCAmelCase__			:		Dict						       =			jnp.ones((input_ids.shape[0], max_decoder_length)     ,dtype='''i4'''	)

						lowerCAmelCase__			:		Union[str, Any]						       =			jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1	)[None, :]     ,(input_ids.shape[0], input_ids.shape[-1] - 1)	)
						lowerCAmelCase__			:		Any						       =			model(
						    input_ids[:, :-1]     ,attention_mask=lowercase_     ,past_key_values=lowercase_     ,position_ids=lowercase_     ,)

						lowerCAmelCase__			:		Dict						       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,dtype='''i4'''	)
						lowerCAmelCase__			:		Dict						       =			model(
						    input_ids[:, -1:]     ,attention_mask=lowercase_     ,past_key_values=outputs_cache.past_key_values     ,position_ids=lowercase_     ,)

						lowerCAmelCase__			:		Optional[int]						       =			model(lowercase_	)

						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  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       List[str]     ,lowercase_    :       Optional[Any]     ,lowercase_    :       List[str]     ,lowercase_    :       Union[str, Any]	):
						lowerCAmelCase__			:		List[str]						       =			2_0
						lowerCAmelCase__			:		Union[str, Any]						       =			model_class_name(lowercase_	)

						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]     ,lowercase_	)
						lowerCAmelCase__			:		List[Any]						       =			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=lowercase_     ,past_key_values=lowercase_     ,position_ids=lowercase_     ,)
						lowerCAmelCase__			:		Optional[int]						       =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,dtype='''i4'''	)
						lowerCAmelCase__			:		List[Any]						       =			model(
						    input_ids[:, -1:]     ,past_key_values=outputs_cache.past_key_values     ,attention_mask=lowercase_     ,position_ids=lowercase_     ,)

						lowerCAmelCase__			:		List[str]						       =			model(lowercase_     ,attention_mask=lowercase_	)

						lowerCAmelCase__			:		int						       =			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        SCREAMING_SNAKE_CASE      (	a_					,							a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					lowercase__       				= (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







					def  __lowerCAmelCase				( self    :       Optional[Any]	):
						lowerCAmelCase__			:		Tuple						       =			FlaxGPTNeoModelTester(self	)







					def  __lowerCAmelCase				( self    :       Optional[Any]	):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(lowercase_     ,lowercase_     ,lowercase_     ,lowercase_	)







					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    lowercase_     ,lowercase_     ,lowercase_     ,lowercase_	)







					@slow
					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			GPTaTokenizer.from_pretrained('''gpt2'''     ,pad_token='''<|endoftext|>'''     ,padding_side='''left'''	)
						lowerCAmelCase__			:		Optional[int]						       =			tokenizer(['''Hello this is a long string''', '''Hey''']     ,return_tensors='''np'''     ,padding=lowercase_     ,truncation=lowercase_	)

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

						lowerCAmelCase__			:		Tuple						       =			jax.jit(model.generate	)

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

						lowerCAmelCase__			:		List[str]						       =			tokenizer.batch_decode(lowercase_     ,skip_special_tokens=lowercase_	)

						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(lowercase_     ,lowercase_	)







					@is_pt_flax_cross_test
					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		str						       =			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__			:		List[str]						       =			self._prepare_for_class(lowercase_     ,lowercase_	)
								lowerCAmelCase__			:		Optional[Any]						       =			{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCAmelCase__			:		Any						       =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCAmelCase__			:		Optional[int]						       =			getattr(lowercase_     ,lowercase_	)

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

								lowerCAmelCase__			:		Union[str, Any]						       =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,lowercase_	)
								lowerCAmelCase__			:		Dict						       =			fx_state

								with torch.no_grad():
									lowerCAmelCase__			:		str						       =			pt_model(**lowercase_	).to_tuple()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(lowercase_	)
									lowerCAmelCase__			:		Optional[Any]						       =			model_class.from_pretrained(lowercase_     ,from_pt=lowercase_	)

								lowerCAmelCase__			:		List[str]						       =			fx_model_loaded(**lowercase_	).to_tuple()
								self.assertEqual(
								    len(lowercase_	)     ,len(lowercase_	)     ,'''Output lengths differ between Flax and PyTorch'''	)
								for fx_output_loaded, pt_output in zip(lowercase_     ,lowercase_	):
									self.assert_almost_equals(fx_output_loaded[:, -1]     ,pt_output[:, -1].numpy()     ,4E-2	)







					@is_pt_flax_cross_test
					def  __lowerCAmelCase				( self    :       int	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		Union[str, 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__			:		List[Any]						       =			self._prepare_for_class(lowercase_     ,lowercase_	)
								lowerCAmelCase__			:		Optional[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__			:		Union[str, Any]						       =			getattr(lowercase_     ,lowercase_	)

								lowerCAmelCase__			:		List[str]						       =			pt_model_class(lowercase_	).eval()
								lowerCAmelCase__			:		List[str]						       =			model_class(lowercase_     ,dtype=jnp.floataa	)

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

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

								with torch.no_grad():
									lowerCAmelCase__			:		Union[str, Any]						       =			pt_model(**lowercase_	).to_tuple()

								lowerCAmelCase__			:		Optional[int]						       =			fx_model(**lowercase_	).to_tuple()
								self.assertEqual(len(lowercase_	)     ,len(lowercase_	)     ,'''Output lengths differ between Flax and PyTorch'''	)
								for fx_output, pt_output in zip(lowercase_     ,lowercase_	):
									self.assert_almost_equals(fx_output[:, -1]     ,pt_output[:, -1].numpy()     ,4E-2	)

								with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(lowercase_	)
									lowerCAmelCase__			:		Dict						       =			pt_model_class.from_pretrained(lowercase_     ,from_flax=lowercase_	)

								with torch.no_grad():
									lowerCAmelCase__			:		Optional[Any]						       =			pt_model_loaded(**lowercase_	).to_tuple()

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







					@slow
					def  __lowerCAmelCase				( self    :       Dict	):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__			:		Union[str, Any]						       =			model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''	)
							lowerCAmelCase__			:		List[str]						       =			model(np.ones((1, 1)	)	)
							self.assertIsNotNone(lowercase_	)







"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			zeros_like(A_				)
	for i in range(A_		,  gray_img.shape[0] - bd				):
		for j in range(A_		,  gray_img.shape[1] - bd				):
			# get mask according with mask
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


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

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

							# show result images
							imshow('''median filter with 3x3 mask''', medianaxa)
							imshow('''median filter with 5x5 mask''', medianaxa)
							waitKey(0)




