




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch







class 						A:



 '''simple docstring'''


 def __init__(       self      :						Any		,							A_      :						Union[str, Any]		,							A_      :						Optional[int]=14		,							A_      :						List[str]=7		,							A_      :						Any=True		,							A_      :						Dict=True		,							A_      :						Union[str, Any]=False		,							A_      :						Any=True		,							A_      :						Optional[Any]=99		,							A_      :						str=32		,							A_      :						List[str]=4		,							A_      :						Union[str, Any]=4		,							A_      :						Union[str, Any]=4		,							A_      :						int=37		,							A_      :						List[str]="gelu"		,							A_      :						str=0.1		,							A_      :						List[str]=0.1		,							A_      :						List[str]=512		,							A_      :						Union[str, Any]=0.02		,							)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					seq_length
       lowerCamelCase_	  =					is_training
       lowerCamelCase_	  =					use_input_mask
       lowerCamelCase_	  =					use_token_type_ids
       lowerCamelCase_	  =					use_labels
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					rotary_dim
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					None
       lowerCamelCase_	  =					vocab_size - 1
       lowerCamelCase_	  =					vocab_size - 1
       lowerCamelCase_	  =					vocab_size - 1

 def        a__				(       self      :						Union[str, Any]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size						)

       lowerCamelCase_	  =					None
       if self.use_input_mask:
             lowerCamelCase_	  =					random_attention_mask([self.batch_size, self.seq_length]						)

       lowerCamelCase_	  =					GPTJConfig(
           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		,							use_cache=A_		,							bos_token_id=self.bos_token_id		,							eos_token_id=self.eos_token_id		,							pad_token_id=self.pad_token_id		,							rotary_dim=self.rotary_dim		,							)

       return (config, input_ids, input_mask)

 def        a__				(       self      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					config_and_inputs
       lowerCamelCase_	  =					{'input_ids': input_ids, 'attention_mask': attention_mask}
       return config, inputs_dict

 def        a__				(       self      :						Any		,							A_      :						List[Any]		,							A_      :						List[str]		,							A_      :						Dict		,							A_      :						Tuple						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					20
       lowerCamelCase_	  =					model_class_name(A_						)

       lowerCamelCase_	  =					model.init_cache(input_ids.shape[0]		,							A_						)
       lowerCamelCase_	  =					jnp.ones((input_ids.shape[0], max_decoder_length)		,							dtype='i4'						)

       lowerCamelCase_	  =					jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]		,							(input_ids.shape[0], input_ids.shape[-1] - 1)						)
       lowerCamelCase_	  =					model(
           input_ids[:, :-1]		,							attention_mask=A_		,							past_key_values=A_		,							position_ids=A_		,							)

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

       lowerCamelCase_	  =					model(A_						)

       lowerCamelCase_	  =					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      :						int		,							A_      :						str		,							A_      :						str		,							A_      :						int		,							A_      :						Union[str, Any]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					20
       lowerCamelCase_	  =					model_class_name(A_						)

       lowerCamelCase_	  =					jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]		,							axis=-1		,							)

       lowerCamelCase_	  =					model.init_cache(input_ids.shape[0]		,							A_						)
       lowerCamelCase_	  =					jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]		,							(input_ids.shape[0], input_ids.shape[-1] - 1)						)

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

       lowerCamelCase_	  =					model(A_		,							attention_mask=A_						)

       lowerCamelCase_	  =					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(		lowercase__							,   lowercase__							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCamelCase		     =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()

 def        a__				(       self      :						List[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					FlaxGPTJModelTester(self						)

 def        a__				(       self      :						List[str]						)     ->			Tuple:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(A_		,							A_		,							A_		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 A_		,							A_		,							A_		,							A_						)

 @tooslow
 def        a__				(       self      :						Dict						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					GPTaTokenizer.from_pretrained('gpt2'		,							pad_token='<|endoftext|>'		,							padding_side='left'						)
       lowerCamelCase_	  =					tokenizer(['Hello this is a long string', 'Hey']		,							return_tensors='np'		,							padding=A_		,							truncation=A_						)

       lowerCamelCase_	  =					FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'						)
       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					model.config.eos_token_id

       lowerCamelCase_	  =					jax.jit(model.generate						)

       lowerCamelCase_	  =					jit_generate(
           inputs['input_ids']		,							attention_mask=inputs['attention_mask']		,							pad_token_id=tokenizer.pad_token_id						).sequences

       lowerCamelCase_	  =					tokenizer.batch_decode(A_		,							skip_special_tokens=A_						)

       lowerCamelCase_	  =					[
           '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      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					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_	  =					self._prepare_for_class(A_		,							A_						)
                   lowerCamelCase_	  =					{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   lowerCamelCase_	  =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   lowerCamelCase_	  =					getattr(A_		,							A_						)

                   lowerCamelCase_  ,       lowerCamelCase_	  =					pt_inputs['input_ids'].shape
                   lowerCamelCase_	  =					np.random.randint(0		,							seq_length - 1		,							size=(batch_size,)						)
                   for batch_idx, start_index in enumerate(A_						):
                         lowerCamelCase_	  =					0
                         lowerCamelCase_	  =					1
                         lowerCamelCase_	  =					0
                         lowerCamelCase_	  =					1
                   lowerCamelCase_	  =					pt_model_class(A_						).eval()
                   lowerCamelCase_	  =					model_class(A_		,							dtype=jnp.floataa						)

                   lowerCamelCase_	  =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,							A_						)
                   lowerCamelCase_	  =					fx_state

                   with torch.no_grad():
                         lowerCamelCase_	  =					pt_model(**A_						).to_tuple()

                   lowerCamelCase_	  =					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_	  =					model_class.from_pretrained(A_		,							from_pt=A_						)

                   lowerCamelCase_	  =					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      :						Any						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					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_	  =					self._prepare_for_class(A_		,							A_						)
                   lowerCamelCase_	  =					{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   lowerCamelCase_	  =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   lowerCamelCase_	  =					getattr(A_		,							A_						)

                   lowerCamelCase_	  =					pt_model_class(A_						).eval()
                   lowerCamelCase_	  =					model_class(A_		,							dtype=jnp.floataa						)

                   lowerCamelCase_	  =					load_flax_weights_in_pytorch_model(A_		,							fx_model.params						)
                   lowerCamelCase_  ,       lowerCamelCase_	  =					pt_inputs['input_ids'].shape
                   lowerCamelCase_	  =					np.random.randint(0		,							seq_length - 1		,							size=(batch_size,)						)
                   for batch_idx, start_index in enumerate(A_						):
                         lowerCamelCase_	  =					0
                         lowerCamelCase_	  =					1
                         lowerCamelCase_	  =					0
                         lowerCamelCase_	  =					1

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

                   with torch.no_grad():
                         lowerCamelCase_	  =					pt_model(**A_						).to_tuple()

                   lowerCamelCase_	  =					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_	  =					pt_model_class.from_pretrained(A_		,							from_flax=A_						)

                   with torch.no_grad():
                         lowerCamelCase_	  =					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						)





 @tooslow
 def        a__				(       self      :						Tuple						)     ->			Any:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_	  =					model_class_name.from_pretrained('EleutherAI/gpt-j-6B'						)
             lowerCamelCase_	  =					model(np.ones((1, 1)						)						)
             self.assertIsNotNone(A_						)









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       if k in (0.04, 0.06):
             lowerCamelCase_	  =					k
             lowerCamelCase_	  =					window_size
       else:
             raise ValueError('invalid k value'						)

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






       lowerCamelCase_	  =					cva.imread(A_		,							0						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					img.shape
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					img.copy()
       lowerCamelCase_	  =					cva.cvtColor(A_		,							cva.COLOR_GRAY2RGB						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					np.gradient(A_						)
       lowerCamelCase_	  =					dx**2
       lowerCamelCase_	  =					dy**2
       lowerCamelCase_	  =					dx * dy
       lowerCamelCase_	  =					0.04
       lowerCamelCase_	  =					self.window_size // 2
       for y in range(A_		,							h - offset						):
             for x in range(A_		,							w - offset						):
                   lowerCamelCase_	  =					ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)		,							0						)
                         color_img.itemset((y, x, 1)		,							0						)
                         color_img.itemset((y, x, 2)		,							255						)
       return color_img, corner_list


if __name__ == "__main__":
 lowerCamelCase       :	Optional[int]									=     HarrisCorner(0.04, 3)
 lowerCamelCase					,					lowerCamelCase       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



