


'''simple docstring'''






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch




class 			a  :
 def __init__(			self   ,					__magic_name__   ,					__magic_name__=14   ,					__magic_name__=7   ,					__magic_name__=True   ,					__magic_name__=True   ,					__magic_name__=False   ,					__magic_name__=True   ,					__magic_name__=99   ,					__magic_name__=32   ,					__magic_name__=5   ,					__magic_name__=4   ,					__magic_name__=37   ,					__magic_name__="gelu"   ,					__magic_name__=0.1   ,					__magic_name__=0.1   ,					__magic_name__=5_12   ,					__magic_name__=0.0_2   ,					)       ->				List[str]:
      _a         =				parent
      _a         =				batch_size
      _a         =				seq_length
      _a         =				is_training
      _a         =				use_input_mask
      _a         =				use_token_type_ids
      _a         =				use_labels
      _a         =				vocab_size
      _a         =				hidden_size
      _a         =				num_hidden_layers
      _a         =				num_attention_heads
      _a         =				intermediate_size
      _a         =				hidden_act
      _a         =				hidden_dropout_prob
      _a         =				attention_probs_dropout_prob
      _a         =				max_position_embeddings
      _a         =				initializer_range
      _a         =				None
      _a         =				vocab_size - 1
      _a         =				vocab_size - 1
      _a         =				vocab_size - 1
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size    )

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

      _a         =				GPTaConfig(
          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=__magic_name__   ,					bos_token_id=self.bos_token_id   ,					eos_token_id=self.eos_token_id   ,					pad_token_id=self.pad_token_id   ,					)

      return (config, input_ids, input_mask)
 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				self.prepare_config_and_inputs()
      _a         =				config_and_inputs
      _a         =				{'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict
 def 				__UpperCAmelCase		(			self    )       ->				int:
      _a         =				self.prepare_config_and_inputs()

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

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				List[Any]:
      _a         =				20
      _a         =				model_class_name(__magic_name__    )

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

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

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

      _a         =				model(__magic_name__    )

      _a         =				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 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				Any:
      _a         =				20
      _a         =				model_class_name(__magic_name__    )

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

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

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

      _a         =				model(__magic_name__   ,					attention_mask=__magic_name__    )

      _a         =				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  (  _SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					unittest.TestCase							):
 _lowerCAmelCase			       =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 _lowerCAmelCase			       =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a         =				FlaxGPTaModelTester(self    )
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:




      for model_class_name in self.all_model_classes:
           _a         =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:




      for model_class_name in self.all_model_classes:
           _a         =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               __magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )
 @slow
 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				GPTaTokenizer.from_pretrained('gpt2'   ,					pad_token='</s>'   ,					padding_side='left'    )
      _a         =				tokenizer(['Hello this is a long string', 'Hey']   ,					return_tensors='np'   ,					padding=__magic_name__   ,					truncation=__magic_name__    )

      _a         =				FlaxGPTaLMHeadModel.from_pretrained('gpt2'    )
      _a         =				False
      _a         =				model.config.eos_token_id

      _a         =				jax.jit(model.generate    )

      _a         =				jit_generate(inputs['input_ids']   ,					attention_mask=inputs['attention_mask']    ).sequences

      _a         =				tokenizer.batch_decode(__magic_name__   ,					skip_special_tokens=__magic_name__    )

      _a         =				[
          'Hello this is a long string of words. I\'m going to start with the first one.\n',
          'Hey, I\'m not sure if I\'m going to be able to do',
      ]

      self.assertListEqual(__magic_name__   ,					__magic_name__    )
 @is_pt_flax_cross_test
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a         =				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
                _a         =				self._prepare_for_class(__magic_name__   ,					__magic_name__    )
                _a         =				{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

                _a         =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,					__magic_name__    )
                _a         =				fx_state

                with torch.no_grad():
                     _a         =				pt_model(**__magic_name__    ).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(__magic_name__    )
                     _a         =				model_class.from_pretrained(__magic_name__   ,					from_pt=__magic_name__    )

                _a         =				fx_model_loaded(**__magic_name__    ).to_tuple()
                self.assertEqual(
                    len(__magic_name__    )   ,					len(__magic_name__    )   ,					'Output lengths differ between Flax and PyTorch'    )
                for fx_output_loaded, pt_output in zip(__magic_name__   ,					__magic_name__    ):
                     self.assert_almost_equals(fx_output_loaded[:, -1]   ,					pt_output[:, -1].numpy()   ,					4e-2    )
 @is_pt_flax_cross_test
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				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
                _a         =				self._prepare_for_class(__magic_name__   ,					__magic_name__    )
                _a         =				{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                _a         =				pt_model_class(__magic_name__    ).eval()
                _a         =				model_class(__magic_name__   ,					dtype=jnp.floataa    )

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

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

                with torch.no_grad():
                     _a         =				pt_model(**__magic_name__    ).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(__magic_name__    )
                     _a         =				pt_model_class.from_pretrained(__magic_name__   ,					from_flax=__magic_name__    )

                with torch.no_grad():
                     _a         =				pt_model_loaded(**__magic_name__    ).to_tuple()

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




 @slow
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:




      for model_class_name in self.all_model_classes:
           _a         =				model_class_name.from_pretrained('gpt2'   ,					from_pt=__magic_name__    )
           _a         =				model(np.ones((1, 1)    )    )
           self.assertIsNotNone(__magic_name__    )









"""simple docstring"""


from cva import destroyAllWindows, imread, imshow, waitKey




def      snake_case    (A_ :Optional[int]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Tuple							=  img.shape[0], img.shape[1]

  def to_grayscale(A_ :Tuple      ,     A_ :List[str]      ,     A_ :Union[str, Any] ):
    return 0.2126 * red + 0.587 * green + 0.114 * blue

  def normalize(A_ :Union[str, Any] ):
    return min(A_      ,     2_5_5 )

  for i in range(A_ ):
    for j in range(A_ ):
      a	:     Union[str, Any]							=  int(to_grayscale(*img[i][j] ) )
      a	:     Any							=  [
          normalize(A_ ),
          normalize(greyscale + factor ),
          normalize(greyscale + 2 * factor ),
      ]

  return img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase							:       List[str]       					=		{
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()




