


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       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Tuple      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Optional[Any]=1_4      ,			_lowerCAmelCase    :    List[str]=7      ,			_lowerCAmelCase    :    Tuple=True      ,			_lowerCAmelCase    :    List[str]=True      ,			_lowerCAmelCase    :    List[Any]=False      ,			_lowerCAmelCase    :    str=True      ,			_lowerCAmelCase    :    Optional[int]=9_9      ,			_lowerCAmelCase    :    Dict=3_2      ,			_lowerCAmelCase    :    Optional[Any]=5      ,			_lowerCAmelCase    :    int=4      ,			_lowerCAmelCase    :    Dict=3_7      ,			_lowerCAmelCase    :    int="gelu"      ,			_lowerCAmelCase    :    List[Any]=0.1      ,			_lowerCAmelCase    :    Any=0.1      ,			_lowerCAmelCase    :    Dict=5_1_2      ,			_lowerCAmelCase    :    List[str]=0.02      ,			)				->	int:




     """simple docstring"""
     snake_case_         =   parent
     snake_case_         =   batch_size
     snake_case_         =   seq_length
     snake_case_         =   is_training
     snake_case_         =   use_input_mask
     snake_case_         =   use_token_type_ids
     snake_case_         =   use_labels
     snake_case_         =   vocab_size
     snake_case_         =   hidden_size
     snake_case_         =   num_hidden_layers
     snake_case_         =   num_attention_heads
     snake_case_         =   intermediate_size
     snake_case_         =   hidden_act
     snake_case_         =   hidden_dropout_prob
     snake_case_         =   attention_probs_dropout_prob
     snake_case_         =   max_position_embeddings
     snake_case_         =   initializer_range
     snake_case_         =   None
     snake_case_         =   vocab_size - 1
     snake_case_         =   vocab_size - 1
     snake_case_         =   vocab_size - 1



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




     """simple docstring"""
     snake_case_         =   ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size					)

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

     snake_case_         =   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=_lowerCAmelCase      ,			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    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   self.prepare_config_and_inputs()
     snake_case_      ,      snake_case_      ,      snake_case_         =   config_and_inputs
     snake_case_         =   {"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict



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




     """simple docstring"""
     snake_case_      ,      snake_case_      ,      snake_case_         =   self.prepare_config_and_inputs()

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

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )



  def    lowerCAmelCase__					(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    List[str]					)				->	Optional[Any]:




     """simple docstring"""
     snake_case_         =   2_0
     snake_case_         =   model_class_name(_lowerCAmelCase					)

     snake_case_         =   model.init_cache(input_ids.shape[0]      ,			_lowerCAmelCase					)
     snake_case_         =   jnp.ones((input_ids.shape[0], max_decoder_length)      ,			dtype="i4"					)

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

     snake_case_         =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,			dtype="i4"					)
     snake_case_         =   model(
         input_ids[:, -1:]      ,			attention_mask=_lowerCAmelCase      ,			past_key_values=outputs_cache.past_key_values      ,			position_ids=_lowerCAmelCase      ,			)

     snake_case_         =   model(_lowerCAmelCase					)

     snake_case_         =   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    :    Optional[Any]      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    List[str]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    str					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   2_0
     snake_case_         =   model_class_name(_lowerCAmelCase					)

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

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

     snake_case_         =   model(
         input_ids[:, :-1]      ,			attention_mask=_lowerCAmelCase      ,			past_key_values=_lowerCAmelCase      ,			position_ids=_lowerCAmelCase      ,			)
     snake_case_         =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,			dtype="i4"					)
     snake_case_         =   model(
         input_ids[:, -1:]      ,			past_key_values=outputs_cache.past_key_values      ,			attention_mask=_lowerCAmelCase      ,			position_ids=_lowerCAmelCase      ,			)

     snake_case_         =   model(_lowerCAmelCase      ,			attention_mask=_lowerCAmelCase					)

     snake_case_         =   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       __lowerCAmelCase       (	a	,					a	,					unittest.TestCase   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  _SCREAMING_SNAKE_CASE						      =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()



  def    lowerCAmelCase__					(       self    :    Dict					)				->	int:




     """simple docstring"""
     snake_case_         =   FlaxGPTaModelTester(self					)



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




     """simple docstring"""
     for model_class_name in self.all_model_classes:
        snake_case_      ,      snake_case_      ,      snake_case_         =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	List[str]:




     """simple docstring"""
     for model_class_name in self.all_model_classes:
        snake_case_      ,      snake_case_      ,      snake_case_         =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)



  @slow
  def    lowerCAmelCase__					(       self    :    Tuple					)				->	int:




     """simple docstring"""
     snake_case_         =   GPTaTokenizer.from_pretrained("gpt2"      ,			pad_token="</s>"      ,			padding_side="left"					)
     snake_case_         =   tokenizer(["Hello this is a long string", "Hey"]      ,			return_tensors="np"      ,			padding=_lowerCAmelCase      ,			truncation=_lowerCAmelCase					)

     snake_case_         =   FlaxGPTaLMHeadModel.from_pretrained("gpt2"					)
     snake_case_         =   False
     snake_case_         =   model.config.eos_token_id

     snake_case_         =   jax.jit(model.generate					)

     snake_case_         =   jit_generate(inputs["input_ids"]      ,			attention_mask=inputs["attention_mask"]					).sequences

     snake_case_         =   tokenizer.batch_decode(_lowerCAmelCase      ,			skip_special_tokens=_lowerCAmelCase					)

     snake_case_         =   [
         "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(_lowerCAmelCase      ,			_lowerCAmelCase					)



  @is_pt_flax_cross_test
  def    lowerCAmelCase__					(       self    :    List[str]					)				->	str:




     """simple docstring"""
     snake_case_      ,      snake_case_         =   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
           snake_case_         =   self._prepare_for_class(_lowerCAmelCase      ,			_lowerCAmelCase					)
           snake_case_         =   {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

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

           snake_case_         =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,			_lowerCAmelCase					)
           snake_case_         =   fx_state

           with torch.no_grad():
              snake_case_         =   pt_model(**_lowerCAmelCase					).to_tuple()

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

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

           snake_case_         =   fx_model_loaded(**_lowerCAmelCase					).to_tuple()
           self.assertEqual(
               len(_lowerCAmelCase					)      ,			len(_lowerCAmelCase					)      ,			"Output lengths differ between Flax and PyTorch"					)
           for fx_output_loaded, pt_output in zip(_lowerCAmelCase      ,			_lowerCAmelCase					):
              self.assert_almost_equals(fx_output_loaded[:, -1]      ,			pt_output[:, -1].numpy()      ,			4e-2					)



  @is_pt_flax_cross_test
  def    lowerCAmelCase__					(       self    :    Dict					)				->	str:




     """simple docstring"""
     snake_case_      ,      snake_case_         =   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
           snake_case_         =   self._prepare_for_class(_lowerCAmelCase      ,			_lowerCAmelCase					)
           snake_case_         =   {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

           snake_case_         =   pt_model_class(_lowerCAmelCase					).eval()
           snake_case_         =   model_class(_lowerCAmelCase      ,			dtype=jnp.floataa					)

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

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

           with torch.no_grad():
              snake_case_         =   pt_model(**_lowerCAmelCase					).to_tuple()

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

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

           with torch.no_grad():
              snake_case_         =   pt_model_loaded(**_lowerCAmelCase					).to_tuple()

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



  @slow
  def    lowerCAmelCase__					(       self    :    str					)				->	List[str]:




     """simple docstring"""
     for model_class_name in self.all_model_classes:
        snake_case_         =   model_class_name.from_pretrained("gpt2"      ,			from_pt=_lowerCAmelCase					)
        snake_case_         =   model(np.ones((1, 1)					)					)
        self.assertIsNotNone(_lowerCAmelCase					)








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   0
     # max greyscale value for #FFFFFF
     snake_case_         =   int(self.get_greyscale(2_5_5      ,			2_5_5      ,			2_5_5					)					)

     if not self.min_threshold < threshold < self.max_threshold:
        snake_case_         =   F'''Factor value should be from 0 to {self.max_threshold}'''
        raise ValueError(_lowerCAmelCase					)

     snake_case_         =   input_img
     snake_case_         =   threshold
     snake_case_      ,      snake_case_         =   self.input_img.shape[1], self.input_img.shape[0]

     # error table size (+4 columns and +1 row) greater than input image because of
     # lack of if statements
     snake_case_         =   [
         [0 for _ in range(self.height + 4					)] for __ in range(self.width + 1					)
     ]
     snake_case_         =   np.ones((self.width, self.height, 3)      ,			np.uinta					) * 2_5_5



  @classmethod
  def    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	None:




     """simple docstring"""
     for y in range(self.height					):
        for x in range(self.width					):
           snake_case_         =   int(self.get_greyscale(*self.input_img[y][x]					)					)
           if self.threshold > greyscale + self.error_table[y][x]:
              snake_case_         =   (0, 0, 0)
              snake_case_         =   greyscale + self.error_table[x][y]
           else:
              snake_case_         =   (2_5_5, 2_5_5, 2_5_5)
              snake_case_         =   greyscale + self.error_table[x][y] - 2_5_5
           self.error_table[y][x + 1] += int(8 / 3_2 * current_error					)
           self.error_table[y][x + 2] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x] += int(8 / 3_2 * current_error					)
           self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error					)
           self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error					)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   SCREAMING_SNAKE_CASE		:Dict											=       [
       Burkes(imread('''image_data/lena.jpg''', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   for burkes in burkes_instances:
      burkes.process()

   for burkes in burkes_instances:
      imshow(
          F'''Original image with dithering threshold: {burkes.threshold}''',
          burkes.output_img,
      )

   waitKey(0)
   destroyAllWindows()




