
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 					__lowercase		:


					def __init__(	self       ,       a__       ,       a__=1_4       ,       a__=7       ,       a__=True       ,       a__=True       ,       a__=False       ,       a__=True       ,       a__=9_9       ,       a__=3_2       ,       a__=5       ,       a__=4       ,       a__=3_7       ,       a__="gelu"       ,       a__=0.1       ,       a__=0.1       ,       a__=5_1_2       ,       a__=0.02       ,       )		->							Any:





									'''simple docstring'''
									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        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									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=a__       ,       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						)		->							Dict:





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


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_	,					A_	,					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        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__						)		->							Dict:





									'''simple docstring'''
									A_       	=  2_0
									A_       	=  model_class_name(a__						)

									A_       	=  model.init_cache(input_ids.shape[0]       ,       a__						)
									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=a__       ,       past_key_values=a__       ,       position_ids=a__       ,       )

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

									A_       	=  model(a__						)

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





									'''simple docstring'''
									A_       	=  2_0
									A_       	=  model_class_name(a__						)

									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]       ,       a__						)
									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=a__       ,       past_key_values=a__       ,       position_ids=a__       ,       )
									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=a__       ,       position_ids=a__       ,       )

									A_       	=  model(a__       ,       attention_mask=a__						)

									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 					__lowercase		(					A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							Dict     	=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
					__magic_name__						:							Any     	=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  FlaxGPTaModelTester(self						)


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									for model_class_name in self.all_model_classes:
													A_	,					A_	,					A_       	=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(a__       ,       a__       ,       a__       ,       a__						)


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									for model_class_name in self.all_model_classes:
													A_	,					A_	,					A_       	=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    a__       ,       a__       ,       a__       ,       a__						)


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





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

									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(a__       ,       skip_special_tokens=a__						)

									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(a__       ,       a__						)


					@is_pt_flax_cross_test
					def        lowerCAmelCase_    (	self						)		->							Optional[Any]:





									'''simple docstring'''
									A_	,					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(a__       ,       a__						)
																	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(a__       ,       a__						)

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

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

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

																	A_       	=  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__						)
																					A_       	=  model_class.from_pretrained(a__       ,       from_pt=a__						)

																	A_       	=  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        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_	,					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(a__       ,       a__						)
																	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(a__       ,       a__						)

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

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

																	A_       	=  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__						)
																					A_       	=  pt_model_class.from_pretrained(a__       ,       from_flax=a__						)

																	with torch.no_grad():
																					A_       	=  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        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									for model_class_name in self.all_model_classes:
													A_       	=  model_class_name.from_pretrained('''gpt2'''       ,       from_pt=a__						)
													A_       	=  model(np.ones((1, 1)						)						)
													self.assertIsNotNone(a__						)

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

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

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    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()