



'''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 _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase=14 ,       __UpperCAmelCase=7 ,       __UpperCAmelCase=True ,       __UpperCAmelCase=True ,       __UpperCAmelCase=False ,       __UpperCAmelCase=True ,       __UpperCAmelCase=99 ,       __UpperCAmelCase=32 ,       __UpperCAmelCase=4 ,       __UpperCAmelCase=4 ,       __UpperCAmelCase=[[["global", "local"], 2]] ,       __UpperCAmelCase=37 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=7 ,       __UpperCAmelCase=0.02 ,       )       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     parent
					__UpperCAmelCase   :      int										=     batch_size
					__UpperCAmelCase   :      int										=     seq_length
					__UpperCAmelCase   :      Optional[int]										=     is_training
					__UpperCAmelCase   :      Tuple										=     use_input_mask
					__UpperCAmelCase   :      List[Any]										=     use_token_type_ids
					__UpperCAmelCase   :      Union[str, Any]										=     use_labels
					__UpperCAmelCase   :      int										=     vocab_size
					__UpperCAmelCase   :      Tuple										=     hidden_size
					__UpperCAmelCase   :      Tuple										=     num_hidden_layers
					__UpperCAmelCase   :      Union[str, Any]										=     num_attention_heads
					__UpperCAmelCase   :      Tuple										=     attention_types
					__UpperCAmelCase   :      Optional[int]										=     intermediate_size
					__UpperCAmelCase   :      List[str]										=     hidden_act
					__UpperCAmelCase   :      List[str]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Optional[Any]										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      str										=     max_position_embeddings
					__UpperCAmelCase   :      Union[str, Any]										=     window_size
					__UpperCAmelCase   :      Any										=     initializer_range
					__UpperCAmelCase   :      List[Any]										=     None
					__UpperCAmelCase   :      Optional[int]										=     vocab_size - 1
					__UpperCAmelCase   :      List[Any]										=     vocab_size - 1
					__UpperCAmelCase   :      Optional[Any]										=     vocab_size - 1





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)

					__UpperCAmelCase   :      str										=     None
					if self.use_input_mask:
								__UpperCAmelCase   :      Any										=     random_attention_mask([self.batch_size, self.seq_length]				)

					__UpperCAmelCase   :      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=__UpperCAmelCase ,       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 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.prepare_config_and_inputs()
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Optional[Any]										=     config_and_inputs
					__UpperCAmelCase   :      Tuple										=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     20
					__UpperCAmelCase   :      Any										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase   :      int										=     model.init_cache(input_ids.shape[0] ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     jnp.ones((input_ids.shape[0], max_decoder_length) ,       dtype="""i4"""				)

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

					__UpperCAmelCase   :      int										=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype="""i4"""				)
					__UpperCAmelCase   :      Union[str, Any]										=     model(
					    input_ids[:, -1:] ,       attention_mask=__UpperCAmelCase ,       past_key_values=outputs_cache.past_key_values ,       position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Dict										=     model(__UpperCAmelCase				)

					__UpperCAmelCase   :      Any										=     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 ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     20
					__UpperCAmelCase   :      int										=     model_class_name(__UpperCAmelCase				)

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

					__UpperCAmelCase   :      int										=     model.init_cache(input_ids.shape[0] ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1				)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)				)

					__UpperCAmelCase   :      Union[str, Any]										=     model(
					    input_ids[:, :-1] ,       attention_mask=__UpperCAmelCase ,       past_key_values=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )
					__UpperCAmelCase   :      Optional[Any]										=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype="""i4"""				)
					__UpperCAmelCase   :      Dict										=     model(
					    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=__UpperCAmelCase ,       position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Dict										=     model(__UpperCAmelCase ,       attention_mask=__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     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    ):
		_SCREAMING_SNAKE_CASE					:    Dict						      =			(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		_SCREAMING_SNAKE_CASE					:    Any						      =			(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     FlaxGPTNeoModelTester(self				)





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					for model_class_name in self.all_model_classes:
								__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Union[str, Any]										=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     Optional[int]:

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





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

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     GPTaTokenizer.from_pretrained("""gpt2""" ,       pad_token="""<|endoftext|>""" ,       padding_side="""left"""				)
					__UpperCAmelCase   :      List[str]										=     tokenizer(["""Hello this is a long string""", """Hey"""] ,       return_tensors="""np""" ,       padding=__UpperCAmelCase ,       truncation=__UpperCAmelCase				)

					__UpperCAmelCase   :      List[Any]										=     FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""				)
					__UpperCAmelCase   :      Union[str, Any]										=     False
					__UpperCAmelCase   :      Optional[int]										=     model.config.eos_token_id

					__UpperCAmelCase   :      List[Any]										=     jax.jit(model.generate				)

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

					__UpperCAmelCase   :      Any										=     tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)

					__UpperCAmelCase   :      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(__UpperCAmelCase ,       __UpperCAmelCase				)





		@is_pt_flax_cross_test
		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     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
											__UpperCAmelCase   :      List[str]										=     self._prepare_for_class(__UpperCAmelCase ,       __UpperCAmelCase				)
											__UpperCAmelCase   :      Optional[int]										=     {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

											# load corresponding PyTorch class
											__UpperCAmelCase   :      str										=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
											__UpperCAmelCase   :      Union[str, Any]										=     getattr(__UpperCAmelCase ,       __UpperCAmelCase				)

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

											__UpperCAmelCase   :      List[Any]										=     convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,       __UpperCAmelCase				)
											__UpperCAmelCase   :      str										=     fx_state

											with torch.no_grad():
														__UpperCAmelCase   :      List[Any]										=     pt_model(**__UpperCAmelCase				).to_tuple()

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

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

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





		@is_pt_flax_cross_test
		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[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
											__UpperCAmelCase   :      Union[str, Any]										=     self._prepare_for_class(__UpperCAmelCase ,       __UpperCAmelCase				)
											__UpperCAmelCase   :      Optional[int]										=     {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

											# load corresponding PyTorch class
											__UpperCAmelCase   :      Tuple										=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
											__UpperCAmelCase   :      List[Any]										=     getattr(__UpperCAmelCase ,       __UpperCAmelCase				)

											__UpperCAmelCase   :      Optional[int]										=     pt_model_class(__UpperCAmelCase				).eval()
											__UpperCAmelCase   :      int										=     model_class(__UpperCAmelCase ,       dtype=jnp.floataa				)

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

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

											with torch.no_grad():
														__UpperCAmelCase   :      List[Any]										=     pt_model(**__UpperCAmelCase				).to_tuple()

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

											with tempfile.TemporaryDirectory() as tmpdirname:
														fx_model.save_pretrained(__UpperCAmelCase				)
														__UpperCAmelCase   :      List[str]										=     pt_model_class.from_pretrained(__UpperCAmelCase ,       from_flax=__UpperCAmelCase				)

											with torch.no_grad():
														__UpperCAmelCase   :      int										=     pt_model_loaded(**__UpperCAmelCase				).to_tuple()

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





		@slow
		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					for model_class_name in self.all_model_classes:
								__UpperCAmelCase   :      str										=     model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""				)
								__UpperCAmelCase   :      Tuple										=     model(np.ones((1, 1)				)				)
								self.assertIsNotNone(__UpperCAmelCase				)









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





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

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

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

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     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
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error				)
											self.error_table[y][x + 2] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x] += int(8 / 32 * current_error				)
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					_UpperCamelCase											=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 126, 130, 140)
					]

					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()




