
'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       "platform"

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=None ,						__UpperCamelCase=None     ):
				if attention_mask is None:
								A_       	=  np.where(input_ids != config.pad_token_id ,						1 ,						0     )
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				}






@require_flax
class 					__lowercase		:


					def __init__(	self       ,       a__       ,       a__=1_3       ,       a__=7       ,       a__=True       ,       a__=False       ,       a__=9_9       ,       a__=1_6       ,       a__=2       ,       a__=4       ,       a__=4       ,       a__="gelu"       ,       a__=0.1       ,       a__=0.1       ,       a__=2_0       ,       a__=2       ,       a__=1       ,       a__=0       ,       a__=1_6       ,       a__=1_6       ,       a__=0.02       ,       )		->							str:





									'''simple docstring'''
									A_       	=  parent
									A_       	=  batch_size
									A_       	=  seq_length
									A_       	=  is_training
									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_       	=  eos_token_id
									A_       	=  pad_token_id
									A_       	=  bos_token_id
									A_       	=  embed_dim
									A_       	=  word_embed_proj_dim
									A_       	=  initializer_range
									A_       	=  False


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size						)       ,       3       ,       self.vocab_size						)
									A_       	=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,       dtype=np.intaa						))       ,       -1						)

									A_       	=  OPTConfig(
									    vocab_size=self.vocab_size       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       ffn_dim=self.intermediate_size       ,       dropout=self.hidden_dropout_prob       ,       attention_dropout=self.attention_probs_dropout_prob       ,       max_position_embeddings=self.max_position_embeddings       ,       eos_token_id=self.eos_token_id       ,       bos_token_id=self.bos_token_id       ,       pad_token_id=self.pad_token_id       ,       embed_dim=self.embed_dim       ,       is_encoder_decoder=lowercase_       ,       word_embed_proj_dim=self.word_embed_proj_dim       ,       initializer_range=self.initializer_range       ,       use_cache=lowercase_       ,       )
									A_       	=  prepare_opt_inputs_dict(lowercase_       ,       lowercase_						)
									return config, inputs_dict


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  self.prepare_config_and_inputs()
									return config, inputs_dict


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Optional[Any]:





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

									A_       	=  inputs_dict['''input_ids''']
									A_       	=  inputs_dict['''attention_mask''']

									A_       	=  model.init_cache(input_ids.shape[0]       ,       lowercase_						)
									A_       	=  jnp.ones((input_ids.shape[0], max_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=lowercase_       ,       past_key_values=lowercase_       ,       position_ids=lowercase_       ,       )

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

									A_       	=  model(lowercase_						)

									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__						)		->							Tuple:





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

									A_       	=  (
									    inputs_dict['''input_ids'''],
									    inputs_dict['''attention_mask'''],
									)

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

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

									A_       	=  model(lowercase_       ,       attention_mask=lowercase_						)

									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		(					__UpperCAmelCase							,       unittest.TestCase							,       __UpperCAmelCase       ):
					__magic_name__						:							Optional[int]     	=	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					__magic_name__						:							Optional[Any]     	=	() if is_flax_available() else ()


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





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


					def        lowerCAmelCase_    (	self						)		->							Dict:





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


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





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


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





									'''simple docstring'''

									for model_class_name in self.all_model_classes:
													A_       	=  model_class_name.from_pretrained('''facebook/opt-125m'''						)
													A_       	=  np.ones((1, 1)						) * model.config.eos_token_id
													A_       	=  model(lowercase_						)
													self.assertIsNotNone(lowercase_						)






@require_sentencepiece
@require_flax
class 					__lowercase		(					unittest.TestCase       ):


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





									'''simple docstring'''
									A_       	=  FlaxOPTModel.from_pretrained('''facebook/opt-350m'''						)
									A_       	=  jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
									A_       	=  model(input_ids=lowercase_						).last_hidden_state
									A_       	=  (1, 1_1, 5_1_2)
									self.assertEqual(output.shape       ,       lowercase_						)
									A_       	=  jnp.array(
									    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]						)
									self.assertTrue(jnp.allclose(output[:, :3, :3]       ,       lowercase_       ,       atol=4E-2						)						)






@require_flax
@slow
class 					__lowercase		(					unittest.TestCase       ):


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





									'''simple docstring'''
									super().setUp()
									A_       	=  '''facebook/opt-350m'''


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





									'''simple docstring'''
									A_       	=  FlaxOPTForCausalLM.from_pretrained(self.path_model						)
									A_       	=  GPTaTokenizer.from_pretrained(self.path_model						)

									A_       	=  [
									    '''Today is a beautiful day and I want to''',
									    '''In the city of''',
									    '''Paris is the capital of France and''',
									    '''Computers and mobile phones have taken''',
									]
									# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
									A_       	=  tokenizer(lowercase_       ,       return_tensors='''jax'''       ,       padding=lowercase_       ,       add_special_tokens=lowercase_						)
									A_       	=  model(inputs.input_ids       ,       attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
									A_       	=  jnp.array(
									    [
									        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
									        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
									        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
									        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
									    ]						)
									self.assertTrue(jnp.allclose(lowercase_       ,       lowercase_       ,       atol=4E-2						)						)

									A_       	=  jax.jit(lowercase_						)
									A_       	=  model(inputs.input_ids       ,       attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
									self.assertTrue(jnp.allclose(lowercase_       ,       lowercase_       ,       atol=4E-2						)						)






@require_flax
@slow
class 					__lowercase		(					unittest.TestCase       ):


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





									'''simple docstring'''
									return [
									    "Today is a beautiful day and I want",
									    "In the city of",
									    "Paris is the capital of France and",
									    "Computers and mobile phones have taken",
									]


					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									A_       	=  '''facebook/opt-125m'''

									A_       	=  [
									    '''Today is a beautiful day and I want to''',
									    '''In the city of New York, the city''',
									    '''Paris is the capital of France and the capital''',
									    '''Computers and mobile phones have taken over the''',
									]

									A_       	=  []

									A_       	=  FlaxOPTForCausalLM.from_pretrained(lowercase_						)
									A_       	=  GPTaTokenizer.from_pretrained(lowercase_						)

									for prompt in self.prompts:
													A_       	=  tokenizer(lowercase_       ,       return_tensors='''jax'''						).input_ids

													A_       	=  model.generate(lowercase_       ,       max_length=1_0						)
													A_       	=  generated_ids[0]

													A_       	=  tokenizer.batch_decode(lowercase_       ,       skip_special_tokens=lowercase_						)
													predicted_outputs += generated_string

									self.assertListEqual(lowercase_       ,       lowercase_						)


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  '''facebook/opt-350m'''

									A_       	=  [
									    '''Today is a beautiful day and I want to''',
									    '''In the city of San Francisco, the city''',
									    '''Paris is the capital of France and the capital''',
									    '''Computers and mobile phones have taken over the''',
									]

									A_       	=  []
									A_       	=  FlaxOPTForCausalLM.from_pretrained(lowercase_						)
									A_       	=  GPTaTokenizer.from_pretrained(lowercase_						)

									for prompt in self.prompts:
													A_       	=  tokenizer(lowercase_       ,       return_tensors='''jax'''						).input_ids

													A_       	=  model.generate(lowercase_       ,       max_length=1_0						)
													A_       	=  generated_ids[0]

													A_       	=  tokenizer.batch_decode(lowercase_       ,       skip_special_tokens=lowercase_						)
													predicted_outputs += generated_string

									self.assertListEqual(lowercase_       ,       lowercase_						)


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  '''facebook/opt-125m'''
									A_       	=  [
									    '''Today is a beautiful day and I want to thank''',
									    '''In the city of Rome Canaver Canaver Canaver Canaver''',
									]
									A_       	=  FlaxOPTForCausalLM.from_pretrained(lowercase_						)
									A_       	=  GPTaTokenizer.from_pretrained(lowercase_						)
									A_       	=  tokenizer(
									    [
									        '''Today is a beautiful day and I want to''',
									        '''In the city of''',
									    ]       ,       return_tensors='''jax'''       ,       padding=lowercase_       ,       )

									A_       	=  jax.jit(model.generate						)

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

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

									self.assertIsNotNone(lowercase_       ,       lowercase_						)


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  '''facebook/opt-350m'''

									A_       	=  GPTaTokenizer.from_pretrained(lowercase_						)
									A_       	=  FlaxOPTForCausalLM.from_pretrained(lowercase_						)

									A_       	=  '''left'''

									# use different length sentences to test batching
									A_       	=  [
									    '''Hello, my dog is a little''',
									    '''Today, I''',
									]

									A_       	=  tokenizer(lowercase_       ,       return_tensors='''jax'''       ,       padding=lowercase_						)
									A_       	=  inputs['''input_ids''']

									A_       	=  model.generate(input_ids=lowercase_       ,       attention_mask=inputs['''attention_mask''']       ,       trace=lowercase_						)

									A_       	=  tokenizer(sentences[0]       ,       return_tensors='''jax'''						).input_ids
									A_       	=  model.generate(input_ids=lowercase_						)

									A_       	=  inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
									A_       	=  tokenizer(sentences[1]       ,       return_tensors='''jax'''						).input_ids
									A_       	=  model.generate(input_ids=lowercase_       ,       max_length=model.config.max_length - num_paddings						)

									A_       	=  tokenizer.batch_decode(outputs[0]       ,       skip_special_tokens=lowercase_						)
									A_       	=  tokenizer.decode(output_non_padded[0][0]       ,       skip_special_tokens=lowercase_						)
									A_       	=  tokenizer.decode(output_padded[0][0]       ,       skip_special_tokens=lowercase_						)

									A_       	=  [
									    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
									    '''Today, I was in the middle of a conversation with a friend about the''',
									]
									self.assertListEqual(lowercase_       ,       lowercase_						)
									self.assertListEqual(lowercase_       ,       [non_padded_sentence, padded_sentence]						)

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()