




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
				lowercase_		=							'''platform'''

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=None,	UpperCAmelCase=None       )  ->List[str]:






					"""simple docstring"""






					if attention_mask is None:
										__magic_name__					:							List[str]		   =							np.where(input_ids != config.pad_token_id,	1,	0       )
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}





@require_flax
class 		A__				:






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase=13				,     lowerCamelCase=7				,     lowerCamelCase=True				,     lowerCamelCase=False				,     lowerCamelCase=99				,     lowerCamelCase=16				,     lowerCamelCase=2				,     lowerCamelCase=4				,     lowerCamelCase=4				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0.1				,     lowerCamelCase=20				,     lowerCamelCase=2				,     lowerCamelCase=1				,     lowerCamelCase=0				,     lowerCamelCase=16				,     lowerCamelCase=16				,     lowerCamelCase=0.0_2				,     )      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Any		   =							parent
						__magic_name__					:							Tuple		   =							batch_size
						__magic_name__					:							Optional[int]		   =							seq_length
						__magic_name__					:							List[str]		   =							is_training
						__magic_name__					:							Optional[int]		   =							use_labels
						__magic_name__					:							List[str]		   =							vocab_size
						__magic_name__					:							Any		   =							hidden_size
						__magic_name__					:							Dict		   =							num_hidden_layers
						__magic_name__					:							str		   =							num_attention_heads
						__magic_name__					:							Tuple		   =							intermediate_size
						__magic_name__					:							Optional[int]		   =							hidden_act
						__magic_name__					:							str		   =							hidden_dropout_prob
						__magic_name__					:							int		   =							attention_probs_dropout_prob
						__magic_name__					:							Dict		   =							max_position_embeddings
						__magic_name__					:							Optional[int]		   =							eos_token_id
						__magic_name__					:							Any		   =							pad_token_id
						__magic_name__					:							Tuple		   =							bos_token_id
						__magic_name__					:							str		   =							embed_dim
						__magic_name__					:							Optional[int]		   =							word_embed_proj_dim
						__magic_name__					:							Any		   =							initializer_range
						__magic_name__					:							Optional[int]		   =							False






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




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size						)				,     3				,     self.vocab_size						)
						__magic_name__					:							Dict		   =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,     dtype=np.intaa						))				,     -1						)

						__magic_name__					:							Tuple		   =							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=lowerCamelCase				,     word_embed_proj_dim=self.word_embed_proj_dim				,     initializer_range=self.initializer_range				,     use_cache=lowerCamelCase				,     )
						__magic_name__					:							Tuple		   =							prepare_opt_inputs_dict(lowerCamelCase				,     lowerCamelCase						)
						return config, inputs_dict






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__   , __magic_name__					:							Optional[int]		   =							self.prepare_config_and_inputs()
						return config, inputs_dict






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							int		   =							20
						__magic_name__					:							str		   =							model_class_name(lowerCamelCase						)

						__magic_name__					:							Union[str, Any]		   =							inputs_dict['''input_ids''']
						__magic_name__					:							Tuple		   =							inputs_dict['''attention_mask''']

						__magic_name__					:							Optional[Any]		   =							model.init_cache(input_ids.shape[0]				,     lowerCamelCase						)
						__magic_name__					:							str		   =							jnp.ones((input_ids.shape[0], max_length)				,     dtype='''i4'''						)

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

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

						__magic_name__					:							Union[str, Any]		   =							model(lowerCamelCase						)

						__magic_name__					:							str		   =							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  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							20
						__magic_name__					:							Dict		   =							model_class_name(lowerCamelCase						)

						__magic_name__   , __magic_name__					:							int		   =							(
						    inputs_dict['''input_ids'''],
						    inputs_dict['''attention_mask'''],
						)

						__magic_name__					:							List[str]		   =							jnp.concatenate(
						    [
						        attention_mask,
						        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])						),
						    ]				,     axis=-1				,     )

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

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

						__magic_name__					:							List[Any]		   =							model(lowerCamelCase				,     attention_mask=lowerCamelCase						)

						__magic_name__					:							int		   =							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		,     unittest.TestCase		,     __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Dict				    =(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
	lowerCamelCase__		: Optional[int]				    =() if is_flax_available() else ()






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							FlaxOPTModelTester(self						)






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




						"""simple docstring"""






						__magic_name__   , __magic_name__					:							int		   =							self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)






	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						__magic_name__   , __magic_name__					:							Optional[int]		   =							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(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)






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




						"""simple docstring"""






						for model_class_name in self.all_model_classes:
											__magic_name__					:							Dict		   =							model_class_name.from_pretrained('''facebook/opt-125m'''						)
											__magic_name__					:							str		   =							np.ones((1, 1)						) * model.config.eos_token_id
											__magic_name__					:							List[str]		   =							model(lowerCamelCase						)
											self.assertIsNotNone(lowerCamelCase						)





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






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




						"""simple docstring"""






						__magic_name__					:							str		   =							FlaxOPTModel.from_pretrained('''facebook/opt-350m'''						)
						__magic_name__					:							Tuple		   =							jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
						__magic_name__					:							Optional[Any]		   =							model(input_ids=lowerCamelCase						).last_hidden_state
						__magic_name__					:							Optional[Any]		   =							(1, 11, 512)
						self.assertEqual(output.shape				,     lowerCamelCase						)
						__magic_name__					:							Tuple		   =							jnp.array(
						    [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]						)
						self.assertTrue(jnp.allclose(output[:, :3, :3]				,     lowerCamelCase				,     atol=4e-2						)						)





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






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						super().setUp()
						__magic_name__					:							str		   =							'''facebook/opt-350m'''






	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							FlaxOPTForCausalLM.from_pretrained(self.path_model						)
						__magic_name__					:							int		   =							GPTaTokenizer.from_pretrained(self.path_model						)

						__magic_name__					:							int		   =							[
						    '''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
						__magic_name__					:							Dict		   =							tokenizer(lowerCamelCase				,     return_tensors='''jax'''				,     padding=lowerCamelCase				,     add_special_tokens=lowerCamelCase						)
						__magic_name__					:							List[str]		   =							model(inputs.input_ids				,     attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
						__magic_name__					:							Optional[int]		   =							jnp.array(
						    [
						        [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
						        [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
						        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
						        [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
						    ]						)
						self.assertTrue(jnp.allclose(lowerCamelCase				,     lowerCamelCase				,     atol=4e-2						)						)

						__magic_name__					:							List[str]		   =							jax.jit(lowerCamelCase						)
						__magic_name__					:							int		   =							model(inputs.input_ids				,     attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
						self.assertTrue(jnp.allclose(lowerCamelCase				,     lowerCamelCase				,     atol=4e-2						)						)





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






	@property
	def  lowercase      (	self						)      ->   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  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							'''facebook/opt-125m'''

						__magic_name__					:							Union[str, Any]		   =							[
						    '''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''',
						]

						__magic_name__					:							List[str]		   =							[]

						__magic_name__					:							Optional[Any]		   =							FlaxOPTForCausalLM.from_pretrained(lowerCamelCase						)
						__magic_name__					:							str		   =							GPTaTokenizer.from_pretrained(lowerCamelCase						)

						for prompt in self.prompts:
											__magic_name__					:							Optional[int]		   =							tokenizer(lowerCamelCase				,     return_tensors='''jax'''						).input_ids

											__magic_name__					:							List[str]		   =							model.generate(lowerCamelCase				,     max_length=10						)
											__magic_name__					:							Any		   =							generated_ids[0]

											__magic_name__					:							Tuple		   =							tokenizer.batch_decode(lowerCamelCase				,     skip_special_tokens=lowerCamelCase						)
											predicted_outputs += generated_string

						self.assertListEqual(lowerCamelCase				,     lowerCamelCase						)






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							str		   =							'''facebook/opt-350m'''

						__magic_name__					:							Tuple		   =							[
						    '''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''',
						]

						__magic_name__					:							Union[str, Any]		   =							[]
						__magic_name__					:							Any		   =							FlaxOPTForCausalLM.from_pretrained(lowerCamelCase						)
						__magic_name__					:							Dict		   =							GPTaTokenizer.from_pretrained(lowerCamelCase						)

						for prompt in self.prompts:
											__magic_name__					:							List[str]		   =							tokenizer(lowerCamelCase				,     return_tensors='''jax'''						).input_ids

											__magic_name__					:							Any		   =							model.generate(lowerCamelCase				,     max_length=10						)
											__magic_name__					:							List[Any]		   =							generated_ids[0]

											__magic_name__					:							Optional[int]		   =							tokenizer.batch_decode(lowerCamelCase				,     skip_special_tokens=lowerCamelCase						)
											predicted_outputs += generated_string

						self.assertListEqual(lowerCamelCase				,     lowerCamelCase						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							Any		   =							'''facebook/opt-125m'''
						__magic_name__					:							str		   =							[
						    '''Today is a beautiful day and I want to thank''',
						    '''In the city of Rome Canaver Canaver Canaver Canaver''',
						]
						__magic_name__					:							Dict		   =							FlaxOPTForCausalLM.from_pretrained(lowerCamelCase						)
						__magic_name__					:							Any		   =							GPTaTokenizer.from_pretrained(lowerCamelCase						)
						__magic_name__					:							Dict		   =							tokenizer(
						    [
						        '''Today is a beautiful day and I want to''',
						        '''In the city of''',
						    ]				,     return_tensors='''jax'''				,     padding=lowerCamelCase				,     )

						__magic_name__					:							Dict		   =							jax.jit(model.generate						)

						__magic_name__					:							Dict		   =							jit_generate(inputs['''input_ids''']				,     attention_mask=inputs['''attention_mask''']						).sequences

						__magic_name__					:							int		   =							tokenizer.batch_decode(lowerCamelCase				,     skip_special_tokens=lowerCamelCase						)

						self.assertIsNotNone(lowerCamelCase				,     lowerCamelCase						)






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							'''facebook/opt-350m'''

						__magic_name__					:							Optional[int]		   =							GPTaTokenizer.from_pretrained(lowerCamelCase						)
						__magic_name__					:							Optional[int]		   =							FlaxOPTForCausalLM.from_pretrained(lowerCamelCase						)

						__magic_name__					:							Tuple		   =							'''left'''

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

						__magic_name__					:							Optional[Any]		   =							tokenizer(lowerCamelCase				,     return_tensors='''jax'''				,     padding=lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							inputs['''input_ids''']

						__magic_name__					:							str		   =							model.generate(input_ids=lowerCamelCase				,     attention_mask=inputs['''attention_mask''']				,     trace=lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tokenizer(sentences[0]				,     return_tensors='''jax'''						).input_ids
						__magic_name__					:							Union[str, Any]		   =							model.generate(input_ids=lowerCamelCase						)

						__magic_name__					:							List[Any]		   =							inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
						__magic_name__					:							List[str]		   =							tokenizer(sentences[1]				,     return_tensors='''jax'''						).input_ids
						__magic_name__					:							Dict		   =							model.generate(input_ids=lowerCamelCase				,     max_length=model.config.max_length - num_paddings						)

						__magic_name__					:							str		   =							tokenizer.batch_decode(outputs[0]				,     skip_special_tokens=lowerCamelCase						)
						__magic_name__					:							Dict		   =							tokenizer.decode(output_non_padded[0][0]				,     skip_special_tokens=lowerCamelCase						)
						__magic_name__					:							Optional[int]		   =							tokenizer.decode(output_padded[0][0]				,     skip_special_tokens=lowerCamelCase						)

						__magic_name__					:							Dict		   =							[
						    '''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(lowerCamelCase				,     lowerCamelCase						)
						self.assertListEqual(lowerCamelCase				,     [non_padded_sentence, padded_sentence]						)









import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation





@require_tf
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]						)
						__magic_name__					:							List[str]		   =							get_tf_activation('''gelu'''						)
						__magic_name__					:							str		   =							get_tf_activation('''gelu_10'''						)

						__magic_name__					:							List[str]		   =							gelu(lowerCamelCase						)
						__magic_name__					:							Optional[Any]		   =							geluaa(lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tf.where(y_gelu_aa < 1_0.0				,     1.0				,     0.0						)

						self.assertEqual(tf.math.reduce_max(lowerCamelCase						).numpy().item()				,     1_0.0						)
						self.assertTrue(np.allclose(y_gelu * clipped_mask				,     y_gelu_aa * clipped_mask						)						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						get_tf_activation('''gelu'''						)
						get_tf_activation('''gelu_10'''						)
						get_tf_activation('''gelu_fast'''						)
						get_tf_activation('''gelu_new'''						)
						get_tf_activation('''glu'''						)
						get_tf_activation('''mish'''						)
						get_tf_activation('''quick_gelu'''						)
						get_tf_activation('''relu'''						)
						get_tf_activation('''sigmoid'''						)
						get_tf_activation('''silu'''						)
						get_tf_activation('''swish'''						)
						get_tf_activation('''tanh'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation('''bogus'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation(lowerCamelCase						)



