




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None )->	str:
		if attention_mask is None:
				SCREAMING_SNAKE_CASE_					      =							tf.cast(tf.math.not_equal(lowerCAmelCase    ,						config.pad_token_id )    ,						tf.inta )
		return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class    lowercase_		:
						lowerCAmelCase__			      =OPTConfig
						lowerCAmelCase__			      ={}
						lowerCAmelCase__			      ="gelu"



						def __init__(			self	:   str					,							snake_case__	:   Any					,							snake_case__	:   Any=13					,							snake_case__	:   int=7					,							snake_case__	:   Union[str, Any]=True					,							snake_case__	:   List[str]=False					,							snake_case__	:   Dict=99					,							snake_case__	:   Optional[int]=16					,							snake_case__	:   List[str]=2					,							snake_case__	:   Any=4					,							snake_case__	:   Union[str, Any]=4					,							snake_case__	:   List[str]="gelu"					,							snake_case__	:   Dict=0.1					,							snake_case__	:   List[str]=0.1					,							snake_case__	:   Dict=20					,							snake_case__	:   Tuple=2					,							snake_case__	:   List[Any]=1					,							snake_case__	:   Optional[int]=0					,							snake_case__	:   List[Any]=16					,							snake_case__	:   Union[str, Any]=16					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent
								SCREAMING_SNAKE_CASE_					      =							batch_size
								SCREAMING_SNAKE_CASE_					      =							seq_length
								SCREAMING_SNAKE_CASE_					      =							is_training
								SCREAMING_SNAKE_CASE_					      =							use_labels
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							intermediate_size
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							pad_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id
								SCREAMING_SNAKE_CASE_					      =							embed_dim
								SCREAMING_SNAKE_CASE_					      =							word_embed_proj_dim
								SCREAMING_SNAKE_CASE_					      =							False



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ids_tensor([self.batch_size, self.seq_length - 1]					,							self.vocab_size  )
								SCREAMING_SNAKE_CASE_					      =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )					,							1  )
								SCREAMING_SNAKE_CASE_					      =							tf.concat([input_ids, eos_tensor]					,							axis=1  )

								SCREAMING_SNAKE_CASE_					      =							self.config_cls(
								    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					,							word_embed_proj_dim=self.word_embed_proj_dim					,							is_encoder_decoder=snake_case__					,							**self.config_updates					,							)
								SCREAMING_SNAKE_CASE_					      =							prepare_opt_inputs_dict(snake_case__					,							snake_case__  )
								return config, inputs_dict






						def 	__a					(			self	:   Tuple					,							snake_case__	:   int					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFOPTModel(config=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							inputs_dict['input_ids']

								SCREAMING_SNAKE_CASE_					      =							input_ids[:1, :]
								SCREAMING_SNAKE_CASE_					      =							inputs_dict['attention_mask'][:1, :]
								SCREAMING_SNAKE_CASE_					      =							1

								# first forward pass
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							attention_mask=snake_case__					,							use_cache=snake_case__  )

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								SCREAMING_SNAKE_CASE_					      =							ids_tensor((self.batch_size, 3)					,							config.vocab_size  )
								SCREAMING_SNAKE_CASE_					      =							tf.cast(ids_tensor((self.batch_size, 3)					,							2  )					,							tf.inta  )

								# append to next input_ids and
								SCREAMING_SNAKE_CASE_					      =							tf.concat([input_ids, next_tokens]					,							axis=-1  )
								SCREAMING_SNAKE_CASE_					      =							tf.concat([attention_mask, next_attn_mask]					,							axis=-1  )

								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							attention_mask=snake_case__  )[0]
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							attention_mask=snake_case__					,							past_key_values=snake_case__  )[0]

								self.parent.assertEqual(next_tokens.shape[1]					,							output_from_past.shape[1]  )

								# select random slice
								SCREAMING_SNAKE_CASE_					      =							int(ids_tensor((1,)					,							output_from_past.shape[-1]  )  )
								SCREAMING_SNAKE_CASE_					      =							output_from_no_past[:, -3:, random_slice_idx]
								SCREAMING_SNAKE_CASE_					      =							output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(snake_case__					,							snake_case__					,							rtol=1e-3  )



@require_tf
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
						lowerCAmelCase__			      =(TFOPTForCausalLM,) if is_tf_available() else ()
						lowerCAmelCase__			      =(
						    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
						)
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =10



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFOPTModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								self.config_tester.run_common_tests()



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*snake_case__  )






						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()

								def _get_word_embedding_weight(snake_case__	:   Dict					,							snake_case__	:   Optional[Any]  ):
										if hasattr(snake_case__					,							'weight'  ):
												return embedding_layer.weight
										else:
												# Here we build the word embeddings weights if not exists.
												# And then we retry to get the attribute once built.
												model.build()
												if hasattr(snake_case__					,							'weight'  ):
														return embedding_layer.weight
												else:
														return None

								for model_class in self.all_model_classes:
										for size in [config.vocab_size - 10, config.vocab_size + 10]:
												# build the embeddings
												SCREAMING_SNAKE_CASE_					      =							model_class(config=snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							_get_word_embedding_weight(snake_case__					,							model.get_input_embeddings()  )
												SCREAMING_SNAKE_CASE_					      =							_get_word_embedding_weight(snake_case__					,							model.get_output_embeddings()  )

												# reshape the embeddings
												model.resize_token_embeddings(snake_case__  )
												SCREAMING_SNAKE_CASE_					      =							_get_word_embedding_weight(snake_case__					,							model.get_input_embeddings()  )
												SCREAMING_SNAKE_CASE_					      =							_get_word_embedding_weight(snake_case__					,							model.get_output_embeddings()  )

												# check that the resized embeddings size matches the desired size.
												SCREAMING_SNAKE_CASE_					      =							size if size is not None else config.vocab_size

												self.assertEqual(new_input_embeddings.shape[0]					,							snake_case__  )

												# check that weights remain the same after resizing
												SCREAMING_SNAKE_CASE_					      =							True
												for pa, pa in zip(old_input_embeddings.value()					,							new_input_embeddings.value()  ):
														if tf.math.reduce_sum(tf.math.abs(pa - pa  )  ) > 0:
																SCREAMING_SNAKE_CASE_					      =							False
												self.assertTrue(snake_case__  )

												if old_output_embeddings is not None and new_output_embeddings is not None:
														self.assertEqual(new_output_embeddings.shape[0]					,							snake_case__  )

														SCREAMING_SNAKE_CASE_					      =							True
														for pa, pa in zip(old_output_embeddings.value()					,							new_output_embeddings.value()  ):
																if tf.math.reduce_sum(tf.math.abs(pa - pa  )  ) > 0:
																		SCREAMING_SNAKE_CASE_					      =							False
														self.assertTrue(snake_case__  )


def 					_a							(					lowerCAmelCase )->	Tuple:
		return tf.constant(lowerCAmelCase    ,						dtype=tf.intaa )



@require_tf
class    lowercase_		(unittest.TestCase ):
						lowerCAmelCase__			      =99



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							tf.ones((4, 1)					,							dtype=tf.intaa  ) * 2
								SCREAMING_SNAKE_CASE_					      =							tf.concat([ids_tensor((4, 6)					,							self.vocab_size - 3  ) + 3, eos_column_vector]					,							axis=1  )
								SCREAMING_SNAKE_CASE_					      =							input_ids.shape[0]
								SCREAMING_SNAKE_CASE_					      =							OPTConfig(
								    vocab_size=self.vocab_size					,							hidden_size=24					,							num_hidden_layers=2					,							num_attention_heads=2					,							ffn_dim=32					,							max_position_embeddings=48					,							eos_token_id=2					,							pad_token_id=1					,							bos_token_id=0					,							)
								return config, input_ids, batch_size



@require_sentencepiece
@require_tf
class    lowercase_		(unittest.TestCase ):



						@slow
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFOPTModel.from_pretrained('facebook/opt-350m'  )
								SCREAMING_SNAKE_CASE_					      =							_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
								SCREAMING_SNAKE_CASE_					      =							tf.not_equal(snake_case__					,							model.config.pad_token_id  )
								with tf.GradientTape():
										SCREAMING_SNAKE_CASE_					      =							model(input_ids=snake_case__					,							attention_mask=snake_case__  ).last_hidden_state
								SCREAMING_SNAKE_CASE_					      =							(1, 11, 5_12)
								self.assertEqual(output.shape					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tf.constant(
								    [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]  )
								self.assertTrue(np.allclose(output[:, :3, :3]					,							snake_case__					,							atol=4e-3  )  )

								SCREAMING_SNAKE_CASE_					      =							tf.function(snake_case__					,							jit_compile=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							xla_generate(snake_case__					,							snake_case__  )[0]
								self.assertTrue(np.allclose(output[:, :3, :3]					,							snake_case__					,							atol=4e-2  )  )



@require_tf
@slow
class    lowercase_		(unittest.TestCase ):



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								super().setUp()
								SCREAMING_SNAKE_CASE_					      =							'facebook/opt-350m'






						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFOPTForCausalLM.from_pretrained(self.path_model  )
								SCREAMING_SNAKE_CASE_					      =							GPTaTokenizer.from_pretrained(self.path_model  )

								SCREAMING_SNAKE_CASE_					      =							[
								    '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
								SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__					,							return_tensors='tf'					,							padding=snake_case__					,							add_special_tokens=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tf.math.reduce_mean(model(inputs.input_ids					,							attention_mask=inputs.attention_mask  )[0]					,							axis=-1  )
								SCREAMING_SNAKE_CASE_					      =							tf.constant(
								    [
								        [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(np.allclose(snake_case__					,							snake_case__					,							atol=1e-4  )  )

								SCREAMING_SNAKE_CASE_					      =							tf.function(snake_case__					,							jit_compile=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tf.math.reduce_mean(xla_generate(inputs.input_ids					,							attention_mask=inputs.attention_mask  )[0]					,							axis=-1  )
								self.assertTrue(np.allclose(snake_case__					,							snake_case__					,							atol=1e-4  )  )



@require_tf
@slow
class    lowercase_		(unittest.TestCase ):



						@property
						def 	__a					(			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 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'facebook/opt-125m'

								SCREAMING_SNAKE_CASE_					      =							[
								    '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',
								]

								SCREAMING_SNAKE_CASE_					      =							[]
								SCREAMING_SNAKE_CASE_					      =							GPTaTokenizer.from_pretrained(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							TFOPTForCausalLM.from_pretrained(snake_case__  )

								for prompt in self.prompts:
										SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__					,							return_tensors='tf'  ).input_ids

										SCREAMING_SNAKE_CASE_					      =							model.generate(snake_case__					,							max_length=10  )

										SCREAMING_SNAKE_CASE_					      =							tokenizer.batch_decode(snake_case__					,							skip_special_tokens=snake_case__  )
										predicted_outputs += generated_string

								self.assertListEqual(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'facebook/opt-350m'

								SCREAMING_SNAKE_CASE_					      =							GPTaTokenizer.from_pretrained(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							TFOPTForCausalLM.from_pretrained(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'left'

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

								SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__					,							return_tensors='tf'					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							inputs['input_ids']

								SCREAMING_SNAKE_CASE_					      =							model.generate(input_ids=snake_case__					,							attention_mask=inputs['attention_mask']  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer(sentences[0]					,							return_tensors='tf'  ).input_ids
								SCREAMING_SNAKE_CASE_					      =							model.generate(input_ids=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							inputs_non_padded.shape[-1] - tf.math.reduce_sum(
								    tf.cast(inputs['attention_mask'][-1]					,							tf.intaa  )  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer(sentences[1]					,							return_tensors='tf'  ).input_ids
								SCREAMING_SNAKE_CASE_					      =							model.generate(input_ids=snake_case__					,							max_length=model.config.max_length - num_paddings  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer.batch_decode(snake_case__					,							skip_special_tokens=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.decode(output_non_padded[0]					,							skip_special_tokens=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.decode(output_padded[0]					,							skip_special_tokens=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							[
								    '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(snake_case__					,							snake_case__  )
								self.assertListEqual(snake_case__					,							[non_padded_sentence, padded_sentence]  )






						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'facebook/opt-350m'

								SCREAMING_SNAKE_CASE_					      =							[
								    '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',
								]

								SCREAMING_SNAKE_CASE_					      =							[]
								SCREAMING_SNAKE_CASE_					      =							GPTaTokenizer.from_pretrained(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							TFOPTForCausalLM.from_pretrained(snake_case__  )

								for prompt in self.prompts:
										SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__					,							return_tensors='tf'  ).input_ids

										SCREAMING_SNAKE_CASE_					      =							model.generate(snake_case__					,							max_length=10  )

										SCREAMING_SNAKE_CASE_					      =							tokenizer.batch_decode(snake_case__					,							skip_special_tokens=snake_case__  )
										predicted_outputs += generated_string

								self.assertListEqual(snake_case__					,							snake_case__  )





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	float:
		if velocity > c:
				raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
		elif velocity < 1:
				# Usually the speed should be much higher than 1 (c order of magnitude)
				raise ValueError('Speed must be greater than or equal to 1!' )

		return velocity / c


def 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		return np.array(
		    [
		        [gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
		        [-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
		        [0, 0, 1, 0],
		        [0, 0, 0, 1],
		    ] )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_SNAKE_CASE_					      =							np.array([ct, x, y, z] )  # Symbolic four vector
		else:
				event[0] *= c  # x0 is ct (speed of light * time)

		return transformation_matrix(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						print('''Example of four vector: ''')
						print(f"""ct' = {four_vector[0]}""")
						print(f"""x' = {four_vector[1]}""")
						print(f"""y' = {four_vector[2]}""")
						print(f"""z' = {four_vector[3]}""")

						# Substitute symbols with numerical values
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

						print(f"""\n{numerical_vector}""")