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 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							str=None ,	UpperCAmelCase__       :							Optional[int]=None					):
					if attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.cast(tf.math.not_equal(UpperCAmelCase__ ,	config.pad_token_id					) ,	tf.inta					)
					return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class   lowercase      :
							lowercase__  :    List[str]									=							OPTConfig
							lowercase__  :    Union[str, Any]									=							{}
							lowercase__  :    List[str]									=							"""gelu"""




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



												'''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 		__snake_case(  self				:   List[str] )   ->			List[str]:



												'''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=_UpperCamelCase		,							**self.config_updates		,							)
												SCREAMING_SNAKE_CASE           =    prepare_opt_inputs_dict(_UpperCamelCase		,							_UpperCamelCase )
												return config, inputs_dict





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Any )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTModel(config=_UpperCamelCase )
												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(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

												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(_UpperCamelCase		,							attention_mask=_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							past_key_values=_UpperCamelCase )[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(_UpperCamelCase		,							_UpperCamelCase		,							rtol=1e-3 )



@require_tf
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    List[str]									=							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
							lowercase__  :    List[Any]									=							(TFOPTForCausalLM,) if is_tf_available() else ()
							lowercase__  :    Optional[Any]									=							(
							    {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
							)
							lowercase__  :    str									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Any									=							False
							lowercase__  :    str									=							10




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase )




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   Any )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )





							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												def _get_word_embedding_weight(_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any] ):
																	if hasattr(_UpperCamelCase		,							"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(_UpperCamelCase		,							"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=_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_input_embeddings() )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_output_embeddings() )

																						# reshape the embeddings
																						model.resize_token_embeddings(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_input_embeddings() )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							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]		,							_UpperCamelCase )

																						# 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(_UpperCamelCase )

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

																											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(_UpperCamelCase )





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					return tf.constant(UpperCAmelCase__ ,	dtype=tf.intaa					)


@require_tf
class   lowercase      (       unittest.TestCase       ):
							lowercase__  :    int									=							99




							def 		__snake_case(  self				:   Union[str, Any] )   ->			Optional[int]:



												'''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 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTModel.from_pretrained("facebook/opt-350m" )
												SCREAMING_SNAKE_CASE           =    _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
												SCREAMING_SNAKE_CASE           =    tf.not_equal(_UpperCamelCase		,							model.config.pad_token_id )
												with tf.GradientTape():
																	SCREAMING_SNAKE_CASE           =    model(input_ids=_UpperCamelCase		,							attention_mask=_UpperCamelCase ).last_hidden_state
												SCREAMING_SNAKE_CASE           =    (1, 11, 512)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tf.constant(
												    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]] )
												self.assertTrue(np.allclose(output[:, :3, :3]		,							_UpperCamelCase		,							atol=4e-3 ) )

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



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




							def 		__snake_case(  self				:   Any )   ->			Dict:



												'''simple docstring'''

												super().setUp()
												SCREAMING_SNAKE_CASE           =    "facebook/opt-350m"





							def 		__snake_case(  self				:   int )   ->			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(_UpperCamelCase		,							return_tensors="tf"		,							padding=_UpperCamelCase		,							add_special_tokens=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tf.math.reduce_mean(model(inputs.input_ids		,							attention_mask=inputs.attention_mask )[0]		,							axis=-1 )
												SCREAMING_SNAKE_CASE           =    tf.constant(
												    [
												        [1.3_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(np.allclose(_UpperCamelCase		,							_UpperCamelCase		,							atol=1e-4 ) )

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



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




							@property
							def 		__snake_case(  self				:   List[str] )   ->			str:



												'''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 		__snake_case(  self				:   Tuple )   ->			Optional[int]:



												'''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(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(_UpperCamelCase )

												for prompt in self.prompts:
																	SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="tf" ).input_ids

																	SCREAMING_SNAKE_CASE           =    model.generate(_UpperCamelCase		,							max_length=10 )

																	SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
																	predicted_outputs += generated_string

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[str] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    "facebook/opt-350m"

												SCREAMING_SNAKE_CASE           =    GPTaTokenizer.from_pretrained(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(_UpperCamelCase )

												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(_UpperCamelCase		,							return_tensors="tf"		,							padding=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    inputs["input_ids"]

												SCREAMING_SNAKE_CASE           =    model.generate(input_ids=_UpperCamelCase		,							attention_mask=inputs["attention_mask"] )

												SCREAMING_SNAKE_CASE           =    tokenizer(sentences[0]		,							return_tensors="tf" ).input_ids
												SCREAMING_SNAKE_CASE           =    model.generate(input_ids=_UpperCamelCase )

												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=_UpperCamelCase		,							max_length=model.config.max_length - num_paddings )

												SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.decode(output_non_padded[0]		,							skip_special_tokens=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.decode(output_padded[0]		,							skip_special_tokens=_UpperCamelCase )

												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(_UpperCamelCase		,							_UpperCamelCase )
												self.assertListEqual(_UpperCamelCase		,							[non_padded_sentence, padded_sentence] )





							def 		__snake_case(  self				:   List[str] )   ->			List[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(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(_UpperCamelCase )

												for prompt in self.prompts:
																	SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="tf" ).input_ids

																	SCREAMING_SNAKE_CASE           =    model.generate(_UpperCamelCase		,							max_length=10 )

																	SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
																	predicted_outputs += generated_string

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




