



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 (    A__    ,	A__    ,	A__=None    ,	A__=None						):
      if attention_mask is None:
            lowercase__  =	tf.cast(tf.math.not_equal(A__    ,	config.pad_token_id						)    ,	tf.inta						)
      return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	List[str]												=     OPTConfig
   A     :	Tuple												=     {}
   A     :	List[Any]												=     "gelu"


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




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      List[Any])						->					Optional[int]:




         """simple docstring"""

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

         lowercase__  =	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=lowerCAmelCase      ,					**self.config_updates      ,					)
         lowercase__  =	prepare_opt_inputs_dict(lowerCAmelCase      ,					lowerCAmelCase)
         return config, inputs_dict



   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	TFOPTModel(config=lowerCAmelCase)
         lowercase__  =	inputs_dict['input_ids']

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

         # first forward pass
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					use_cache=lowerCAmelCase)

         lowercase__,						lowercase__  =	outputs.to_tuple()

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

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

         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase)[0]
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					past_key_values=lowerCAmelCase)[0]

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

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

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



@require_tf
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	int												=     (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
   A     :	Dict												=     (TFOPTForCausalLM,) if is_tf_available() else ()
   A     :	Union[str, Any]												=     (
       {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
   )
   A     :	int												=     False
   A     :	Dict												=     False
   A     :	Any												=     False
   A     :	Tuple												=     10


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




         """simple docstring"""

         lowercase__  =	TFOPTModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase)


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




         """simple docstring"""

         self.config_tester.run_common_tests()


   def       UpperCAmelCase		(    self	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase)



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




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         def _get_word_embedding_weight(lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Any):
               if hasattr(lowerCAmelCase      ,					'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(lowerCAmelCase      ,					'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
                     lowercase__  =	model_class(config=lowerCAmelCase)
                     lowercase__  =	_get_word_embedding_weight(lowerCAmelCase      ,					model.get_input_embeddings())
                     lowercase__  =	_get_word_embedding_weight(lowerCAmelCase      ,					model.get_output_embeddings())

                     # reshape the embeddings
                     model.resize_token_embeddings(lowerCAmelCase)
                     lowercase__  =	_get_word_embedding_weight(lowerCAmelCase      ,					model.get_input_embeddings())
                     lowercase__  =	_get_word_embedding_weight(lowerCAmelCase      ,					model.get_output_embeddings())

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

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

                     # check that weights remain the same after resizing
                     lowercase__  =	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:
                                 lowercase__  =	False
                     self.assertTrue(lowerCAmelCase)

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

                           lowercase__  =	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:
                                       lowercase__  =	False
                           self.assertTrue(lowerCAmelCase)

def 	_lowerCAmelCase (    A__						):
      return tf.constant(A__    ,	dtype=tf.intaa						)



@require_tf
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     99


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




         """simple docstring"""

         lowercase__  =	tf.ones((4, 1)      ,					dtype=tf.intaa) * 2
         lowercase__  =	tf.concat([ids_tensor((4, 6)      ,					self.vocab_size - 3) + 3, eos_column_vector]      ,					axis=1)
         lowercase__  =	input_ids.shape[0]
         lowercase__  =	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    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   @slow
   def       UpperCAmelCase		(    self	:      Optional[int])						->					Tuple:




         """simple docstring"""

         lowercase__  =	TFOPTModel.from_pretrained('facebook/opt-350m')
         lowercase__  =	_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
         lowercase__  =	tf.not_equal(lowerCAmelCase      ,					model.config.pad_token_id)
         with tf.GradientTape():
               lowercase__  =	model(input_ids=lowerCAmelCase      ,					attention_mask=lowerCAmelCase).last_hidden_state
         lowercase__  =	(1, 11, 5_12)
         self.assertEqual(output.shape      ,					lowerCAmelCase)
         lowercase__  =	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]      ,					lowerCAmelCase      ,					atol=4E-3))

         lowercase__  =	tf.function(lowerCAmelCase      ,					jit_compile=lowerCAmelCase)
         lowercase__  =	xla_generate(lowerCAmelCase      ,					lowerCAmelCase)[0]
         self.assertTrue(np.allclose(output[:, :3, :3]      ,					lowerCAmelCase      ,					atol=4E-2))



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

   '''simple docstring'''




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




         """simple docstring"""

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



   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Dict:




         """simple docstring"""

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

         lowercase__  =	[
             '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
         lowercase__  =	tokenizer(lowerCAmelCase      ,					return_tensors='tf'      ,					padding=lowerCAmelCase      ,					add_special_tokens=lowerCAmelCase)
         lowercase__  =	tf.math.reduce_mean(model(inputs.input_ids      ,					attention_mask=inputs.attention_mask)[0]      ,					axis=-1)
         lowercase__  =	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(lowerCAmelCase      ,					lowerCAmelCase      ,					atol=1E-4))

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



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

   '''simple docstring'''




   @property
   def       UpperCAmelCase		(    self	:      List[Any])						->					List[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       UpperCAmelCase		(    self	:      str)						->					int:




         """simple docstring"""

         lowercase__  =	'facebook/opt-125m'

         lowercase__  =	[
             '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',
         ]

         lowercase__  =	[]
         lowercase__  =	GPTaTokenizer.from_pretrained(lowerCAmelCase)
         lowercase__  =	TFOPTForCausalLM.from_pretrained(lowerCAmelCase)

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

               lowercase__  =	model.generate(lowerCAmelCase      ,					max_length=10)

               lowercase__  =	tokenizer.batch_decode(lowerCAmelCase      ,					skip_special_tokens=lowerCAmelCase)
               predicted_outputs += generated_string

         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)


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




         """simple docstring"""

         lowercase__  =	'facebook/opt-350m'

         lowercase__  =	GPTaTokenizer.from_pretrained(lowerCAmelCase)
         lowercase__  =	TFOPTForCausalLM.from_pretrained(lowerCAmelCase)

         lowercase__  =	'left'

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

         lowercase__  =	tokenizer(lowerCAmelCase      ,					return_tensors='tf'      ,					padding=lowerCAmelCase)
         lowercase__  =	inputs['input_ids']

         lowercase__  =	model.generate(input_ids=lowerCAmelCase      ,					attention_mask=inputs['attention_mask'])

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

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

         lowercase__  =	tokenizer.batch_decode(lowerCAmelCase      ,					skip_special_tokens=lowerCAmelCase)
         lowercase__  =	tokenizer.decode(output_non_padded[0]      ,					skip_special_tokens=lowerCAmelCase)
         lowercase__  =	tokenizer.decode(output_padded[0]      ,					skip_special_tokens=lowerCAmelCase)

         lowercase__  =	[
             '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])



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




         """simple docstring"""

         lowercase__  =	'facebook/opt-350m'

         lowercase__  =	[
             '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',
         ]

         lowercase__  =	[]
         lowercase__  =	GPTaTokenizer.from_pretrained(lowerCAmelCase)
         lowercase__  =	TFOPTForCausalLM.from_pretrained(lowerCAmelCase)

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

               lowercase__  =	model.generate(lowerCAmelCase      ,					max_length=10)

               lowercase__  =	tokenizer.batch_decode(lowerCAmelCase      ,					skip_special_tokens=lowerCAmelCase)
               predicted_outputs += generated_string

         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)





from __future__ import annotations

def 	_lowerCAmelCase (    A__    ,	A__ = None						):
      lowercase__  =	word_bank or []
      # create a table
      lowercase__  =	len(A__						) + 1

      lowercase__  =	[]
      for _ in range(A__						):
            table.append([]						)
      # seed value
      lowercase__  =	[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(A__						):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(A__						)] == word:
                              lowercase__  =	[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(A__						)] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(A__						)]:
            combination.reverse()

      return table[len(A__						)]


if __name__ == "__main__":
       print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
       print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
       print(
           all_construct(
               "hexagonosaurus",
               ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
           )
       )
