
"""simple docstring"""







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_      (       lowercase    ,			lowercase    ,			lowercase=None    ,			lowercase=None   )  ->				Dict:


     """simple docstring"""







     if attention_mask is None:
          UpperCAmelCase_    :     Union[str, Any]            =	tf.cast(tf.math.not_equal(lowercase    ,			config.pad_token_id   )    ,			tf.inta   )
     return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 		UpperCAmelCase_						:
 """simple docstring"""




 UpperCamelCase_    :  List[str]					 =     OPTConfig
 UpperCamelCase_    :  int					 =     {}
 UpperCamelCase_    :  Any					 =     """gelu"""




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



      """simple docstring"""

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




 def 	a   (			self							:			int			)->       Any:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	ids_tensor([self.batch_size, self.seq_length - 1]  , self.vocab_size			)
      UpperCAmelCase_    :     List[str]            =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)  , 1			)
      UpperCAmelCase_    :     int            =	tf.concat([input_ids, eos_tensor]  , axis=1			)

      UpperCAmelCase_    :     Any            =	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=a_  , **self.config_updates  , )
      UpperCAmelCase_    :     Optional[Any]            =	prepare_opt_inputs_dict(a_  , a_			)
      return config, inputs_dict






 def 	a   (			self							:			Tuple  , a_							:			Dict  , a_							:			str			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	TFOPTModel(config=a_			)
      UpperCAmelCase_    :     Dict            =	inputs_dict["""input_ids"""]

      UpperCAmelCase_    :     str            =	input_ids[:1, :]
      UpperCAmelCase_    :     Optional[int]            =	inputs_dict["""attention_mask"""][:1, :]
      UpperCAmelCase_    :     List[str]            =	1

      # first forward pass
      UpperCAmelCase_    :     List[str]            =	model(a_  , attention_mask=a_  , use_cache=a_			)

      UpperCAmelCase_       ,UpperCAmelCase_    :     Optional[Any]            =	outputs.to_tuple()

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

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

      UpperCAmelCase_    :     Optional[Any]            =	model(a_  , attention_mask=a_			)[0]
      UpperCAmelCase_    :     Union[str, Any]            =	model(a_  , attention_mask=a_  , past_key_values=a_			)[0]

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

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

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



@require_tf
class 		UpperCAmelCase_						(lowerCamelCase_	,			lowerCamelCase_	,			unittest.TestCase    ):
 """simple docstring"""




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




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



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	TFOPTModelTester(self			)
      UpperCAmelCase_    :     Optional[int]            =	ConfigTester(self  , config_class=a_			)




 def 	a   (			self							:			Any			)->       int:



      """simple docstring"""

      self.config_tester.run_common_tests()




 def 	a   (			self							:			Tuple			)->       Dict:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*a_			)






 def 	a   (			self							:			Any			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	self.model_tester.prepare_config_and_inputs_for_common()

      def _get_word_embedding_weight(a_							:			Optional[int]  , a_							:			Any			):
           if hasattr(a_  , """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(a_  , """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
                UpperCAmelCase_    :     Optional[int]            =	model_class(config=a_			)
                UpperCAmelCase_    :     Union[str, Any]            =	_get_word_embedding_weight(a_  , model.get_input_embeddings()			)
                UpperCAmelCase_    :     Union[str, Any]            =	_get_word_embedding_weight(a_  , model.get_output_embeddings()			)

                # reshape the embeddings
                model.resize_token_embeddings(a_			)
                UpperCAmelCase_    :     int            =	_get_word_embedding_weight(a_  , model.get_input_embeddings()			)
                UpperCAmelCase_    :     Dict            =	_get_word_embedding_weight(a_  , model.get_output_embeddings()			)

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

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

                # check that weights remain the same after resizing
                UpperCAmelCase_    :     Tuple            =	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:
                          UpperCAmelCase_    :     int            =	False
                self.assertTrue(a_			)

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

                     UpperCAmelCase_    :     Tuple            =	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:
                               UpperCAmelCase_    :     Any            =	False
                     self.assertTrue(a_			)




def       A_      (       lowercase   )  ->				str:


     """simple docstring"""







     return tf.constant(lowercase    ,			dtype=tf.intaa   )



@require_tf
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 UpperCamelCase_    :  List[str]					 =     99




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



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	tf.ones((4, 1)  , dtype=tf.intaa			) * 2
      UpperCAmelCase_    :     Optional[Any]            =	tf.concat([ids_tensor((4, 6)  , self.vocab_size - 3			) + 3, eos_column_vector]  , axis=1			)
      UpperCAmelCase_    :     Optional[Any]            =	input_ids.shape[0]
      UpperCAmelCase_    :     Tuple            =	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 	a   (			self							:			List[Any]			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	TFOPTModel.from_pretrained("""facebook/opt-350m"""			)
      UpperCAmelCase_    :     Dict            =	_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			)
      UpperCAmelCase_    :     Optional[int]            =	tf.not_equal(a_  , model.config.pad_token_id			)
      with tf.GradientTape():
           UpperCAmelCase_    :     List[str]            =	model(input_ids=a_  , attention_mask=a_			).last_hidden_state
      UpperCAmelCase_    :     Union[str, Any]            =	(1, 11, 5_12)
      self.assertEqual(output.shape  , a_			)
      UpperCAmelCase_    :     Tuple            =	tf.constant(
          [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]			)
      self.assertTrue(np.allclose(output[:, :3, :3]  , a_  , atol=4E-3			)			)

      UpperCAmelCase_    :     List[Any]            =	tf.function(a_  , jit_compile=a_			)
      UpperCAmelCase_    :     List[str]            =	xla_generate(a_  , a_			)[0]
      self.assertTrue(np.allclose(output[:, :3, :3]  , a_  , atol=4E-2			)			)



@require_tf
@slow
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




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



      """simple docstring"""

      super().setUp()
      UpperCAmelCase_    :     List[Any]            =	"""facebook/opt-350m"""






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



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	TFOPTForCausalLM.from_pretrained(self.path_model			)
      UpperCAmelCase_    :     Optional[int]            =	GPTaTokenizer.from_pretrained(self.path_model			)

      UpperCAmelCase_    :     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
      UpperCAmelCase_    :     Optional[Any]            =	tokenizer(a_  , return_tensors="""tf"""  , padding=a_  , add_special_tokens=a_			)
      UpperCAmelCase_    :     Optional[int]            =	tf.math.reduce_mean(model(inputs.input_ids  , attention_mask=inputs.attention_mask			)[0]  , axis=-1			)
      UpperCAmelCase_    :     List[str]            =	tf.constant(
          [
              [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
              [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
              [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
              [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
          ]			)
      self.assertTrue(np.allclose(a_  , a_  , atol=1E-4			)			)

      UpperCAmelCase_    :     Any            =	tf.function(a_  , jit_compile=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	tf.math.reduce_mean(xla_generate(inputs.input_ids  , attention_mask=inputs.attention_mask			)[0]  , axis=-1			)
      self.assertTrue(np.allclose(a_  , a_  , atol=1E-4			)			)



@require_tf
@slow
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 @property
 def 	a   (			self							:			Dict			)->       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 	a   (			self							:			Tuple			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	"""facebook/opt-125m"""

      UpperCAmelCase_    :     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""",
      ]

      UpperCAmelCase_    :     List[Any]            =	[]
      UpperCAmelCase_    :     List[str]            =	GPTaTokenizer.from_pretrained(a_			)
      UpperCAmelCase_    :     List[Any]            =	TFOPTForCausalLM.from_pretrained(a_			)

      for prompt in self.prompts:
           UpperCAmelCase_    :     Dict            =	tokenizer(a_  , return_tensors="""tf"""			).input_ids

           UpperCAmelCase_    :     Union[str, Any]            =	model.generate(a_  , max_length=10			)

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

      self.assertListEqual(a_  , a_			)




 def 	a   (			self							:			str			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	"""facebook/opt-350m"""

      UpperCAmelCase_    :     Tuple            =	GPTaTokenizer.from_pretrained(a_			)
      UpperCAmelCase_    :     Dict            =	TFOPTForCausalLM.from_pretrained(a_			)

      UpperCAmelCase_    :     Dict            =	"""left"""

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

      UpperCAmelCase_    :     Optional[Any]            =	tokenizer(a_  , return_tensors="""tf"""  , padding=a_			)
      UpperCAmelCase_    :     Optional[int]            =	inputs["""input_ids"""]

      UpperCAmelCase_    :     Dict            =	model.generate(input_ids=a_  , attention_mask=inputs["""attention_mask"""]			)

      UpperCAmelCase_    :     List[Any]            =	tokenizer(sentences[0]  , return_tensors="""tf"""			).input_ids
      UpperCAmelCase_    :     Dict            =	model.generate(input_ids=a_			)

      UpperCAmelCase_    :     Union[str, Any]            =	inputs_non_padded.shape[-1] - tf.math.reduce_sum(
          tf.cast(inputs["""attention_mask"""][-1]  , tf.intaa			)			)
      UpperCAmelCase_    :     Optional[int]            =	tokenizer(sentences[1]  , return_tensors="""tf"""			).input_ids
      UpperCAmelCase_    :     Optional[Any]            =	model.generate(input_ids=a_  , max_length=model.config.max_length - num_paddings			)

      UpperCAmelCase_    :     Union[str, Any]            =	tokenizer.batch_decode(a_  , skip_special_tokens=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	tokenizer.decode(output_non_padded[0]  , skip_special_tokens=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	tokenizer.decode(output_padded[0]  , skip_special_tokens=a_			)

      UpperCAmelCase_    :     List[str]            =	[
          """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(a_  , a_			)
      self.assertListEqual(a_  , [non_padded_sentence, padded_sentence]			)






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



      """simple docstring"""

      UpperCAmelCase_    :     str            =	"""facebook/opt-350m"""

      UpperCAmelCase_    :     Union[str, Any]            =	[
          """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""",
      ]

      UpperCAmelCase_    :     str            =	[]
      UpperCAmelCase_    :     List[Any]            =	GPTaTokenizer.from_pretrained(a_			)
      UpperCAmelCase_    :     List[str]            =	TFOPTForCausalLM.from_pretrained(a_			)

      for prompt in self.prompts:
           UpperCAmelCase_    :     Optional[Any]            =	tokenizer(a_  , return_tensors="""tf"""			).input_ids

           UpperCAmelCase_    :     List[Any]            =	model.generate(a_  , max_length=10			)

           UpperCAmelCase_    :     List[Any]            =	tokenizer.batch_decode(a_  , skip_special_tokens=a_			)
           predicted_outputs += generated_string

      self.assertListEqual(a_  , a_			)



"""simple docstring"""







import argparse
import json

from tqdm import tqdm




def       A_      (       )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path"""    ,			type=lowercase    ,			default="""biencoder-nq-dev.json"""    ,			help="""Path to raw DPR training data"""    ,			)
     parser.add_argument(
         """--evaluation_set"""    ,			type=lowercase    ,			help="""where to store parsed evaluation_set file"""    ,			)
     parser.add_argument(
         """--gold_data_path"""    ,			type=lowercase    ,			help="""where to store parsed gold_data_path file"""    ,			)
     UpperCAmelCase_    :     Dict            =	parser.parse_args()

     with open(args.src_path    ,			"""r"""   ) as src_file, open(args.evaluation_set    ,			"""w"""   ) as eval_file, open(
         args.gold_data_path    ,			"""w"""   ) as gold_file:
          UpperCAmelCase_    :     List[Any]            =	json.load(lowercase   )
          for dpr_record in tqdm(lowercase   ):
               UpperCAmelCase_    :     List[Any]            =	dpr_record["""question"""]
               UpperCAmelCase_    :     Dict            =	[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""   )
               gold_file.write("""\t""".join(lowercase   ) + """\n"""   )


if __name__ == "__main__":
       main()

