

'''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__			(    a__ ,       a__ ,       a__=None ,       a__=None		):




     """simple docstring"""


     if attention_mask is None:
          __SCREAMING_SNAKE_CASE      = 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 		lowerCAmelCase__  :



 """simple docstring"""



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


      """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  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[Any]:


      """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=__SCREAMING_SNAKE_CASE   ,				**self.config_updates   ,				)
      __SCREAMING_SNAKE_CASE      = prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      return config, inputs_dict


 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFOPTModel(config=__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				use_cache=__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE     )[0]
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				past_key_values=__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				rtol=1E-3     )







@require_tf
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 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  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = TFOPTModelTester(self     )
      __SCREAMING_SNAKE_CASE      = ConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




      self.config_tester.run_common_tests()
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE     )


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

      def _get_word_embedding_weight(__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]     ):
           if hasattr(__SCREAMING_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(__SCREAMING_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=__SCREAMING_SNAKE_CASE     )
                __SCREAMING_SNAKE_CASE      = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE   ,				model.get_input_embeddings()     )
                __SCREAMING_SNAKE_CASE      = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE   ,				model.get_output_embeddings()     )

                # reshape the embeddings
                model.resize_token_embeddings(__SCREAMING_SNAKE_CASE     )
                __SCREAMING_SNAKE_CASE      = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE   ,				model.get_input_embeddings()     )
                __SCREAMING_SNAKE_CASE      = _get_word_embedding_weight(__SCREAMING_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]   ,				__SCREAMING_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(__SCREAMING_SNAKE_CASE     )

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







def   a__			(    a__		):




     """simple docstring"""


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







@require_tf
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = 99
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """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 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



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


      """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(__SCREAMING_SNAKE_CASE   ,				model.config.pad_token_id     )
      with tf.GradientTape():
           __SCREAMING_SNAKE_CASE      = model(input_ids=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE     ).last_hidden_state
      __SCREAMING_SNAKE_CASE      = (1, 11, 512)
      self.assertEqual(output.shape   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = 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]   ,				__SCREAMING_SNAKE_CASE   ,				atol=4E-3     )     )

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







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



 """simple docstring"""



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


      """simple docstring"""




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


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


      """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(__SCREAMING_SNAKE_CASE   ,				return_tensors="""tf"""   ,				padding=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_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(
          [
              [1.3851, -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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-4     )     )

      __SCREAMING_SNAKE_CASE      = tf.function(__SCREAMING_SNAKE_CASE   ,				jit_compile=__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-4     )     )







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



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """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   :			Any     )    ->					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(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE     )

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

           __SCREAMING_SNAKE_CASE      = model.generate(__SCREAMING_SNAKE_CASE   ,				max_length=10     )

           __SCREAMING_SNAKE_CASE      = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     )
           predicted_outputs += generated_string

      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """facebook/opt-350m"""

      __SCREAMING_SNAKE_CASE      = GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = TFOPTForCausalLM.from_pretrained(__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				return_tensors="""tf"""   ,				padding=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = inputs["""input_ids"""]

      __SCREAMING_SNAKE_CASE      = model.generate(input_ids=__SCREAMING_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=__SCREAMING_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=__SCREAMING_SNAKE_CASE   ,				max_length=model.config.max_length - num_paddings     )

      __SCREAMING_SNAKE_CASE      = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = tokenizer.decode(output_non_padded[0]   ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = tokenizer.decode(output_padded[0]   ,				skip_special_tokens=__SCREAMING_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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				[non_padded_sentence, padded_sentence]     )


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """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(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE     )

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

           __SCREAMING_SNAKE_CASE      = model.generate(__SCREAMING_SNAKE_CASE   ,				max_length=10     )

           __SCREAMING_SNAKE_CASE      = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     )
           predicted_outputs += generated_string

      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )









'''simple docstring'''



import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase					:			int       =	get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
class 		lowerCAmelCase__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = XLMProphetNetTokenizer
 lowerCAmelCase__       = False
 lowerCAmelCase__       = True
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE      = XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE   ,				keep_accents=__SCREAMING_SNAKE_CASE     )
      tokenizer.save_pretrained(self.tmpdirname     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """[PAD]"""
      __SCREAMING_SNAKE_CASE      = 0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = list(self.get_tokenizer().get_vocab().keys()     )

      self.assertEqual(vocab_keys[0]   ,				"""[PAD]"""     )
      self.assertEqual(vocab_keys[1]   ,				"""[CLS]"""     )
      self.assertEqual(vocab_keys[-1]   ,				"""j"""     )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				1_012     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[str]:


      """simple docstring"""




      self.assertEqual(self.get_tokenizer().vocab_size   ,				1_012     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE   ,				keep_accents=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = tokenizer.tokenize("""This is a test"""     )
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]     )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )   ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,				)

      __SCREAMING_SNAKE_CASE      = tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]   ,				)
      __SCREAMING_SNAKE_CASE      = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
          ]   ,				)

      __SCREAMING_SNAKE_CASE      = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """[UNK]""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """[UNK]""",
              """.""",
          ]   ,				)
 @cached_property
 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""     )
 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """Hello World!"""
      __SCREAMING_SNAKE_CASE      = [35_389, 6_672, 49, 2]
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE     )     )


 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__SCREAMING_SNAKE_CASE   ,				model_name="""microsoft/xprophetnet-large-wiki100-cased"""   ,				revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""   ,				)






