

'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel





@require_tf
class 							_snake_case       :
   _A				:   int			      =    BlenderbotConfig
   _A				:   str			      =    {}
   _A				:   str			      =    '''gelu'''
   def __init__(       self	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]   ,SCREAMING_SNAKE_CASE__	:					Tuple=13   ,SCREAMING_SNAKE_CASE__	:					List[str]=7   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=True   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=False   ,SCREAMING_SNAKE_CASE__	:					List[Any]=99   ,SCREAMING_SNAKE_CASE__	:					List[Any]=32   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=2   ,SCREAMING_SNAKE_CASE__	:					Any=4   ,SCREAMING_SNAKE_CASE__	:					Dict=37   ,SCREAMING_SNAKE_CASE__	:					Dict=0.1   ,SCREAMING_SNAKE_CASE__	:					Any=0.1   ,SCREAMING_SNAKE_CASE__	:					str=20   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]=2   ,SCREAMING_SNAKE_CASE__	:					Any=1   ,SCREAMING_SNAKE_CASE__	:					Union[str, Any]=0   ,):
     SCREAMING_SNAKE_CASE:str  					=    parent
     SCREAMING_SNAKE_CASE:Tuple  					=    batch_size
     SCREAMING_SNAKE_CASE:str  					=    seq_length
     SCREAMING_SNAKE_CASE:str  					=    is_training
     SCREAMING_SNAKE_CASE:List[str]  					=    use_labels
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    vocab_size
     SCREAMING_SNAKE_CASE:int  					=    hidden_size
     SCREAMING_SNAKE_CASE:Tuple  					=    num_hidden_layers
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    num_attention_heads
     SCREAMING_SNAKE_CASE:List[Any]  					=    intermediate_size

     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    hidden_dropout_prob
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE:Dict  					=    max_position_embeddings
     SCREAMING_SNAKE_CASE:Optional[int]  					=    eos_token_id
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    pad_token_id
     SCREAMING_SNAKE_CASE:Dict  					=    bos_token_id
   def 			__UpperCamelCase  (       self	:					Dict							):
     SCREAMING_SNAKE_CASE:Any  					=    ids_tensor([self.batch_size, self.seq_length - 1]   ,self.vocab_size							)
     SCREAMING_SNAKE_CASE:List[Any]  					=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)   ,1							)
     SCREAMING_SNAKE_CASE:int  					=    tf.concat([input_ids, eos_tensor]   ,axis=1							)

     SCREAMING_SNAKE_CASE:Optional[int]  					=    ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size							)

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    self.config_cls(
         vocab_size=self.vocab_size   ,d_model=self.hidden_size   ,encoder_layers=self.num_hidden_layers   ,decoder_layers=self.num_hidden_layers   ,encoder_attention_heads=self.num_attention_heads   ,decoder_attention_heads=self.num_attention_heads   ,encoder_ffn_dim=self.intermediate_size   ,decoder_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_ids=[2]   ,bos_token_id=self.bos_token_id   ,pad_token_id=self.pad_token_id   ,decoder_start_token_id=self.pad_token_id   ,**self.config_updates   ,)
     SCREAMING_SNAKE_CASE:List[Any]  					=    prepare_blenderbot_inputs_dict(lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_							)
     return config, inputs_dict







   def 			__UpperCamelCase  (       self	:					List[str]   ,SCREAMING_SNAKE_CASE__	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					Optional[int]							):
     SCREAMING_SNAKE_CASE:List[str]  					=    TFBlenderbotModel(config=lowerCamelCase_							).get_decoder()
     SCREAMING_SNAKE_CASE:Any  					=    inputs_dict["input_ids"]

     SCREAMING_SNAKE_CASE:Optional[int]  					=    input_ids[:1, :]
     SCREAMING_SNAKE_CASE:Tuple  					=    inputs_dict["attention_mask"][:1, :]
     SCREAMING_SNAKE_CASE:Tuple  					=    inputs_dict["head_mask"]
     SCREAMING_SNAKE_CASE:Any  					=    1

     # first forward pass
     SCREAMING_SNAKE_CASE:Dict  					=    model(lowerCamelCase_   ,attention_mask=lowerCamelCase_   ,head_mask=lowerCamelCase_   ,use_cache=lowerCamelCase_							)

     SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE:List[Any]  					=    outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    model(lowerCamelCase_   ,attention_mask=lowerCamelCase_							)[0]
     SCREAMING_SNAKE_CASE:int  					=    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
     SCREAMING_SNAKE_CASE:List[str]  					=    int(ids_tensor((1,)   ,output_from_past.shape[-1]							)							)
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    output_from_no_past[:, -3:, random_slice_idx]
     SCREAMING_SNAKE_CASE:Optional[int]  					=    output_from_past[:, :, random_slice_idx]

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





def       A_     (       snake_case					,				snake_case					,				snake_case					,				snake_case=None					,				snake_case=None					,				snake_case=None					,				snake_case=None					,				snake_case=None					,				):





  if attention_mask is None:
    SCREAMING_SNAKE_CASE:Dict  					=    tf.cast(tf.math.not_equal(snake_case					,				config.pad_token_id       )					,				tf.inta       )
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE:Dict  					=    tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape					,				dtype=tf.inta       ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,				config.pad_token_id       )					,				tf.inta       ),
        ]					,				axis=-1					,				)
  if head_mask is None:
    SCREAMING_SNAKE_CASE:Tuple  					=    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE:str  					=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }





@require_tf
class 							_snake_case       (				_a		,     _a		,     unittest.TestCase  ):
   _A				:   Union[str, Any]			      =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
   _A				:   List[str]			      =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
   _A				:   int			      =    (
       {
           '''conversational''': TFBlenderbotForConditionalGeneration,
           '''feature-extraction''': TFBlenderbotModel,
           '''summarization''': TFBlenderbotForConditionalGeneration,
           '''text2text-generation''': TFBlenderbotForConditionalGeneration,
           '''translation''': TFBlenderbotForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   _A				:   Dict			      =    True
   _A				:   Optional[int]			      =    False
   _A				:   List[str]			      =    False
   def 			__UpperCamelCase  (       self	:					str							):
     SCREAMING_SNAKE_CASE:Any  					=    TFBlenderbotModelTester(self							)
     SCREAMING_SNAKE_CASE:Dict  					=    ConfigTester(self   ,config_class=lowerCamelCase_							)
   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     self.config_tester.run_common_tests()







   def 			__UpperCamelCase  (       self	:					str							):
     SCREAMING_SNAKE_CASE:int  					=    self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_							)





@require_tokenizers
@require_tf
class 							_snake_case       (				unittest.TestCase  ):
   _A				:   Dict			      =    ['''My friends are cool but they eat too many carbs.''']
   _A				:   Optional[Any]			      =    '''facebook/blenderbot-400M-distill'''
   @cached_property
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     return BlenderbotTokenizer.from_pretrained(self.model_name							)
   @cached_property
   def 			__UpperCamelCase  (       self	:					Any							):
     SCREAMING_SNAKE_CASE:str  					=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
     return model







   @slow
   def 			__UpperCamelCase  (       self	:					List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    self.tokenizer(self.src_text   ,return_tensors="tf"							)
     SCREAMING_SNAKE_CASE:Tuple  					=    self.model.generate(
         model_inputs.input_ids   ,)
     SCREAMING_SNAKE_CASE:List[str]  					=    self.tokenizer.batch_decode(generated_ids.numpy()   ,skip_special_tokens=lowerCamelCase_							)[0]
     assert (
         generated_words
         == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
     )











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


_lowercase: Optional[int]      			=		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							XLMProphetNetTokenizer
	__A							=							False
	__A							=							True




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# We have a SentencePiece fixture for testing
						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)
						tokenizer.save_pretrained(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "[PAD]"
						a					     =   0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_		)		,       lowerCamelCase_		)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_		)		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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(lowerCamelCase_		)		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.assertEqual(self.get_tokenizer().vocab_size		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)

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

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

						a					     =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        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",
						        ".",
						    ]		,       )
						a					     =   tokenizer.convert_tokens_to_ids(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        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]
						    ]		,       )

						a					     =   tokenizer.convert_ids_to_tokens(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        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		):
						"""simple docstring"""

						return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "Hello World!"
						a					     =   [35389, 6672, 49, 2]
						self.assertListEqual(lowerCamelCase_		,       self.big_tokenizer.encode(lowerCamelCase_		)		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 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, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 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=lowerCamelCase_		,       model_name="microsoft/xprophetnet-large-wiki100-cased"		,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"		,       )
