



"""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       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							BlenderbotConfig
						__UpperCamelCase						=							{}
						__UpperCamelCase						=							"""gelu"""






						def __init__(					self :Optional[int]   ,  lowercase_ :Optional[Any]   ,  lowercase_ :Any=13   ,  lowercase_ :Optional[int]=7   ,  lowercase_ :Union[str, Any]=True   ,  lowercase_ :Union[str, Any]=False   ,  lowercase_ :List[Any]=99   ,  lowercase_ :str=32   ,  lowercase_ :Union[str, Any]=2   ,  lowercase_ :Optional[Any]=4   ,  lowercase_ :Any=37   ,  lowercase_ :Optional[Any]=0.1   ,  lowercase_ :Any=0.1   ,  lowercase_ :Optional[Any]=20   ,  lowercase_ :Dict=2   ,  lowercase_ :Optional[int]=1   ,  lowercase_ :Dict=0   ,  )				->			Dict:
												UpperCAmelCase             = parent
												UpperCAmelCase             = batch_size
												UpperCAmelCase             = seq_length
												UpperCAmelCase             = is_training
												UpperCAmelCase             = use_labels
												UpperCAmelCase             = vocab_size
												UpperCAmelCase             = hidden_size
												UpperCAmelCase             = num_hidden_layers
												UpperCAmelCase             = num_attention_heads
												UpperCAmelCase             = intermediate_size

												UpperCAmelCase             = hidden_dropout_prob
												UpperCAmelCase             = attention_probs_dropout_prob
												UpperCAmelCase             = max_position_embeddings
												UpperCAmelCase             = eos_token_id
												UpperCAmelCase             = pad_token_id
												UpperCAmelCase             = bos_token_id






						def 	UpperCAmelCase__     (					self :str					)				->			List[Any]:
												UpperCAmelCase             = ids_tensor([self.batch_size, self.seq_length - 1]   ,  self.vocab_size					)
												UpperCAmelCase             = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)   ,  1					)
												UpperCAmelCase             = tf.concat([input_ids, eos_tensor]   ,  axis=1					)

												UpperCAmelCase             = ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size					)

												UpperCAmelCase             = 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   ,  )
												UpperCAmelCase             = prepare_blenderbot_inputs_dict(lowercase_   ,  lowercase_   ,  lowercase_					)
												return config, inputs_dict






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :str   ,  lowercase_ :List[str]					)				->			Optional[int]:
												UpperCAmelCase             = TFBlenderbotModel(config=lowercase_					).get_decoder()
												UpperCAmelCase             = inputs_dict['input_ids']

												UpperCAmelCase             = input_ids[:1, :]
												UpperCAmelCase             = inputs_dict['attention_mask'][:1, :]
												UpperCAmelCase             = inputs_dict['head_mask']
												UpperCAmelCase             = 1

												# first forward pass
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  head_mask=lowercase_   ,  use_cache=lowercase_					)

												UpperCAmelCase      ,    UpperCAmelCase             = outputs.to_tuple()

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

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

												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_					)[0]
												UpperCAmelCase             = model(lowercase_   ,  attention_mask=lowercase_   ,  past_key_values=lowercase_					)[0]

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

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

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


def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	lowercase_=None		,	):
						if attention_mask is None:
												UpperCAmelCase             = tf.cast(tf.math.not_equal(lowercase_		,	config.pad_token_id						)		,	tf.inta						)
						if decoder_attention_mask is None:
												UpperCAmelCase             = 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:
												UpperCAmelCase             = tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												UpperCAmelCase             = tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												UpperCAmelCase             = 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       A_			(     SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
						__UpperCamelCase						=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
						__UpperCamelCase						=							(
						    {
						        """conversational""": TFBlenderbotForConditionalGeneration,
						        """feature-extraction""": TFBlenderbotModel,
						        """summarization""": TFBlenderbotForConditionalGeneration,
						        """text2text-generation""": TFBlenderbotForConditionalGeneration,
						        """translation""": TFBlenderbotForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						__UpperCamelCase						=							True
						__UpperCamelCase						=							False
						__UpperCamelCase						=							False






						def 	UpperCAmelCase__     (					self :int					)				->			List[Any]:
												UpperCAmelCase             = TFBlenderbotModelTester(self					)
												UpperCAmelCase             = ConfigTester(self   ,  config_class=lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Any:
												self.config_tester.run_common_tests()






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			List[str]:
												UpperCAmelCase             = self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*lowercase_					)







@require_tokenizers
@require_tf
class       A_			(     unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							["""My friends are cool but they eat too many carbs."""]
						__UpperCamelCase						=							"""facebook/blenderbot-400M-distill"""






						@cached_property
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Optional[Any]:
												return BlenderbotTokenizer.from_pretrained(self.model_name					)






						@cached_property
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Dict:
												UpperCAmelCase             = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
												return model






						@slow
						def 	UpperCAmelCase__     (					self :int					)				->			Optional[int]:
												UpperCAmelCase             = self.tokenizer(self.src_text   ,  return_tensors='tf'					)
												UpperCAmelCase             = self.model.generate(
												    model_inputs.input_ids   ,  )
												UpperCAmelCase             = self.tokenizer.batch_decode(generated_ids.numpy()   ,  skip_special_tokens=lowercase_					)[0]
												assert (
												    generated_words
												    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
												)







"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    {
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   UpperCAmelCase_					:					Optional[int]			     =    Dataset.from_dict(__lowerCamelCase  )
   return dataset


class A_	(lowercase__      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    get_dataset()
     UpperCAmelCase_					:					Any			     =    make_duplicate_clusters(lowercase_				,   0.85  )
     self.assertEqual(len(duplicate_clusters[0]  )				,   2  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    get_dataset()
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    deduplicate_dataset(lowercase_  )
     self.assertEqual(len(lowercase_  )				,   2  )
     print(lowercase_  )
     self.assertEqual(duplicate_clusters[0][0]["copies"]				,   2  )
     self.assertEqual(duplicate_clusters[0][0]["is_extreme"]				,   lowercase_  )


