from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class    snake_case       :
  lowercase_								=  BlenderbotSmallConfig
  lowercase_								=  {}
  lowercase_								=  'gelu'




  def __init__(   self  :						List[Any]  ,			a_  :						int  ,			a_  :						Any=13  ,			a_  :						List[str]=7  ,			a_  :						Optional[int]=True  ,			a_  :						Tuple=False  ,			a_  :						Optional[int]=99  ,			a_  :						Tuple=32  ,			a_  :						Union[str, Any]=2  ,			a_  :						Union[str, Any]=4  ,			a_  :						str=37  ,			a_  :						List[Any]=0.1  ,			a_  :						int=0.1  ,			a_  :						List[Any]=20  ,			a_  :						Optional[Any]=2  ,			a_  :						List[str]=1  ,			a_  :						Tuple=0  ,			)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		parent
       SCREAMING_SNAKE_CASE__					:					Tuple      =		batch_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		is_training
       SCREAMING_SNAKE_CASE__					:					Tuple      =		use_labels
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					int      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		intermediate_size

       SCREAMING_SNAKE_CASE__					:					Tuple      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Any      =		pad_token_id
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bos_token_id




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		ids_tensor([self.batch_size, self.seq_length - 1]  ,			self.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)  ,			1					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tf.concat([input_ids, eos_tensor]  ,			axis=1					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size					)

       SCREAMING_SNAKE_CASE__					:					List[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_small_inputs_dict(a_  ,			a_  ,			a_					)
       return config, inputs_dict




  def 						__lowercase(   self  :						Dict  ,			a_  :						Dict  ,			a_  :						str					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TFBlenderbotSmallModel(config=a_					).get_decoder()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		input_ids[:1, :]
       SCREAMING_SNAKE_CASE__					:					List[str]      =		inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE__					:					Any      =		inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE__					:					str      =		1

       # first forward pass
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			attention_mask=a_  ,			head_mask=a_  ,			use_cache=a_					)

       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__					:					List[Any]      =		tf.cast(ids_tensor((self.batch_size, 3)  ,			2					)  ,			tf.inta					)

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

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

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







def      _a					(   lowercase__	:      Any  ,	lowercase__	:      Any  ,	lowercase__	:      List[str]  ,	lowercase__	:      Any=None  ,	lowercase__	:      str=None  ,	lowercase__	:      int=None  ,	lowercase__	:      str=None  ,	lowercase__	:      Tuple=None  ,	):







     '''simple docstring'''


     if attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		tf.cast(tf.math.not_equal(lowercase__  ,	config.pad_token_id			)  ,	tf.inta			)
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		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__					:					List[str]      =		tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
     if cross_attn_head_mask is None:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		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       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
  )
  lowercase_								=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
  lowercase_								=  (
      {
          'conversational': TFBlenderbotSmallForConditionalGeneration,
          'feature-extraction': TFBlenderbotSmallModel,
          'summarization': TFBlenderbotSmallForConditionalGeneration,
          'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
          'translation': TFBlenderbotSmallForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowercase_								=  True
  lowercase_								=  False
  lowercase_								=  False




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		TFBlenderbotSmallModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		ConfigTester(self  ,			config_class=a_					)




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




       """simple docstring"""
       self.config_tester.run_common_tests()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*a_					)




@require_tokenizers
@require_tf
class    snake_case       (   unittest.TestCase    ):
  lowercase_								=  [
      'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
      '  i\'m going to throw up.\nand why is that?'
  ]
  lowercase_								=  'facebook/blenderbot_small-90M'




  @cached_property
  def 						__lowercase(   self  :						int					)->							Any:




       """simple docstring"""
       # use "old" tokenizer here because of bug when downloading new tokenizer
       return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'					)




  @cached_property
  def 						__lowercase(   self  :						Optional[int]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
       return model




  @slow
  def 						__lowercase(   self  :						str					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.tokenizer(self.src_text  ,			return_tensors='tf'					)
       SCREAMING_SNAKE_CASE__					:					int      =		self.model.generate(
           model_inputs.input_ids  ,			attention_mask=model_inputs.attention_mask  ,			num_beams=2  ,			use_cache=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.tokenizer.batch_decode(generated_ids.numpy()  ,			skip_special_tokens=a_					)[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )









'''simple docstring'''



from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  List[Any]						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	[]
     for part_id in partition_order:
          __snake_case            =	df.where(f'''SPARK_PARTITION_ID() = {part_id}'''						).collect()
          for row_idx, row in enumerate(_lowerCamelCase						):
               expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict())						)
     return expected_row_ids_and_row_dicts







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Any:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(1_00						).repartition(1						)
     __snake_case            =	Spark(_lowerCamelCase						)
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16						)
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(10						).repartition(2						)
     __snake_case            =	[1, 0]
     __snake_case            =	_generate_iterable_examples(_lowerCamelCase ,						_lowerCamelCase						)  # Reverse the partitions.
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						_lowerCamelCase						)

     for i, (row_id, row_dict) in enumerate(generate_fn()						):
          __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> int:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(10						).repartition(1						)
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(30						).repartition(3						)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch('''numpy.random.Generator'''						) as generator_mock:
          __snake_case            =	lambda _lowerCamelCase						: x.reverse()
          __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[2, 1, 0]						)

          __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shuffle_data_sources(_lowerCamelCase						)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
               __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(20						).repartition(4						)

     # Partitions 0 and 2
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=0 ,						num_workers=2						)
     assert shard_it_a.n_shards == 2
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[0, 2]						)
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __snake_case            =	SparkExamplesIterable(_lowerCamelCase						).shard_data_sources(worker_id=1 ,						num_workers=2						)
     assert shard_it_a.n_shards == 2
     __snake_case            =	_get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase ,						[1, 3]						)
     for i, (row_id, row_dict) in enumerate(_lowerCamelCase						):
          __snake_case       ,     __snake_case            =	expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def       _UpperCamelCase			()-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	pyspark.sql.SparkSession.builder.master('''local[*]'''						).appName('''pyspark'''						).getOrCreate()
     __snake_case            =	spark.range(1_00						).repartition(1						)
     __snake_case            =	Spark(_lowerCamelCase						)
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1						)
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 1_00






