


'''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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   BlenderbotConfig
    a													=   {}
    a													=   "gelu"


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

           _A					:     Optional[Any]     = hidden_dropout_prob
           _A					:     Optional[Any]     = attention_probs_dropout_prob
           _A					:     Dict     = max_position_embeddings
           _A					:     Union[str, Any]     = eos_token_id
           _A					:     Optional[Any]     = pad_token_id
           _A					:     Dict     = bos_token_id


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

           _A					:     Dict     = ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size)

           _A					:     Optional[int]     = 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				,							)
           _A					:     List[str]     = prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           return config, inputs_dict


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Optional[Any]     = TFBlenderbotModel(config=SCREAMING_SNAKE_CASE).get_decoder()
           _A					:     str     = inputs_dict['input_ids']

           _A					:     Union[str, Any]     = input_ids[:1, :]
           _A					:     Tuple     = inputs_dict['attention_mask'][:1, :]
           _A					:     Any     = inputs_dict['head_mask']
           _A					:     Any     = 1

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

           _A  ,			_A					:     Optional[int]     = outputs.to_tuple()

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

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

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict  ,lowerCamelCase					:      Any=None  ,lowerCamelCase					:      int=None  ,lowerCamelCase					:      Optional[Any]=None  ,lowerCamelCase					:      Optional[Any]=None  ,lowerCamelCase					:      Union[str, Any]=None  ,):
       if attention_mask is None:
              _A					:     Any     = tf.cast(tf.math.not_equal(lowerCamelCase  ,config.pad_token_id       )  ,tf.inta       )
       if decoder_attention_mask is None:
              _A					:     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:
              _A					:     List[str]     = tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              _A					:     str     = tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              _A					:     List[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 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
    a													=   (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
    a													=   (
        {
            "conversational": TFBlenderbotForConditionalGeneration,
            "feature-extraction": TFBlenderbotModel,
            "summarization": TFBlenderbotForConditionalGeneration,
            "text2text-generation": TFBlenderbotForConditionalGeneration,
            "translation": TFBlenderbotForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    a													=   True
    a													=   False
    a													=   False


    def        A   (  self      :							int):
           _A					:     Dict     = TFBlenderbotModelTester(self)
           _A					:     Tuple     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           self.config_tester.run_common_tests()


    def        A   (  self      :							int):
           _A					:     List[Any]     = self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE)




@require_tokenizers
@require_tf
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""
    a													=   ["My friends are cool but they eat too many carbs."]
    a													=   "facebook/blenderbot-400M-distill"


    @cached_property
    def        A   (  self      :							Optional[Any]):
           return BlenderbotTokenizer.from_pretrained(self.model_name)


    @cached_property
    def        A   (  self      :							List[Any]):
           _A					:     Any     = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
           return model


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










'''simple docstring'''







class 			__lowerCamelCase			:  # Public class to implement a graph
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           _A					:     List[Any]     = row
           _A					:     Union[str, Any]     = col
           _A					:     List[str]     = graph


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           # Checking all 8 elements surrounding nth element
           _A					:     Tuple     = [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           _A					:     Dict     = [-1, 0, 1, -1, 1, -1, 0, 1]
           _A					:     List[Any]     = True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE):
                         self.diffs(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):  # And finally, count all islands.
           _A					:     Dict     = [[False for j in range(self.COL)] for i in range(self.ROW)]
           _A					:     Union[str, Any]     = 0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                                count += 1
           return count






