
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      UpperCamelCase :
    __UpperCamelCase         =BlenderbotSmallConfig
    __UpperCamelCase         ={}
    __UpperCamelCase         ="gelu"

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      List[Any]=1_3							,    snake_case__ :      List[Any]=7							,    snake_case__ :      str=True							,    snake_case__ :      Optional[Any]=False							,    snake_case__ :      Union[str, Any]=9_9							,    snake_case__ :      Optional[int]=3_2							,    snake_case__ :      str=2							,    snake_case__ :      str=4							,    snake_case__ :      Dict=3_7							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Optional[Any]=0.1							,    snake_case__ :      Optional[int]=2_0							,    snake_case__ :      int=2							,    snake_case__ :      str=1							,    snake_case__ :      Union[str, Any]=0							,    ):

     """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_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

    def UpperCamelCase			(			self :      int    ):

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

     SCREAMING_SNAKE_CASE								=			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								=			prepare_blenderbot_small_inputs_dict(snake_case__							,    snake_case__							,    snake_case__    )
     return config, inputs_dict




    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFBlenderbotSmallModel(config=snake_case__    ).get_decoder()
     SCREAMING_SNAKE_CASE								=			inputs_dict['input_ids']

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

     # first forward pass
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    head_mask=snake_case__							,    use_cache=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(snake_case__							,    attention_mask=snake_case__    )[0]
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    past_key_values=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(snake_case__							,    snake_case__							,    rtol=1E-3    )


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Optional[int]=None		,							_UpperCamelCase		:  Dict=None		,							_UpperCamelCase		:  Tuple=None		,							_UpperCamelCase		:  Optional[Any]=None		,							_UpperCamelCase		:  List[Any]=None		,							)		->							Union[str, Any]:
 '''simple docstring'''


 if attention_mask is None:
  SCREAMING_SNAKE_CASE								=			tf.cast(tf.math.not_equal(_UpperCamelCase		,							config.pad_token_id	)		,							tf.inta	)
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE								=			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								=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE								=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE								=			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      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    __UpperCamelCase         =(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    __UpperCamelCase         =(
        {
            "conversational": TFBlenderbotSmallForConditionalGeneration,
            "feature-extraction": TFBlenderbotSmallModel,
            "summarization": TFBlenderbotSmallForConditionalGeneration,
            "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
            "translation": TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __UpperCamelCase         =True
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFBlenderbotSmallModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     self.config_tester.run_common_tests()




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*snake_case__    )




@require_tokenizers
@require_tf
class      UpperCamelCase (     unittest.TestCase   ):
    __UpperCamelCase         =[
        "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?"
    ]
    __UpperCamelCase         ="facebook/blenderbot_small-90M"

    @cached_property
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'    )

    @cached_property
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
     return model




    @slow
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.tokenizer(self.src_text							,    return_tensors='tf'    )
     SCREAMING_SNAKE_CASE								=			self.model.generate(
         model_inputs.input_ids							,    attention_mask=model_inputs.attention_mask							,    num_beams=2							,    use_cache=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			self.tokenizer.batch_decode(generated_ids.numpy()							,    skip_special_tokens=snake_case__    )[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.",
     )




import random


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  float		,							_UpperCamelCase		:  bool = False	)		->							dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			{i: [] for i in range(_UpperCamelCase	)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(_UpperCamelCase	)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(_UpperCamelCase	):
  for j in range(i + 1		,							_UpperCamelCase	):
   if random.random() < probability:
    graph[i].append(_UpperCamelCase	)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(_UpperCamelCase	)
 return graph


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							dict:
 '''simple docstring'''


 return {
     i: [j for j in range(_UpperCamelCase	) if i != j] for i in range(_UpperCamelCase	)
 }


if __name__ == "__main__":
      import doctest

      doctest.testmod()

