

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     A__:
						"""simple docstring"""






						_A  :							List[Any]     =       BlenderbotSmallConfig
						_A  :							Any     =       {}
						_A  :							Tuple     =       """gelu"""





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

									a_		:		int      				=      hidden_dropout_prob
									a_		:		Dict      				=      attention_probs_dropout_prob
									a_		:		Optional[Any]      				=      max_position_embeddings
									a_		:		str      				=      eos_token_id
									a_		:		Dict      				=      pad_token_id
									a_		:		Tuple      				=      bos_token_id





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

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

									a_		:		Dict      				=      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_		:		Any      				=      prepare_blenderbot_small_inputs_dict(_lowercase   ,	_lowercase   ,	_lowercase  )
									return config, inputs_dict





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase  )			->      str:
									a_		:		List[Any]      				=      TFBlenderbotSmallModel(config=_lowercase  ).get_decoder()
									a_		:		List[str]      				=      inputs_dict["""input_ids"""]

									a_		:		int      				=      input_ids[:1, :]
									a_		:		str      				=      inputs_dict["""attention_mask"""][:1, :]
									a_		:		Tuple      				=      inputs_dict["""head_mask"""]
									a_		:		int      				=      1

									# first forward pass
									a_		:		List[str]      				=      model(_lowercase   ,	attention_mask=_lowercase   ,	head_mask=_lowercase   ,	use_cache=_lowercase  )

									a_		:		int      				=      outputs.to_tuple()

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

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

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

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







def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__=None   ,     a__=None   ,     a__=None   ,     a__=None   ,     a__=None   ,     ):

			'''simple docstring'''



			if attention_mask is None:
						a_		:		Union[str, Any]      				=      tf.cast(tf.math.not_equal(a__   ,     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[Any]      				=      tf.ones((config.encoder_layers, config.encoder_attention_heads))
			if decoder_head_mask is None:
						a_		:		Optional[Any]      				=      tf.ones((config.decoder_layers, config.decoder_attention_heads))
			if cross_attn_head_mask is None:
						a_		:		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     A__(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE,   unittest.TestCase				):
						"""simple docstring"""






						_A  :							str     =       (
						    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
						)
						_A  :							Any     =       (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
						_A  :							int     =       (
						    {
						        """conversational""": TFBlenderbotSmallForConditionalGeneration,
						        """feature-extraction""": TFBlenderbotSmallModel,
						        """summarization""": TFBlenderbotSmallForConditionalGeneration,
						        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
						        """translation""": TFBlenderbotSmallForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_A  :							List[Any]     =       True
						_A  :							str     =       False
						_A  :							Any     =       False





						def        UpperCamelCase__ (  self  )			->      Union[str, Any]:
									a_		:		int      				=      TFBlenderbotSmallModelTester(self  )
									a_		:		List[str]      				=      ConfigTester(self   ,	config_class=_lowercase  )





						def        UpperCamelCase__ (  self  )			->      List[Any]:
									self.config_tester.run_common_tests()





						def        UpperCamelCase__ (  self  )			->      int:
									a_		:		Any      				=      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"""






						_A  :							int     =       [
						    """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?"""
						]
						_A  :							Tuple     =       """facebook/blenderbot_small-90M"""





						@cached_property
						def        UpperCamelCase__ (  self  )			->      Dict:
									# use "old" tokenizer here because of bug when downloading new tokenizer
									return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""  )





						@cached_property
						def        UpperCamelCase__ (  self  )			->      Any:
									a_		:		Tuple      				=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
									return model





						@slow
						def        UpperCamelCase__ (  self  )			->      Tuple:
									a_		:		List[str]      				=      self.tokenizer(self.src_text   ,	return_tensors="""tf"""  )
									a_		:		Dict      				=      self.model.generate(
									    model_inputs.input_ids   ,	attention_mask=model_inputs.attention_mask   ,	num_beams=2   ,	use_cache=_lowercase   ,	)
									a_		:		Any      				=      self.tokenizer.batch_decode(generated_ids.numpy()   ,	skip_special_tokens=_lowercase  )[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.",
									)




_lowerCamelCase					   ={
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355818,
}





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
  lowerCamelCase		:	Dict							=      (
      F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Valid values are: {", ".join(lowerCamelCase							)}'''
  )
  raise ValueError(lowerCamelCase							)
 return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




