






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	:




      """simple docstring"""



      A    =     BlenderbotSmallConfig
      A    =     {}
      A    =     '''gelu'''
      def __init__( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase=13     ,						_lowerCAmelCase=7     ,						_lowerCAmelCase=True     ,						_lowerCAmelCase=False     ,						_lowerCAmelCase=99     ,						_lowerCAmelCase=32     ,						_lowerCAmelCase=2     ,						_lowerCAmelCase=4     ,						_lowerCAmelCase=37     ,						_lowerCAmelCase=0.1     ,						_lowerCAmelCase=0.1     ,						_lowerCAmelCase=20     ,						_lowerCAmelCase=2     ,						_lowerCAmelCase=1     ,						_lowerCAmelCase=0     ,						):



          '''simple docstring'''
          lowerCAmelCase__ :Dict   				=					parent
          lowerCAmelCase__ :Tuple   				=					batch_size
          lowerCAmelCase__ :Optional[int]   				=					seq_length
          lowerCAmelCase__ :Optional[int]   				=					is_training
          lowerCAmelCase__ :Optional[Any]   				=					use_labels
          lowerCAmelCase__ :Optional[int]   				=					vocab_size
          lowerCAmelCase__ :List[Any]   				=					hidden_size
          lowerCAmelCase__ :List[str]   				=					num_hidden_layers
          lowerCAmelCase__ :Optional[Any]   				=					num_attention_heads
          lowerCAmelCase__ :Optional[int]   				=					intermediate_size

          lowerCAmelCase__ :List[str]   				=					hidden_dropout_prob
          lowerCAmelCase__ :Optional[Any]   				=					attention_probs_dropout_prob
          lowerCAmelCase__ :List[Any]   				=					max_position_embeddings
          lowerCAmelCase__ :List[Any]   				=					eos_token_id
          lowerCAmelCase__ :List[str]   				=					pad_token_id
          lowerCAmelCase__ :int   				=					bos_token_id
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :str   				=					ids_tensor([self.batch_size, self.seq_length - 1]     ,						self.vocab_size		)
          lowerCAmelCase__ :Union[str, Any]   				=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)     ,						1		)
          lowerCAmelCase__ :List[Any]   				=					tf.concat([input_ids, eos_tensor]     ,						axis=1		)

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

          lowerCAmelCase__ :str   				=					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     ,						)
          lowerCAmelCase__ :Tuple   				=					prepare_blenderbot_small_inputs_dict(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE		)
          return config, inputs_dict



      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__ :Any   				=					TFBlenderbotSmallModel(config=_SCREAMING_SNAKE_CASE		).get_decoder()
          lowerCAmelCase__ :List[str]   				=					inputs_dict["input_ids"]

          lowerCAmelCase__ :Dict   				=					input_ids[:1, :]
          lowerCAmelCase__ :Optional[Any]   				=					inputs_dict["attention_mask"][:1, :]
          lowerCAmelCase__ :str   				=					inputs_dict["head_mask"]
          lowerCAmelCase__ :List[str]   				=					1

          # first forward pass
          lowerCAmelCase__ :Union[str, Any]   				=					model(_SCREAMING_SNAKE_CASE     ,						attention_mask=_SCREAMING_SNAKE_CASE     ,						head_mask=_SCREAMING_SNAKE_CASE     ,						use_cache=_SCREAMING_SNAKE_CASE		)

          lowerCAmelCase__ :int   				=					outputs.to_tuple()

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

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

          lowerCAmelCase__ :Optional[int]   				=					model(_SCREAMING_SNAKE_CASE     ,						attention_mask=_SCREAMING_SNAKE_CASE		)[0]
          lowerCAmelCase__ :List[str]   				=					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
          lowerCAmelCase__ :Any   				=					int(ids_tensor((1,)     ,						output_from_past.shape[-1]		)		)
          lowerCAmelCase__ :Union[str, Any]   				=					output_from_no_past[:, -3:, random_slice_idx]
          lowerCAmelCase__ :Union[str, 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 					snake_case__				(				UpperCAmelCase				:	Tuple     ,      UpperCAmelCase				:	Tuple     ,      UpperCAmelCase				:	List[Any]     ,      UpperCAmelCase				:	Tuple=None     ,      UpperCAmelCase				:	Union[str, Any]=None     ,      UpperCAmelCase				:	List[str]=None     ,      UpperCAmelCase				:	Union[str, Any]=None     ,      UpperCAmelCase				:	Any=None     ,      ):
    if attention_mask is None:
        lowerCAmelCase__ :Union[str, Any]   				=					tf.cast(tf.math.not_equal(__snake_case     ,      config.pad_token_id							)     ,      tf.inta							)
    if decoder_attention_mask is None:
        lowerCAmelCase__ :List[str]   				=					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:
        lowerCAmelCase__ :str   				=					tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
    if decoder_head_mask is None:
        lowerCAmelCase__ :List[str]   				=					tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
    if cross_attn_head_mask is None:
        lowerCAmelCase__ :int   				=					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		):




      """simple docstring"""



      A    =     (
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      A    =     (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      A    =     (
          {
              '''conversational''': TFBlenderbotSmallForConditionalGeneration,
              '''feature-extraction''': TFBlenderbotSmallModel,
              '''summarization''': TFBlenderbotSmallForConditionalGeneration,
              '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
              '''translation''': TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      A    =     True
      A    =     False
      A    =     False
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Optional[int]   				=					TFBlenderbotSmallModelTester(self		)
          lowerCAmelCase__ :List[str]   				=					ConfigTester(self     ,						config_class=_SCREAMING_SNAKE_CASE		)
      def       snake_case_   ( self		):



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



      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :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 						_UpperCAmelCase	(	unittest.TestCase		):




      """simple docstring"""



      A    =     [
          '''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    =     '''facebook/blenderbot_small-90M'''
      @cached_property
      def       snake_case_   ( self		):



          '''simple docstring'''
          return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)
      @cached_property
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Tuple   				=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
          return model



      @slow
      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Any   				=					self.tokenizer(self.src_text     ,						return_tensors="tf"		)
          lowerCAmelCase__ :Optional[Any]   				=					self.model.generate(
              model_inputs.input_ids     ,						attention_mask=model_inputs.attention_mask     ,						num_beams=2     ,						use_cache=_SCREAMING_SNAKE_CASE     ,						)
          lowerCAmelCase__ :List[str]   				=					self.tokenizer.batch_decode(generated_ids.numpy()     ,						skip_special_tokens=_SCREAMING_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.",
          )








"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()