






'''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 				lowercase       :


				'''simple docstring'''

				lowerCAmelCase__      				=							BlenderbotConfig
				lowerCAmelCase__      				=							{}
				lowerCAmelCase__      				=							"""gelu"""

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

					'''simple docstring'''




					lowerCamelCase__  =     parent
					lowerCamelCase__  =     batch_size
					lowerCamelCase__  =     seq_length
					lowerCamelCase__  =     is_training
					lowerCamelCase__  =     use_labels
					lowerCamelCase__  =     vocab_size
					lowerCamelCase__  =     hidden_size
					lowerCamelCase__  =     num_hidden_layers
					lowerCamelCase__  =     num_attention_heads
					lowerCamelCase__  =     intermediate_size

					lowerCamelCase__  =     hidden_dropout_prob
					lowerCamelCase__  =     attention_probs_dropout_prob
					lowerCamelCase__  =     max_position_embeddings
					lowerCamelCase__  =     eos_token_id
					lowerCamelCase__  =     pad_token_id
					lowerCamelCase__  =     bos_token_id

				def    a__				(					self     :     Union[str, Any]     )							->							Any:

					'''simple docstring'''




					lowerCamelCase__  =     ids_tensor([self.batch_size, self.seq_length - 1]				,     self.vocab_size     )
					lowerCamelCase__  =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )				,     1     )
					lowerCamelCase__  =     tf.concat([input_ids, eos_tensor]				,     axis=1     )

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

					lowerCamelCase__  =     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__  =     prepare_blenderbot_inputs_dict(lowerCAmelCase__				,     lowerCAmelCase__				,     lowerCAmelCase__     )
					return config, inputs_dict




				def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Tuple     )							->							Tuple:

					'''simple docstring'''




					lowerCamelCase__  =     TFBlenderbotModel(config=lowerCAmelCase__     ).get_decoder()
					lowerCamelCase__  =     inputs_dict["input_ids"]

					lowerCamelCase__  =     input_ids[:1, :]
					lowerCamelCase__  =     inputs_dict["attention_mask"][:1, :]
					lowerCamelCase__  =     inputs_dict["head_mask"]
					lowerCamelCase__  =     1

					# first forward pass
					lowerCamelCase__  =     model(lowerCAmelCase__				,     attention_mask=lowerCAmelCase__				,     head_mask=lowerCAmelCase__				,     use_cache=lowerCAmelCase__     )

					lowerCamelCase__  =     outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					lowerCamelCase__  =     ids_tensor((self.batch_size, 3)				,     config.vocab_size     )
					lowerCamelCase__  =     tf.cast(ids_tensor((self.batch_size, 3)				,     2     )				,     tf.inta     )

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

					lowerCamelCase__  =     model(lowerCAmelCase__				,     attention_mask=lowerCAmelCase__     )[0]
					lowerCamelCase__  =     model(lowerCAmelCase__				,     attention_mask=lowerCAmelCase__				,     past_key_values=lowerCAmelCase__     )[0]

					self.parent.assertEqual(next_tokens.shape[1]				,     output_from_past.shape[1]     )

					# select random slice
					lowerCamelCase__  =     int(ids_tensor((1,)				,     output_from_past.shape[-1]     )     )
					lowerCamelCase__  =     output_from_no_past[:, -3:, random_slice_idx]
					lowerCamelCase__  =     output_from_past[:, :, random_slice_idx]

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     ):






	if attention_mask is None:
		lowerCamelCase__  =     tf.cast(tf.math.not_equal(_lowercase				,     config.pad_token_id)				,     tf.inta)
	if decoder_attention_mask is None:
		lowerCamelCase__  =     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__  =     tf.ones((config.encoder_layers, config.encoder_attention_heads))
	if decoder_head_mask is None:
		lowerCamelCase__  =     tf.ones((config.decoder_layers, config.decoder_attention_heads))
	if cross_attn_head_mask is None:
		lowerCamelCase__  =     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 				lowercase       (							a__ ,       a__ ,       unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				lowerCAmelCase__      				=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				lowerCAmelCase__      				=							(
				    {
				        """conversational""": TFBlenderbotForConditionalGeneration,
				        """feature-extraction""": TFBlenderbotModel,
				        """summarization""": TFBlenderbotForConditionalGeneration,
				        """text2text-generation""": TFBlenderbotForConditionalGeneration,
				        """translation""": TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				lowerCAmelCase__      				=							True
				lowerCAmelCase__      				=							False
				lowerCAmelCase__      				=							False

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

					'''simple docstring'''




					lowerCamelCase__  =     TFBlenderbotModelTester(self     )
					lowerCamelCase__  =     ConfigTester(self				,     config_class=lowerCAmelCase__     )

				def    a__				(					self     :     Optional[int]     )							->							List[str]:

					'''simple docstring'''




					self.config_tester.run_common_tests()




				def    a__				(					self     :     Dict     )							->							int:

					'''simple docstring'''




					lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__     )
@require_tokenizers
@require_tf
class 				lowercase       (							unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							["""My friends are cool but they eat too many carbs."""]
				lowerCAmelCase__      				=							"""facebook/blenderbot-400M-distill"""

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

					'''simple docstring'''




					return BlenderbotTokenizer.from_pretrained(self.model_name     )

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

					'''simple docstring'''




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




				@slow
				def    a__				(					self     :     Dict     )							->							Union[str, Any]:

					'''simple docstring'''




					lowerCamelCase__  =     self.tokenizer(self.src_text				,     return_tensors="tf"     )
					lowerCamelCase__  =     self.model.generate(
					    model_inputs.input_ids				,     )
					lowerCamelCase__  =     self.tokenizer.batch_decode(generated_ids.numpy()				,     skip_special_tokens=lowerCAmelCase__     )[0]
					assert (
					    generated_words
					    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
					)









'''simple docstring'''





from __future__ import annotations
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     order

     # a_{0} ... a_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order
     # b_{0} ... b_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     lowerCamelCase__  =     [0.0] * self.order
     # y[n-1] ... y[n-k]
     lowerCamelCase__  =     [0.0] * self.order

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     list[float]				,     __lowerCamelCase     :     list[float]     )							->							None:

     '''simple docstring'''




     if len(__lowerCamelCase     ) < self.order:
      lowerCamelCase__  =     [1.0, *a_coeffs]

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected a_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected b_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     lowerCamelCase__  =     a_coeffs
     lowerCamelCase__  =     b_coeffs




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     float     )							->							float:

     '''simple docstring'''




     lowerCamelCase__  =     0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1				,     self.order + 1     ):
      result += (
          self.b_coeffs[i] * self.input_history[i - 1]
          - self.a_coeffs[i] * self.output_history[i - 1]
      )

     lowerCamelCase__  =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     lowerCamelCase__  =     self.input_history[:-1]
     lowerCamelCase__  =     self.output_history[:-1]

     lowerCamelCase__  =     sample
     lowerCamelCase__  =     result

     return result

