






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  SCREAMING_SNAKE_CASE			:
					'''simple docstring'''


					UpperCamelCase_							:		str			=	BlenderbotConfig
					UpperCamelCase_							:		Optional[Any]			=	{}
					UpperCamelCase_							:		Dict			=	'''gelu'''


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

							SCREAMING_SNAKE_CASE       :      int							=    hidden_dropout_prob
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE       :      List[Any]							=    max_position_embeddings
							SCREAMING_SNAKE_CASE       :      Tuple							=    eos_token_id
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    pad_token_id
							SCREAMING_SNAKE_CASE       :      List[str]							=    bos_token_id


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

							SCREAMING_SNAKE_CASE       :      List[str]							=    ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size      )

							SCREAMING_SNAKE_CASE       :      List[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					, )
							SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_blenderbot_inputs_dict(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )
							return config, inputs_dict






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

							SCREAMING_SNAKE_CASE       :      int							=    input_ids[:1, :]
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    inputs_dict["attention_mask"][:1, :]
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    inputs_dict["head_mask"]
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    1

							# first forward pass
							SCREAMING_SNAKE_CASE       :      str							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, head_mask=UpperCAmelCase_					, use_cache=UpperCAmelCase_      )

							SCREAMING_SNAKE_CASE       :      Any							=    outputs.to_tuple()

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

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

							SCREAMING_SNAKE_CASE       :      str							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_      )[0]
							SCREAMING_SNAKE_CASE       :      Any							=    model(UpperCAmelCase_					, attention_mask=UpperCAmelCase_					, past_key_values=UpperCAmelCase_      )[0]

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

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

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





def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     lowercase=None						,     ):

		"""simple docstring"""






		if attention_mask is None:
				SCREAMING_SNAKE_CASE       :      Optional[int]							=    tf.cast(tf.math.not_equal(lowercase						,     config.pad_token_id				)						,     tf.inta				)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE       :      int							=    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       :      str							=    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE       :      Any							=    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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
					'''simple docstring'''


					UpperCamelCase_							:		str			=	(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					UpperCamelCase_							:		Union[str, Any]			=	(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					UpperCamelCase_							:		List[Any]			=	(
					    {
					        '''conversational''': TFBlenderbotForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotModel,
					        '''summarization''': TFBlenderbotForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
					        '''translation''': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					UpperCamelCase_							:		List[Any]			=	True
					UpperCamelCase_							:		Union[str, Any]			=	False
					UpperCamelCase_							:		Dict			=	False


					def 				_A       (				self       :	List[str]      ):
							SCREAMING_SNAKE_CASE       :      int							=    TFBlenderbotModelTester(self      )
							SCREAMING_SNAKE_CASE       :      Tuple							=    ConfigTester(self					, config_class=UpperCAmelCase_      )


					def 				_A       (				self       :	str      ):
							self.config_tester.run_common_tests()






					def 				_A       (				self       :	Tuple      ):
							SCREAMING_SNAKE_CASE       :      Dict							=    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_      )





@require_tokenizers
@require_tf
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
					'''simple docstring'''


					UpperCamelCase_							:		Optional[Any]			=	['''My friends are cool but they eat too many carbs.''']
					UpperCamelCase_							:		int			=	'''facebook/blenderbot-400M-distill'''


					@cached_property
					def 				_A       (				self       :	Dict      ):
							return BlenderbotTokenizer.from_pretrained(self.model_name      )


					@cached_property
					def 				_A       (				self       :	Dict      ):
							SCREAMING_SNAKE_CASE       :      Dict							=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
							return model






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














snake_case    				=		"""Tobias Carryer"""

from time import time


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=int(time()      )      ):  # noqa: B008
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    multiplier
       SCREAMING_SNAKE_CASE       :      Tuple							=    increment
       SCREAMING_SNAKE_CASE       :      int							=    modulo
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    seed






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   snake_case    				=		LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
   while True:
      print(lcg.next_number())






