




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel





@require_tf
class     lowerCamelCase__:
      UpperCAmelCase__      :	Optional[Any]    		=   MBartConfig
      UpperCAmelCase__      :	Union[str, Any]    		=   {}
      UpperCAmelCase__      :	List[str]    		=   "gelu"
      def __init__(				self: Optional[int]    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: int=13    ,       UpperCamelCase_: Optional[Any]=7    ,       UpperCamelCase_: Dict=True    ,       UpperCamelCase_: List[Any]=False    ,       UpperCamelCase_: Dict=99    ,       UpperCamelCase_: Optional[Any]=32    ,       UpperCamelCase_: Union[str, Any]=2    ,       UpperCamelCase_: int=4    ,       UpperCamelCase_: List[str]=37    ,       UpperCamelCase_: Dict=0.1    ,       UpperCamelCase_: Any=0.1    ,       UpperCamelCase_: str=20    ,       UpperCamelCase_: Tuple=2    ,       UpperCamelCase_: Tuple=1    ,       UpperCamelCase_: Optional[int]=0    ,       ):
             __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 			lowerCAmelCase__						(				self: int   ):
             __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_mbart_inputs_dict(__A    ,       __A    ,       __A   )
             return config, inputs_dict




      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Optional[Any]   ):
             __lowerCamelCase   =						TFMBartModel(config=__A   ).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(__A    ,       attention_mask=__A    ,       head_mask=__A    ,       use_cache=__A   )

             __lowerCamelCase,   __lowerCamelCase   =						outputs.to_tuple()
             __lowerCamelCase   =						past_key_values[1]




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					List[Any]  ,			A__       :					Optional[int]  ,			A__       :					Union[str, Any]=None  ,			A__       :					List[str]=None  ,			A__       :					Dict=None  ,			A__       :					Dict=None  ,			A__       :					List[Any]=None  ,			):





       '''simple docstring'''



       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     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
      UpperCAmelCase__      :	str    		=   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
      UpperCAmelCase__      :	List[Any]    		=   (
          {
              "conversational": TFMBartForConditionalGeneration,
              "feature-extraction": TFMBartModel,
              "summarization": TFMBartForConditionalGeneration,
              "text2text-generation": TFMBartForConditionalGeneration,
              "translation": TFMBartForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase__      :	Dict    		=   True
      UpperCAmelCase__      :	Union[str, Any]    		=   False
      UpperCAmelCase__      :	Optional[int]    		=   False
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: Optional[int]   ):
             if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                    # Exception encountered when calling layer '...'
                    return True

             return False
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						TFMBartModelTester(self   )
             __lowerCamelCase   =						ConfigTester(self    ,       config_class=__A   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             self.config_tester.run_common_tests()




      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*__A   )





@require_sentencepiece
@require_tokenizers
@require_tf
class     lowerCamelCase__(					unittest.TestCase):
      UpperCAmelCase__      :	Optional[int]    		=   [
          " UN Chief Says There Is No Military Solution in Syria",
      ]
      UpperCAmelCase__      :	Tuple    		=   [
          "Şeful ONU declară că nu există o soluţie militară în Siria",
      ]
      UpperCAmelCase__      :	Tuple    		=   "facebook/mbart-large-en-ro"
      @cached_property
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             return AutoTokenizer.from_pretrained(self.model_name   )
      @cached_property
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
             return model
      def 			lowerCAmelCase__						(				self: List[Any]    ,       **UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						self.translate_src_text(**__A   )
             self.assertListEqual(self.expected_text    ,       __A   )
      def 			lowerCAmelCase__						(				self: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						self.tokenizer(self.src_text    ,       **__A    ,       return_tensors="""tf"""   )
             __lowerCamelCase   =						self.model.generate(
                 model_inputs.input_ids    ,       attention_mask=model_inputs.attention_mask    ,       num_beams=2   )
             __lowerCamelCase   =						self.tokenizer.batch_decode(__A    ,       skip_special_tokens=__A   )
             return generated_words




      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             self._assert_generated_batch_equal_expected()








'''simple docstring'''
from __future__ import annotations

from typing import Any

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 pass






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Any	):
        __UpperCamelCase       =  data
        __UpperCamelCase       =  None






 def __iter__(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self
        __UpperCamelCase       =  []
        while node:
               if node in visited:
                      raise ContainsLoopError
               visited.append(__A	)
               yield node.data
               __UpperCamelCase       =  node.next_node






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        try:
               list(self	)
               return False
        except ContainsLoopError:
               return True


if __name__ == "__main__":
    a__							:							Dict =Node(1)
    a__							:							Optional[int] =Node(2)
    a__							:							List[str] =Node(3)
    a__							:							Optional[int] =Node(4)
    print(root_node.has_loop)  # False
    a__							:							str =root_node.next_node
    print(root_node.has_loop)  # True

    a__							:							Optional[int] =Node(5)
    a__							:							List[Any] =Node(6)
    a__							:							int =Node(5)
    a__							:							Tuple =Node(6)
    print(root_node.has_loop)  # False

    a__							:							str =Node(1)
    print(root_node.has_loop)  # False
