






'''simple docstring'''

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel


@require_tf
class   lowerCAmelCase     :
       lowerCAmelCase_		= MarianConfig
       lowerCAmelCase_		= {}
       lowerCAmelCase_		= "gelu"







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







            """simple docstring"""







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

            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=max_position_embeddings
            __lowercase					=eos_token_id
            __lowercase					=pad_token_id
            __lowercase					=bos_token_id







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







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

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

            __lowercase					=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						,      )
            __lowercase					=prepare_marian_inputs_dict(UpperCAmelCase_						,      UpperCAmelCase_						,      UpperCAmelCase_      )
            return config, inputs_dict







       def 			snake_case   (							self			: Optional[int]						,      __lowercase			: str						,      __lowercase			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=TFMarianModel(config=UpperCAmelCase_      ).get_decoder()
            __lowercase					=inputs_dict['input_ids']

            __lowercase					=input_ids[:1, :]
            __lowercase					=inputs_dict['attention_mask'][:1, :]
            __lowercase					=inputs_dict['head_mask']
            __lowercase					=1

            # first forward pass
            __lowercase					=model(UpperCAmelCase_						,      attention_mask=UpperCAmelCase_						,      head_mask=UpperCAmelCase_						,      use_cache=UpperCAmelCase_      )

            __lowercase					=outputs.to_tuple()

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

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

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

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






def 				__UpperCamelCase							(	lowercase__						:  Optional[int],					lowercase__						:  int,					lowercase__						:  List[Any],					lowercase__						:  Optional[int]=None,					lowercase__						:  int=None,					lowercase__						:  Optional[Any]=None,					lowercase__						:  Dict=None,					lowercase__						:  int=None,					):






     '''simple docstring'''





     if attention_mask is None:
          __lowercase					=tf.cast(tf.math.not_equal(_UpperCAmelCase,					config.pad_token_id ),					tf.inta )
     if decoder_attention_mask is None:
          __lowercase					=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:
          __lowercase					=tf.ones((config.encoder_layers, config.encoder_attention_heads) )
     if decoder_head_mask is None:
          __lowercase					=tf.ones((config.decoder_layers, config.decoder_attention_heads) )
     if cross_attn_head_mask is None:
          __lowercase					=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     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
       lowerCAmelCase_		= (TFMarianMTModel,) if is_tf_available() else ()
       lowerCAmelCase_		= (
           {
               "conversational": TFMarianMTModel,
               "feature-extraction": TFMarianModel,
               "summarization": TFMarianMTModel,
               "text2text-generation": TFMarianMTModel,
               "translation": TFMarianMTModel,
           }
           if is_tf_available()
           else {}
       )
       lowerCAmelCase_		= True
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            __lowercase					=TFMarianModelTester(self      )
            __lowercase					=ConfigTester(self						,      config_class=UpperCAmelCase_      )







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_      )






@require_tf
class   lowerCAmelCase     (      unittest.TestCase   ):
       lowerCAmelCase_		= 1_0_0_0  # show more chars for failing integration tests







       @classmethod
       def 			snake_case   (							cls			: Dict      ):







            """simple docstring"""







            __lowercase					=f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
            return cls







       @cached_property
       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            return AutoTokenizer.from_pretrained(self.model_name      )







       @property
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            return self.tokenizer.eos_token_id







       @cached_property
       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            warnings.simplefilter('error'      )
            __lowercase					=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
            assert isinstance(UpperCAmelCase_						,      UpperCAmelCase_      )
            __lowercase					=model.config
            self.assertListEqual(c.bad_words_ids						,      [[c.pad_token_id]]      )
            self.assertEqual(c.max_length						,      512      )
            self.assertEqual(c.decoder_start_token_id						,      c.pad_token_id      )
            return model







       def 			snake_case   (							self			: int						,      **__lowercase			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.translate_src_text(**UpperCAmelCase_      )
            self.assertListEqual(self.expected_text						,      UpperCAmelCase_      )







       def 			snake_case   (							self			: List[str]						,      **__lowercase			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.tokenizer(self.src_text						,      **UpperCAmelCase_						,      padding=UpperCAmelCase_						,      return_tensors='tf'      )
            __lowercase					=self.model.generate(
                model_inputs.input_ids						,      attention_mask=model_inputs.attention_mask						,      num_beams=2						,      max_length=128      )
            __lowercase					=self.tokenizer.batch_decode(generated_ids.numpy()						,      skip_special_tokens=UpperCAmelCase_      )
            return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= "mt"
       lowerCAmelCase_		= "en"
       lowerCAmelCase_		= ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
       lowerCAmelCase_		= ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]







       @unittest.skip('Skipping until #12647 is resolved.'      )
       @slow
       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= "en"
       lowerCAmelCase_		= "zh"
       lowerCAmelCase_		= ["My name is Wolfgang and I live in Berlin"]
       lowerCAmelCase_		= ["我叫沃尔夫冈 我住在柏林"]







       @unittest.skip('Skipping until #12647 is resolved.'      )
       @slow
       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= "en"
       lowerCAmelCase_		= "ROMANCE"
       lowerCAmelCase_		= [
           ">>fr<< Don't spend so much time watching TV.",
           ">>pt<< Your message has been sent.",
           ">>es<< He's two years older than me.",
       ]
       lowerCAmelCase_		= [
           "Ne passez pas autant de temps à regarder la télé.",
           "A sua mensagem foi enviada.",
           "Es dos años más viejo que yo.",
       ]







       @unittest.skip('Skipping until #12647 is resolved.'      )
       @slow
       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            self._assert_generated_batch_equal_expected()







       @unittest.skip('Skipping until #12647 is resolved.'      )
       @slow
       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=TranslationPipeline(self.model						,      self.tokenizer						,      framework='tf'      )
            __lowercase					=pipeline(self.src_text      )
            self.assertEqual(self.expected_text						,      [x['translation_text'] for x in output]      )





'''simple docstring'''

from __future__ import annotations

def 				__UpperCamelCase							(	lowercase__						:  list[int | float],					lowercase__						:  int,					lowercase__						:  int ):






     '''simple docstring'''





     if len(lowercase__ ) == 0:
          raise ValueError('find_min() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     __lowercase					=(left + right) >> 1  # the middle
     __lowercase					=find_min(lowercase__,					lowercase__,					lowercase__ )  # find min in range[left, mid]
     __lowercase					=find_min(lowercase__,					mid + 1,					lowercase__ )  # find min in range[mid + 1, right]

     return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)


