




'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   A					       ='''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





def 		snake_case_				(_a      :				List[str]       ,  _a      :				List[Any]       ,  _a      :				Optional[int]=None       ,  _a      :				Tuple=None       ,  _a      :				List[str]=None       ,  _a      :				Tuple=None       ,  _a      :				str=None       ,  _a      :				Any=None       ,  ):
    if attention_mask is None:
        UpperCAmelCase     			=						np.where(input_ids != config.pad_token_id       ,  1       ,  0	)
    if decoder_attention_mask is None:
        UpperCAmelCase     			=						np.where(decoder_input_ids != config.pad_token_id       ,  1       ,  0	)
    if head_mask is None:
        UpperCAmelCase     			=						np.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        UpperCAmelCase     			=						np.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        UpperCAmelCase     			=						np.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": attention_mask,
    }


class      _a    :
       def __init__(      self		:      Union[str, Any]  ,							lowercase		:      Optional[Any]  ,							lowercase		:      Any=13  ,							lowercase		:      Any=7  ,							lowercase		:      Any=True  ,							lowercase		:      List[str]=False  ,							lowercase		:      str=99  ,							lowercase		:      Tuple=16  ,							lowercase		:      int=2  ,							lowercase		:      Union[str, Any]=4  ,							lowercase		:      Tuple=4  ,							lowercase		:      Union[str, Any]="gelu"  ,							lowercase		:      List[Any]=0.1  ,							lowercase		:      Any=0.1  ,							lowercase		:      Optional[int]=32  ,							lowercase		:      int=2  ,							lowercase		:      List[str]=1  ,							lowercase		:      List[Any]=0  ,							lowercase		:      Any=0.02  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						seq_length
           UpperCAmelCase     			=						is_training
           UpperCAmelCase     			=						use_labels
           UpperCAmelCase     			=						vocab_size
           UpperCAmelCase     			=						hidden_size
           UpperCAmelCase     			=						num_hidden_layers
           UpperCAmelCase     			=						num_attention_heads
           UpperCAmelCase     			=						intermediate_size
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						attention_probs_dropout_prob
           UpperCAmelCase     			=						max_position_embeddings
           UpperCAmelCase     			=						eos_token_id
           UpperCAmelCase     			=						pad_token_id
           UpperCAmelCase     			=						bos_token_id
           UpperCAmelCase     			=						initializer_range
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,							self.vocab_size							)  ,							3  ,							self.vocab_size							)
           UpperCAmelCase     			=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,							dtype=np.intaa							))  ,							-1							)

           UpperCAmelCase     			=						shift_tokens_right(_SCREAMING_SNAKE_CASE  ,							1  ,							2							)

           UpperCAmelCase     			=						MarianConfig(
               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_id=self.eos_token_id  ,							bos_token_id=self.bos_token_id  ,							pad_token_id=self.pad_token_id  ,							initializer_range=self.initializer_range  ,							use_cache=_SCREAMING_SNAKE_CASE  ,							)
           UpperCAmelCase     			=						prepare_marian_inputs_dict(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
           return config, inputs_dict
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.prepare_config_and_inputs()
           return config, inputs_dict
       def        A (      self		:      int  ,							lowercase		:      Tuple  ,							lowercase		:      List[Any]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						20
           UpperCAmelCase     			=						model_class_name(_SCREAMING_SNAKE_CASE							)

           UpperCAmelCase     			=						model.encode(inputs_dict['''input_ids''']							)

           UpperCAmelCase			,	UpperCAmelCase     			=						(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           UpperCAmelCase     			=						model.init_cache(decoder_input_ids.shape[0]  ,							_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,							dtype='''i4'''							)

           UpperCAmelCase     			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]  ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,							)
           UpperCAmelCase     			=						model.decode(
               decoder_input_ids[:, :-1]  ,							_SCREAMING_SNAKE_CASE  ,							decoder_attention_mask=_SCREAMING_SNAKE_CASE  ,							past_key_values=_SCREAMING_SNAKE_CASE  ,							decoder_position_ids=_SCREAMING_SNAKE_CASE  ,							)

           UpperCAmelCase     			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,							dtype='''i4'''							)
           UpperCAmelCase     			=						model.decode(
               decoder_input_ids[:, -1:]  ,							_SCREAMING_SNAKE_CASE  ,							decoder_attention_mask=_SCREAMING_SNAKE_CASE  ,							past_key_values=outputs_cache.past_key_values  ,							decoder_position_ids=_SCREAMING_SNAKE_CASE  ,							)

           UpperCAmelCase     			=						model.decode(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)

           UpperCAmelCase     			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
           self.parent.assertTrue(diff < 1E-3  ,							msg=f"Max diff is {diff}"							)







       def        A (      self		:      Dict  ,							lowercase		:      List[str]  ,							lowercase		:      Union[str, Any]  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						20
           UpperCAmelCase     			=						model_class_name(_SCREAMING_SNAKE_CASE							)

           UpperCAmelCase     			=						model.encode(inputs_dict['''input_ids''']							)

           UpperCAmelCase			,	UpperCAmelCase     			=						(
               inputs_dict['''decoder_input_ids'''],
               inputs_dict['''decoder_attention_mask'''],
           )

           UpperCAmelCase     			=						jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
               ]  ,							axis=-1  ,							)

           UpperCAmelCase     			=						model.init_cache(decoder_input_ids.shape[0]  ,							_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]  ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,							)

           UpperCAmelCase     			=						model.decode(
               decoder_input_ids[:, :-1]  ,							_SCREAMING_SNAKE_CASE  ,							decoder_attention_mask=_SCREAMING_SNAKE_CASE  ,							past_key_values=_SCREAMING_SNAKE_CASE  ,							decoder_position_ids=_SCREAMING_SNAKE_CASE  ,							)
           UpperCAmelCase     			=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,							dtype='''i4'''							)
           UpperCAmelCase     			=						model.decode(
               decoder_input_ids[:, -1:]  ,							_SCREAMING_SNAKE_CASE  ,							past_key_values=outputs_cache.past_key_values  ,							decoder_attention_mask=_SCREAMING_SNAKE_CASE  ,							decoder_position_ids=_SCREAMING_SNAKE_CASE  ,							)

           UpperCAmelCase     			=						model.decode(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE  ,							decoder_attention_mask=_SCREAMING_SNAKE_CASE							)

           UpperCAmelCase     			=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
           self.parent.assertTrue(diff < 1E-3  ,							msg=f"Max diff is {diff}"							)






@require_flax
class      _a    (      __a				,				unittest.TestCase				,				__a					):
       __a							:	Any  							=   True
       __a							:	List[Any]  							=   (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       __a							:	List[Any]  							=   (FlaxMarianMTModel,) if is_flax_available() else ()
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						FlaxMarianModelTester(self							)
       def        A (      self		:      int							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                   UpperCAmelCase     			=						self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
                   UpperCAmelCase     			=						model_class(_SCREAMING_SNAKE_CASE							)

                   @jax.jit
                   def encode_jitted(lowercase		:      Optional[int]  ,							lowercase		:      Union[str, Any]=None  ,							**lowercase		:      Tuple							):
                       return model.encode(input_ids=_SCREAMING_SNAKE_CASE  ,							attention_mask=_SCREAMING_SNAKE_CASE							)

                   with self.subTest('''JIT Enabled'''							):
                       UpperCAmelCase     			=						encode_jitted(**_SCREAMING_SNAKE_CASE							).to_tuple()

                   with self.subTest('''JIT Disabled'''							):
                       with jax.disable_jit():
                           UpperCAmelCase     			=						encode_jitted(**_SCREAMING_SNAKE_CASE							).to_tuple()

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE							)  ,							len(_SCREAMING_SNAKE_CASE							)							)
                   for jitted_output, output in zip(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							):
                       self.assertEqual(jitted_output.shape  ,							output.shape							)
       def        A (      self		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                   UpperCAmelCase     			=						model_class(_SCREAMING_SNAKE_CASE							)
                   UpperCAmelCase     			=						model.encode(inputs_dict['''input_ids''']  ,							inputs_dict['''attention_mask''']							)

                   UpperCAmelCase     			=						{
                       '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                       '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                       '''encoder_outputs''': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(lowercase		:      List[str]  ,							lowercase		:      Dict  ,							lowercase		:      Optional[Any]							):
                       return model.decode(
                           decoder_input_ids=_SCREAMING_SNAKE_CASE  ,							decoder_attention_mask=_SCREAMING_SNAKE_CASE  ,							encoder_outputs=_SCREAMING_SNAKE_CASE  ,							)

                   with self.subTest('''JIT Enabled'''							):
                       UpperCAmelCase     			=						decode_jitted(**_SCREAMING_SNAKE_CASE							).to_tuple()

                   with self.subTest('''JIT Disabled'''							):
                       with jax.disable_jit():
                           UpperCAmelCase     			=						decode_jitted(**_SCREAMING_SNAKE_CASE							).to_tuple()

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE							)  ,							len(_SCREAMING_SNAKE_CASE							)							)
                   for jitted_output, output in zip(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							):
                       self.assertEqual(jitted_output.shape  ,							output.shape							)







       @slow
       def        A (      self		:      Any							):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               UpperCAmelCase     			=						model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''							)
               # FlaxMarianForSequenceClassification expects eos token in input_ids
               UpperCAmelCase     			=						np.ones((1, 1)							) * model.config.eos_token_id
               UpperCAmelCase     			=						model(_SCREAMING_SNAKE_CASE							)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      unittest.TestCase					):
       __a							:	Union[str, Any]  							=   None
       __a							:	Tuple  							=   None
       @classmethod
       def        A (      cls		:      List[Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
           return cls
       @cached_property
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           return MarianTokenizer.from_pretrained(self.model_name							)
       @property
       def        A (      self		:      Dict							):



           '''simple docstring'''

           return self.tokenizer.eos_token_id
       @cached_property
       def        A (      self		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						FlaxMarianMTModel.from_pretrained(self.model_name							)
           self.assertEqual(model.config.decoder_start_token_id  ,							model.config.pad_token_id							)
           return model
       def        A (      self		:      Optional[Any]  ,							**lowercase		:      int							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.translate_src_text(**_SCREAMING_SNAKE_CASE							)
           self.assertListEqual(self.expected_text  ,							_SCREAMING_SNAKE_CASE							)







       def        A (      self		:      Tuple  ,							**lowercase		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.tokenizer(self.src_text  ,							padding=_SCREAMING_SNAKE_CASE  ,							return_tensors='''np'''  ,							**_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						self.model.generate(
               model_inputs.input_ids  ,							attention_mask=model_inputs.attention_mask  ,							num_beams=2  ,							max_length=128  ,							).sequences
           UpperCAmelCase     			=						self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  ,							skip_special_tokens=_SCREAMING_SNAKE_CASE							)
           return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      __a					):
       __a							:	Union[str, Any]  							=   "en"
       __a							:	Optional[Any]  							=   "fr"
       __a							:	Dict  							=   [
           "I am a small frog.",
           "Now I can forget the 100 words of german that I know.",
       ]
       __a							:	Any  							=   [
           "Je suis une petite grenouille.",
           "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
       ]
       @slow
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      __a					):
       __a							:	Dict  							=   "fr"
       __a							:	List[str]  							=   "en"
       __a							:	str  							=   [
           "Donnez moi le micro.",
           "Tom et Mary étaient assis à une table.",  # Accents
       ]
       __a							:	Optional[int]  							=   [
           "Give me the microphone.",
           "Tom and Mary were sitting at a table.",
       ]
       @slow
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      __a					):
       __a							:	str  							=   "mt"
       __a							:	Optional[int]  							=   "en"
       __a							:	int  							=   ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
       __a							:	Union[str, Any]  							=   ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
       @slow
       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      __a					):
       __a							:	Tuple  							=   "en"
       __a							:	str  							=   "de"
       __a							:	List[str]  							=   [
           "I am a small frog.",
           "Now I can forget the 100 words of german that I know.",
           "Tom asked his teacher for advice.",
           "That's how I would do it.",
           "Tom really admired Mary's courage.",
           "Turn around and close your eyes.",
       ]
       __a							:	List[Any]  							=   [
           "Ich bin ein kleiner Frosch.",
           "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
           "Tom bat seinen Lehrer um Rat.",
           "So würde ich das machen.",
           "Tom bewunderte Marias Mut wirklich.",
           "Drehen Sie sich um und schließen Sie die Augen.",
       ]
       @slow
       def        A (      self		:      int							):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      __a					):
       __a							:	Tuple  							=   "en"
       __a							:	Tuple  							=   "zh"
       __a							:	Dict  							=   ["My name is Wolfgang and I live in Berlin"]
       __a							:	List[Any]  							=   ["我叫沃尔夫冈 我住在柏林"]
       @slow
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      _a    (      __a					):
       __a							:	List[Any]  							=   "ru"
       __a							:	Any  							=   "fr"
       __a							:	List[str]  							=   ["Он показал мне рукопись своей новой пьесы."]
       __a							:	Optional[int]  							=   ["Il m'a montré le manuscrit de sa nouvelle pièce."]
       @slow
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()






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



           '''simple docstring'''

           self._assert_generated_batch_equal_expected()





"""simple docstring"""







def       A__    (	lowercase_     :	list						)  ->							list:

   for insert_index, insert_value in enumerate(collection[1:]						):
      __lowerCamelCase    		=					insert_index
      while insert_index >= 0 and insert_value < collection[insert_index]:
         __lowerCamelCase    		=					collection[insert_index]
         insert_index -= 1
      if insert_index != temp_index:
         __lowerCamelCase    		=					insert_value
   return collection


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _SCREAMING_SNAKE_CASE	:		List[str]   =   input('''Enter numbers separated by a comma:\n''').strip()
     _SCREAMING_SNAKE_CASE	:		Optional[int]   =   [int(item) for item in user_input.split(''',''')]
     print(F"{insertion_sort(unsorted) = }")






