


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		:					Union[str, Any]          =							'''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      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     Optional[Any]    ,   snake_case						:     Union[str, Any]=None    ,   snake_case						:     Optional[int]=None    ,   snake_case						:     str=None    ,   snake_case						:     int=None    ,   snake_case						:     Optional[Any]=None    ,   snake_case						:     str=None    ,   )							->				Dict:
   if attention_mask is None:
      __lowerCamelCase:					List[str]	 =						np.where(input_ids != config.pad_token_id    ,   1    ,   0			)
   if decoder_attention_mask is None:
      __lowerCamelCase:					Optional[Any]	 =						np.where(decoder_input_ids != config.pad_token_id    ,   1    ,   0			)
   if head_mask is None:
      __lowerCamelCase:					Optional[Any]	 =						np.ones((config.encoder_layers, config.encoder_attention_heads)			)
   if decoder_head_mask is None:
      __lowerCamelCase:					Union[str, Any]	 =						np.ones((config.decoder_layers, config.decoder_attention_heads)			)
   if cross_attn_head_mask is None:
      __lowerCamelCase:					str	 =						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       :					Any     ,						SCREAMING_SNAKE_CASE_       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=13     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=7     ,						SCREAMING_SNAKE_CASE_       :					List[str]=True     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]=False     ,						SCREAMING_SNAKE_CASE_       :					str=99     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=16     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=2     ,						SCREAMING_SNAKE_CASE_       :					Any=4     ,						SCREAMING_SNAKE_CASE_       :					str=4     ,						SCREAMING_SNAKE_CASE_       :					str="gelu"     ,						SCREAMING_SNAKE_CASE_       :					Dict=0.1     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=0.1     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=32     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=2     ,						SCREAMING_SNAKE_CASE_       :					Dict=1     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=0     ,						SCREAMING_SNAKE_CASE_       :					Any=0.02     ,						):
        __lowerCamelCase:					int	 =						parent
        __lowerCamelCase:					Tuple	 =						batch_size
        __lowerCamelCase:					Optional[Any]	 =						seq_length
        __lowerCamelCase:					Optional[int]	 =						is_training
        __lowerCamelCase:					Optional[int]	 =						use_labels
        __lowerCamelCase:					int	 =						vocab_size
        __lowerCamelCase:					Any	 =						hidden_size
        __lowerCamelCase:					int	 =						num_hidden_layers
        __lowerCamelCase:					Optional[Any]	 =						num_attention_heads
        __lowerCamelCase:					List[str]	 =						intermediate_size
        __lowerCamelCase:					Any	 =						hidden_act
        __lowerCamelCase:					Union[str, Any]	 =						hidden_dropout_prob
        __lowerCamelCase:					Optional[Any]	 =						attention_probs_dropout_prob
        __lowerCamelCase:					Union[str, Any]	 =						max_position_embeddings
        __lowerCamelCase:					Optional[int]	 =						eos_token_id
        __lowerCamelCase:					Optional[Any]	 =						pad_token_id
        __lowerCamelCase:					Dict	 =						bos_token_id
        __lowerCamelCase:					str	 =						initializer_range





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        __lowerCamelCase:					Dict	 =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,						self.vocab_size				)     ,						3     ,						self.vocab_size				)
        __lowerCamelCase:					Dict	 =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,						dtype=np.intaa				))     ,						-1				)

        __lowerCamelCase:					int	 =						shift_tokens_right(SCREAMING_SNAKE_CASE_     ,						1     ,						2				)

        __lowerCamelCase:					List[str]	 =						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_     ,						)
        __lowerCamelCase:					Tuple	 =						prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        return config, inputs_dict





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase ,  __lowerCamelCase:					int	 =						self.prepare_config_and_inputs()
        return config, inputs_dict





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]     ,						SCREAMING_SNAKE_CASE_       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					Tuple				):
        __lowerCamelCase:					Union[str, Any]	 =						20
        __lowerCamelCase:					Any	 =						model_class_name(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Optional[Any]	 =						model.encode(inputs_dict["""input_ids"""]				)

        __lowerCamelCase ,  __lowerCamelCase:					int	 =						(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        __lowerCamelCase:					List[str]	 =						model.init_cache(decoder_input_ids.shape[0]     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Optional[int]	 =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,						dtype="""i4"""				)

        __lowerCamelCase:					Any	 =						jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]     ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,						)
        __lowerCamelCase:					Tuple	 =						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_     ,						)

        __lowerCamelCase:					List[str]	 =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,						dtype="""i4"""				)
        __lowerCamelCase:					int	 =						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_     ,						)

        __lowerCamelCase:					int	 =						model.decode(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Optional[Any]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					List[str]				):
        __lowerCamelCase:					List[Any]	 =						20
        __lowerCamelCase:					Tuple	 =						model_class_name(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					str	 =						model.encode(inputs_dict["""input_ids"""]				)

        __lowerCamelCase ,  __lowerCamelCase:					Tuple	 =						(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

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

        __lowerCamelCase:					int	 =						model.init_cache(decoder_input_ids.shape[0]     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					str	 =						jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]     ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,						)

        __lowerCamelCase:					List[str]	 =						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_     ,						)
        __lowerCamelCase:					Dict	 =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,						dtype="""i4"""				)
        __lowerCamelCase:					Union[str, Any]	 =						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_     ,						)

        __lowerCamelCase:					Optional[Any]	 =						model.decode(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						decoder_attention_mask=SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Any	 =						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       (      _UpperCAmelCase  ,unittest.TestCase  ,_UpperCAmelCase ):
     UpperCAmelCase__			:	List[str]       =						True
     UpperCAmelCase__			:	Any       =						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     UpperCAmelCase__			:	Union[str, Any]       =						(FlaxMarianMTModel,) if is_flax_available() else ()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					List[str]	 =						FlaxMarianModelTester(self				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        __lowerCamelCase ,  __lowerCamelCase:					Union[str, Any]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					int				):
        __lowerCamelCase ,  __lowerCamelCase:					Union[str, Any]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        __lowerCamelCase ,  __lowerCamelCase:					str	 =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__				):
              __lowerCamelCase:					int	 =						self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
              __lowerCamelCase:					Dict	 =						model_class(SCREAMING_SNAKE_CASE_				)

              @jax.jit
              def encode_jitted(SCREAMING_SNAKE_CASE_       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Tuple=None     ,						**SCREAMING_SNAKE_CASE_       :					Tuple				):
                 return model.encode(input_ids=SCREAMING_SNAKE_CASE_     ,						attention_mask=SCREAMING_SNAKE_CASE_				)

              with self.subTest("""JIT Enabled"""				):
                 __lowerCamelCase:					str	 =						encode_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

              with self.subTest("""JIT Disabled"""				):
                 with jax.disable_jit():
                    __lowerCamelCase:					Optional[Any]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]				):
        __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__				):
              __lowerCamelCase:					Optional[int]	 =						model_class(SCREAMING_SNAKE_CASE_				)
              __lowerCamelCase:					Union[str, Any]	 =						model.encode(inputs_dict["""input_ids"""]     ,						inputs_dict["""attention_mask"""]				)

              __lowerCamelCase:					str	 =						{
                  """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(SCREAMING_SNAKE_CASE_       :					Any     ,						SCREAMING_SNAKE_CASE_       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Dict				):
                 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"""				):
                 __lowerCamelCase:					Optional[Any]	 =						decode_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

              with self.subTest("""JIT Disabled"""				):
                 with jax.disable_jit():
                    __lowerCamelCase:					Union[str, Any]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]				):
        for model_class_name in self.all_model_classes:
           __lowerCamelCase:					Dict	 =						model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""				)
           # FlaxMarianForSequenceClassification expects eos token in input_ids
           __lowerCamelCase:					List[str]	 =						np.ones((1, 1)				) * model.config.eos_token_id
           __lowerCamelCase:					Tuple	 =						model(SCREAMING_SNAKE_CASE_				)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)



@require_flax
@require_sentencepiece
@require_tokenizers
class 							a       (      unittest.TestCase ):
     UpperCAmelCase__			:	List[str]       =						None
     UpperCAmelCase__			:	int       =						None





     @classmethod
     def 			SCREAMING_SNAKE_CASE__							(     cls       :					str				):
        __lowerCamelCase:					Optional[Any]	 =						F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
        return cls





     @cached_property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        return MarianTokenizer.from_pretrained(self.model_name				)





     @property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]				):
        return self.tokenizer.eos_token_id





     @cached_property
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        __lowerCamelCase:					FlaxMarianMTModel	 =						FlaxMarianMTModel.from_pretrained(self.model_name				)
        self.assertEqual(model.config.decoder_start_token_id     ,						model.config.pad_token_id				)
        return model





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]     ,						**SCREAMING_SNAKE_CASE_       :					Dict				):
        __lowerCamelCase:					Optional[int]	 =						self.translate_src_text(**SCREAMING_SNAKE_CASE_				)
        self.assertListEqual(self.expected_text     ,						SCREAMING_SNAKE_CASE_				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[Any]     ,						**SCREAMING_SNAKE_CASE_       :					int				):
        __lowerCamelCase:					List[str]	 =						self.tokenizer(self.src_text     ,						padding=SCREAMING_SNAKE_CASE_     ,						return_tensors="""np"""     ,						**SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Any	 =						self.model.generate(
            model_inputs.input_ids     ,						attention_mask=model_inputs.attention_mask     ,						num_beams=2     ,						max_length=128     ,						).sequences
        __lowerCamelCase:					List[str]	 =						self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,						skip_special_tokens=SCREAMING_SNAKE_CASE_				)
        return generated_words



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





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Optional[Any]       =						"fr"
     UpperCAmelCase__			:	Optional[Any]       =						"en"
     UpperCAmelCase__			:	Tuple       =						[
         "Donnez moi le micro.",
         "Tom et Mary étaient assis à une table.",  # Accents
     ]
     UpperCAmelCase__			:	Any       =						[
         "Give me the microphone.",
         "Tom and Mary were sitting at a table.",
     ]





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        self._assert_generated_batch_equal_expected()



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





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Optional[int]       =						"en"
     UpperCAmelCase__			:	int       =						"de"
     UpperCAmelCase__			:	Union[str, Any]       =						[
         "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.",
     ]
     UpperCAmelCase__			:	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 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	str       =						"en"
     UpperCAmelCase__			:	Optional[int]       =						"zh"
     UpperCAmelCase__			:	Dict       =						["My name is Wolfgang and I live in Berlin"]
     UpperCAmelCase__			:	Tuple       =						["我叫沃尔夫冈 我住在柏林"]





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



@require_flax
@require_sentencepiece
@require_tokenizers
class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	List[str]       =						"ru"
     UpperCAmelCase__			:	List[Any]       =						"fr"
     UpperCAmelCase__			:	List[Any]       =						["Он показал мне рукопись своей новой пьесы."]
     UpperCAmelCase__			:	Union[str, Any]       =						["Il m'a montré le manuscrit de sa nouvelle pièce."]





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        self._assert_generated_batch_equal_expected()



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





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        self._assert_generated_batch_equal_expected()






import os

import requests

_A		:					List[Any]          =							'''https://www.amdoren.com/api/currency.php'''


# Currency and their description
_A		:					int          =							'''
AED	United Arab Emirates Dirham
AFN	Afghan Afghani
ALL	Albanian Lek
AMD	Armenian Dram
ANG	Netherlands Antillean Guilder
AOA	Angolan Kwanza
ARS	Argentine Peso
AUD	Australian Dollar
AWG	Aruban Florin
AZN	Azerbaijani Manat
BAM	Bosnia & Herzegovina Convertible Mark
BBD	Barbadian Dollar
BDT	Bangladeshi Taka
BGN	Bulgarian Lev
BHD	Bahraini Dinar
BIF	Burundian Franc
BMD	Bermudian Dollar
BND	Brunei Dollar
BOB	Bolivian Boliviano
BRL	Brazilian Real
BSD	Bahamian Dollar
BTN	Bhutanese Ngultrum
BWP	Botswana Pula
BYN	Belarus Ruble
BZD	Belize Dollar
CAD	Canadian Dollar
CDF	Congolese Franc
CHF	Swiss Franc
CLP	Chilean Peso
CNY	Chinese Yuan
COP	Colombian Peso
CRC	Costa Rican Colon
CUC	Cuban Convertible Peso
CVE	Cape Verdean Escudo
CZK	Czech Republic Koruna
DJF	Djiboutian Franc
DKK	Danish Krone
DOP	Dominican Peso
DZD	Algerian Dinar
EGP	Egyptian Pound
ERN	Eritrean Nakfa
ETB	Ethiopian Birr
EUR	Euro
FJD	Fiji Dollar
GBP	British Pound Sterling
GEL	Georgian Lari
GHS	Ghanaian Cedi
GIP	Gibraltar Pound
GMD	Gambian Dalasi
GNF	Guinea Franc
GTQ	Guatemalan Quetzal
GYD	Guyanaese Dollar
HKD	Hong Kong Dollar
HNL	Honduran Lempira
HRK	Croatian Kuna
HTG	Haiti Gourde
HUF	Hungarian Forint
IDR	Indonesian Rupiah
ILS	Israeli Shekel
INR	Indian Rupee
IQD	Iraqi Dinar
IRR	Iranian Rial
ISK	Icelandic Krona
JMD	Jamaican Dollar
JOD	Jordanian Dinar
JPY	Japanese Yen
KES	Kenyan Shilling
KGS	Kyrgystani Som
KHR	Cambodian Riel
KMF	Comorian Franc
KPW	North Korean Won
KRW	South Korean Won
KWD	Kuwaiti Dinar
KYD	Cayman Islands Dollar
KZT	Kazakhstan Tenge
LAK	Laotian Kip
LBP	Lebanese Pound
LKR	Sri Lankan Rupee
LRD	Liberian Dollar
LSL	Lesotho Loti
LYD	Libyan Dinar
MAD	Moroccan Dirham
MDL	Moldovan Leu
MGA	Malagasy Ariary
MKD	Macedonian Denar
MMK	Myanma Kyat
MNT	Mongolian Tugrik
MOP	Macau Pataca
MRO	Mauritanian Ouguiya
MUR	Mauritian Rupee
MVR	Maldivian Rufiyaa
MWK	Malawi Kwacha
MXN	Mexican Peso
MYR	Malaysian Ringgit
MZN	Mozambican Metical
NAD	Namibian Dollar
NGN	Nigerian Naira
NIO	Nicaragua Cordoba
NOK	Norwegian Krone
NPR	Nepalese Rupee
NZD	New Zealand Dollar
OMR	Omani Rial
PAB	Panamanian Balboa
PEN	Peruvian Nuevo Sol
PGK	Papua New Guinean Kina
PHP	Philippine Peso
PKR	Pakistani Rupee
PLN	Polish Zloty
PYG	Paraguayan Guarani
QAR	Qatari Riyal
RON	Romanian Leu
RSD	Serbian Dinar
RUB	Russian Ruble
RWF	Rwanda Franc
SAR	Saudi Riyal
SBD	Solomon Islands Dollar
SCR	Seychellois Rupee
SDG	Sudanese Pound
SEK	Swedish Krona
SGD	Singapore Dollar
SHP	Saint Helena Pound
SLL	Sierra Leonean Leone
SOS	Somali Shilling
SRD	Surinamese Dollar
SSP	South Sudanese Pound
STD	Sao Tome and Principe Dobra
SYP	Syrian Pound
SZL	Swazi Lilangeni
THB	Thai Baht
TJS	Tajikistan Somoni
TMT	Turkmenistani Manat
TND	Tunisian Dinar
TOP	Tonga Paanga
TRY	Turkish Lira
TTD	Trinidad and Tobago Dollar
TWD	New Taiwan Dollar
TZS	Tanzanian Shilling
UAH	Ukrainian Hryvnia
UGX	Ugandan Shilling
USD	United States Dollar
UYU	Uruguayan Peso
UZS	Uzbekistan Som
VEF	Venezuelan Bolivar
VND	Vietnamese Dong
VUV	Vanuatu Vatu
WST	Samoan Tala
XAF	Central African CFA franc
XCD	East Caribbean Dollar
XOF	West African CFA franc
XPF	CFP Franc
YER	Yemeni Rial
ZAR	South African Rand
ZMW	Zambian Kwacha
'''



def      __lowerCAmelCase    (				snake_case						:     str = "USD"    ,   snake_case						:     str = "INR"    ,   snake_case						:     float = 1.0    ,   snake_case						:     str = ""			)							->				str:
   __lowerCamelCase:					str	 =						locals()
   # from is a reserved keyword
   __lowerCamelCase:					int	 =						params.pop("""from_"""			)
   __lowerCamelCase:					int	 =						requests.get(snake_case    ,   params=snake_case			).json()
   return str(res["""amount"""]			) if res["error"] == 0 else res["error_message"]


if __name__ == "__main__":
   _A		:					Any          =							os.getenv('''CI''', '''''')
   _A		:					List[str]          =							os.getenv('''AMDOREN_API_KEY''', '''''')

   if not API_KEY and not TESTING:
      raise KeyError(
          '''API key must be provided in the \'AMDOREN_API_KEY\' environment variable.'''
      )

   print(
       convert_currency(
           input('''Enter from currency: ''').strip(),
           input('''Enter to currency: ''').strip(),
           float(input('''Enter the amount: ''').strip()),
           API_KEY,
       )
   )


