
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
     __magic_name__  :  List[str]     			=   """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__   :      Dict				,					snake_case__   :      List[Any]				,					snake_case__   :      Dict=None				,					snake_case__   :      str=None				,					snake_case__   :      Optional[int]=None				,					snake_case__   :      Optional[int]=None				,					snake_case__   :      Optional[int]=None				,					snake_case__   :      Optional[int]=None				,					)->			Optional[int]:


    if attention_mask is None:
        A_								=					np.where(input_ids != config.pad_token_id				,					1				,					0 )
    if decoder_attention_mask is None:
        A_								=					np.where(decoder_input_ids != config.pad_token_id				,					1				,					0 )
    if head_mask is None:
        A_								=					np.ones((config.encoder_layers, config.encoder_attention_heads) )
    if decoder_head_mask is None:
        A_								=					np.ones((config.decoder_layers, config.decoder_attention_heads) )
    if cross_attn_head_mask is None:
        A_								=					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 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase=13						,    __UpperCamelCase=7						,    __UpperCamelCase=True						,    __UpperCamelCase=False						,    __UpperCamelCase=99						,    __UpperCamelCase=16						,    __UpperCamelCase=2						,    __UpperCamelCase=4						,    __UpperCamelCase=4						,    __UpperCamelCase="gelu"						,    __UpperCamelCase=0.1						,    __UpperCamelCase=0.1						,    __UpperCamelCase=32						,    __UpperCamelCase=2						,    __UpperCamelCase=1						,    __UpperCamelCase=0						,    __UpperCamelCase=0.02						,    ):
      A_								=					parent
      A_								=					batch_size
      A_								=					seq_length
      A_								=					is_training
      A_								=					use_labels
      A_								=					vocab_size
      A_								=					hidden_size
      A_								=					num_hidden_layers
      A_								=					num_attention_heads
      A_								=					intermediate_size
      A_								=					hidden_act
      A_								=					hidden_dropout_prob
      A_								=					attention_probs_dropout_prob
      A_								=					max_position_embeddings
      A_								=					eos_token_id
      A_								=					pad_token_id
      A_								=					bos_token_id
      A_								=					initializer_range

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

      A_								=					shift_tokens_right(__UpperCAmelCase						,    1						,    2					)

      A_								=					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=__UpperCAmelCase						,    )
      A_								=					prepare_marian_inputs_dict(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)
      return config, inputs_dict

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.prepare_config_and_inputs()
      return config, inputs_dict

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					20
      A_								=					model_class_name(__UpperCAmelCase					)

      A_								=					model.encode(inputs_dict["input_ids"]					)

      A_	,	A_								=					(
          inputs_dict["decoder_input_ids"],
          inputs_dict["decoder_attention_mask"],
      )

      A_								=					model.init_cache(decoder_input_ids.shape[0]						,    __UpperCAmelCase						,    __UpperCAmelCase					)
      A_								=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,    dtype="i4"					)

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

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

      A_								=					model.decode(__UpperCAmelCase						,    __UpperCAmelCase					)

      A_								=					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 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					20
      A_								=					model_class_name(__UpperCAmelCase					)

      A_								=					model.encode(inputs_dict["input_ids"]					)

      A_	,	A_								=					(
          inputs_dict["decoder_input_ids"],
          inputs_dict["decoder_attention_mask"],
      )

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

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

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

      A_								=					model.decode(__UpperCAmelCase						,    __UpperCAmelCase						,    decoder_attention_mask=__UpperCAmelCase					)

      A_								=					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 							lowerCamelCase       (		lowerCamelCase_			,  unittest.TestCase			,  lowerCamelCase_					):



  """simple docstring"""





  lowerCAmelCase_								=		True
  lowerCAmelCase_								=		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  lowerCAmelCase_								=		(FlaxMarianMTModel,) if is_flax_available() else ()

  def 					lowercase_						(					self					):
      A_								=					FlaxMarianModelTester(self					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					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(__UpperCAmelCase						,    __UpperCAmelCase						,    __UpperCAmelCase					)

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__					):
              A_								=					self._prepare_for_class(__UpperCAmelCase						,    __UpperCAmelCase					)
              A_								=					model_class(__UpperCAmelCase					)

              @jax.jit
              def encode_jitted(__UpperCamelCase						,    __UpperCamelCase=None						,    **__UpperCamelCase					):
                  return model.encode(input_ids=__UpperCAmelCase						,    attention_mask=__UpperCAmelCase					)

              with self.subTest("JIT Enabled"					):
                  A_								=					encode_jitted(**__UpperCAmelCase					).to_tuple()

              with self.subTest("JIT Disabled"					):
                  with jax.disable_jit():
                      A_								=					encode_jitted(**__UpperCAmelCase					).to_tuple()

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

  def 					lowercase_						(					self					):
      A_	,	A_								=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__					):
              A_								=					model_class(__UpperCAmelCase					)
              A_								=					model.encode(inputs_dict["input_ids"]						,    inputs_dict["attention_mask"]					)

              A_								=					{
                  "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(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
                  return model.decode(
                      decoder_input_ids=__UpperCAmelCase						,    decoder_attention_mask=__UpperCAmelCase						,    encoder_outputs=__UpperCAmelCase						,    )

              with self.subTest("JIT Enabled"					):
                  A_								=					decode_jitted(**__UpperCAmelCase					).to_tuple()

              with self.subTest("JIT Disabled"					):
                  with jax.disable_jit():
                      A_								=					decode_jitted(**__UpperCAmelCase					).to_tuple()

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

  @slow
  def 					lowercase_						(					self					):



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



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  lowerCAmelCase_								=		None
  lowerCAmelCase_								=		None

  @classmethod
  def 					lowercase_						(					cls					):
      A_								=					f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
      return cls

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

  @property
  def 					lowercase_						(					self					):
      return self.tokenizer.eos_token_id

  @cached_property
  def 					lowercase_						(					self					):
      A_								=					FlaxMarianMTModel.from_pretrained(self.model_name					)
      self.assertEqual(model.config.decoder_start_token_id						,    model.config.pad_token_id					)
      return model

  def 					lowercase_						(					self						,    **__UpperCamelCase					):
      A_								=					self.translate_src_text(**__UpperCAmelCase					)
      self.assertListEqual(self.expected_text						,    __UpperCAmelCase					)

  def 					lowercase_						(					self						,    **__UpperCamelCase					):
      A_								=					self.tokenizer(self.src_text						,    padding=__UpperCAmelCase						,    return_tensors="np"						,    **__UpperCAmelCase					)
      A_								=					self.model.generate(
          model_inputs.input_ids						,    attention_mask=model_inputs.attention_mask						,    num_beams=2						,    max_length=128						,    ).sequences
      A_								=					self.tokenizer.batch_decode(__UpperCAmelCase						,    skip_special_tokens=__UpperCAmelCase					)
      return generated_words



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  lowerCAmelCase_								=		'''en'''
  lowerCAmelCase_								=		'''fr'''
  lowerCAmelCase_								=		[
      '''I am a small frog.''',
      '''Now I can forget the 100 words of german that I know.''',
  ]
  lowerCAmelCase_								=		[
      '''Je suis une petite grenouille.''',
      '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
  ]

  @slow
  def 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  lowerCAmelCase_								=		'''fr'''
  lowerCAmelCase_								=		'''en'''
  lowerCAmelCase_								=		[
      '''Donnez moi le micro.''',
      '''Tom et Mary étaient assis à une table.''',  # Accents
  ]
  lowerCAmelCase_								=		[
      '''Give me the microphone.''',
      '''Tom and Mary were sitting at a table.''',
  ]

  @slow
  def 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  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.''']

  @slow
  def 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  lowerCAmelCase_								=		'''en'''
  lowerCAmelCase_								=		'''de'''
  lowerCAmelCase_								=		[
      '''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.''',
  ]
  lowerCAmelCase_								=		[
      '''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 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  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''']

  @slow
  def 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  lowerCAmelCase_								=		'''ru'''
  lowerCAmelCase_								=		'''fr'''
  lowerCAmelCase_								=		['''Он показал мне рукопись своей новой пьесы.''']
  lowerCAmelCase_								=		['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']

  @slow
  def 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()



@require_flax
@require_sentencepiece
@require_tokenizers
class 							lowerCamelCase       (		lowerCamelCase_					):



  """simple docstring"""





  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.''',
  ]

  @slow
  def 					lowercase_						(					self					):
      self._assert_generated_batch_equal_expected()












from collections import Counter




def  a_		(							__snake_case					)	->			int:

			'''simple docstring'''


			return sum(socks_by_color // 2 for socks_by_color in Counter(__snake_case					).values()					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__a			:	List[Any]							     =		[int(x) for x in input("""Enter socks by color :""").rstrip().split()]
				print(f"""sock_merchant({colors}) = {sock_merchant(colors)}""")
