





"""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
   lowerCamelCase_					:						List[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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, _UpperCAmelCase=None			, ):






  """simple docstring"""





  if attention_mask is None:
    A_  :	Any    						=  np.where(input_ids != config.pad_token_id			, 1			, 0 )
  if decoder_attention_mask is None:
    A_  :	Optional[int]    						=  np.where(decoder_input_ids != config.pad_token_id			, 1			, 0 )
  if head_mask is None:
    A_  :	Any    						=  np.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    A_  :	Dict    						=  np.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    A_  :	int    						=  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 			_UpperCAmelCase  :



      '''simple docstring'''

      def __init__(					self ,				snake_case_ ,				snake_case_=1_3 ,				snake_case_=7 ,				snake_case_=True ,				snake_case_=False ,				snake_case_=9_9 ,				snake_case_=1_6 ,				snake_case_=2 ,				snake_case_=4 ,				snake_case_=4 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=3_2 ,				snake_case_=2 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=0.02 ,				):

        """simple docstring"""

        A_  :	Tuple    						=  parent
        A_  :	Union[str, Any]    						=  batch_size
        A_  :	int    						=  seq_length
        A_  :	int    						=  is_training
        A_  :	str    						=  use_labels
        A_  :	Tuple    						=  vocab_size
        A_  :	Optional[int]    						=  hidden_size
        A_  :	Optional[int]    						=  num_hidden_layers
        A_  :	Optional[int]    						=  num_attention_heads
        A_  :	Optional[Any]    						=  intermediate_size
        A_  :	Tuple    						=  hidden_act
        A_  :	str    						=  hidden_dropout_prob
        A_  :	Tuple    						=  attention_probs_dropout_prob
        A_  :	Dict    						=  max_position_embeddings
        A_  :	Optional[Any]    						=  eos_token_id
        A_  :	List[Any]    						=  pad_token_id
        A_  :	Dict    						=  bos_token_id
        A_  :	Dict    						=  initializer_range

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

        A_  :	str    						=  shift_tokens_right(snake_case_ ,				1 ,				2  )

        A_  :	Optional[Any]    						=  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=snake_case_ ,				)
        A_  :	int    						=  prepare_marian_inputs_dict(snake_case_ ,				snake_case_ ,				snake_case_  )
        return config, inputs_dict

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[Any]    						=  2_0
        A_  :	Any    						=  model_class_name(snake_case_  )

        A_  :	str    						=  model.encode(inputs_dict['input_ids']  )

        A_						,	A_  :	Any    						=  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

        A_  :	Union[str, Any]    						=  model.init_cache(decoder_input_ids.shape[0] ,				snake_case_ ,				snake_case_  )
        A_  :	List[str]    						=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,				dtype='i4'  )

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

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

        A_  :	int    						=  model.decode(snake_case_ ,				snake_case_  )

        A_  :	Dict    						=  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  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	Optional[Any]    						=  2_0
        A_  :	Tuple    						=  model_class_name(snake_case_  )

        A_  :	Optional[Any]    						=  model.encode(inputs_dict['input_ids']  )

        A_						,	A_  :	Any    						=  (
            inputs_dict['decoder_input_ids'],
            inputs_dict['decoder_attention_mask'],
        )

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

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

        A_  :	List[Any]    						=  model.decode(
            decoder_input_ids[:, :-1] ,				snake_case_ ,				decoder_attention_mask=snake_case_ ,				past_key_values=snake_case_ ,				decoder_position_ids=snake_case_ ,				)
        A_  :	Dict    						=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='i4'  )
        A_  :	Dict    						=  model.decode(
            decoder_input_ids[:, -1:] ,				snake_case_ ,				past_key_values=outputs_cache.past_key_values ,				decoder_attention_mask=snake_case_ ,				decoder_position_ids=snake_case_ ,				)

        A_  :	str    						=  model.decode(snake_case_ ,				snake_case_ ,				decoder_attention_mask=snake_case_  )

        A_  :	int    						=  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 			_UpperCAmelCase  (       UpperCAmelCase__      ,       unittest.TestCase      ,       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Optional[int]        =       True
      lowercase_				:			int        =       (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      lowercase_				:			str        =       (FlaxMarianMTModel,) if is_flax_available() else ()

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  FlaxMarianModelTester(self  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_						,	A_  :	Any    						=  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(snake_case_ ,				snake_case_ ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_						,	A_  :	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(snake_case_ ,				snake_case_ ,				snake_case_  )

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_						,	A_  :	int    						=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__  ):
            A_  :	int    						=  self._prepare_for_class(snake_case_ ,				snake_case_  )
            A_  :	Optional[Any]    						=  model_class(snake_case_  )

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

            with self.subTest('JIT Enabled'  ):
              A_  :	List[str]    						=  encode_jitted(**snake_case_  ).to_tuple()

            with self.subTest('JIT Disabled'  ):
              with jax.disable_jit():
                A_  :	int    						=  encode_jitted(**snake_case_  ).to_tuple()

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

      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_						,	A_  :	Any    						=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__  ):
            A_  :	List[Any]    						=  model_class(snake_case_  )
            A_  :	Optional[int]    						=  model.encode(inputs_dict['input_ids'] ,				inputs_dict['attention_mask']  )

            A_  :	Optional[int]    						=  {
                '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(snake_case_ ,				snake_case_ ,				snake_case_  ):
              return model.decode(
                  decoder_input_ids=snake_case_ ,				decoder_attention_mask=snake_case_ ,				encoder_outputs=snake_case_ ,				)

            with self.subTest('JIT Enabled'  ):
              A_  :	str    						=  decode_jitted(**snake_case_  ).to_tuple()

            with self.subTest('JIT Disabled'  ):
              with jax.disable_jit():
                A_  :	List[str]    						=  decode_jitted(**snake_case_  ).to_tuple()

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

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        for model_class_name in self.all_model_classes:
          A_  :	Tuple    						=  model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'  )
          # FlaxMarianForSequenceClassification expects eos token in input_ids
          A_  :	List[str]    						=  np.ones((1, 1)  ) * model.config.eos_token_id
          A_  :	str    						=  model(snake_case_  )
          self.assertIsNotNone(snake_case_  )


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



      '''simple docstring'''
      lowercase_				:			Any        =       None
      lowercase_				:			Union[str, Any]        =       None

      @classmethod
      def  lowerCamelCase_      (					cls  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
        return cls

      @cached_property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return MarianTokenizer.from_pretrained(self.model_name  )

      @property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        return self.tokenizer.eos_token_id

      @cached_property
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

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

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

        A_  :	Dict    						=  self.translate_src_text(**snake_case_  )
        self.assertListEqual(self.expected_text ,				snake_case_  )

      def  lowerCamelCase_      (					self ,				**snake_case_  ):

        """simple docstring"""

        A_  :	Dict    						=  self.tokenizer(self.src_text ,				padding=snake_case_ ,				return_tensors='np' ,				**snake_case_  )
        A_  :	int    						=  self.model.generate(
            model_inputs.input_ids ,				attention_mask=model_inputs.attention_mask ,				num_beams=2 ,				max_length=1_2_8 ,				).sequences
        A_  :	List[str]    						=  self.tokenizer.batch_decode(snake_case_ ,				skip_special_tokens=snake_case_  )
        return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Optional[int]        =       """en"""
      lowercase_				:			Any        =       """fr"""
      lowercase_				:			Dict        =       [
          """I am a small frog.""",
          """Now I can forget the 100 words of german that I know.""",
      ]
      lowercase_				:			str        =       [
          """Je suis une petite grenouille.""",
          """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
      ]

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Union[str, Any]        =       """fr"""
      lowercase_				:			Optional[Any]        =       """en"""
      lowercase_				:			int        =       [
          """Donnez moi le micro.""",
          """Tom et Mary étaient assis à une table.""",  # Accents
      ]
      lowercase_				:			Any        =       [
          """Give me the microphone.""",
          """Tom and Mary were sitting at a table.""",
      ]

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			int        =       """mt"""
      lowercase_				:			Any        =       """en"""
      lowercase_				:			List[Any]        =       ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
      lowercase_				:			Tuple        =       ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			Union[str, Any]        =       """en"""
      lowercase_				:			Tuple        =       """de"""
      lowercase_				:			Optional[int]        =       [
          """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.""",
      ]
      lowercase_				:			Tuple        =       [
          """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  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			int        =       """en"""
      lowercase_				:			Union[str, Any]        =       """zh"""
      lowercase_				:			Any        =       ["""My name is Wolfgang and I live in Berlin"""]
      lowercase_				:			List[Any]        =       ["""我叫沃尔夫冈 我住在柏林"""]

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_				:			List[Any]        =       """ru"""
      lowercase_				:			List[str]        =       """fr"""
      lowercase_				:			str        =       ["""Он показал мне рукопись своей новой пьесы."""]
      lowercase_				:			str        =       ["""Il m'a montré le manuscrit de sa nouvelle pièce."""]

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase  (       UpperCAmelCase__		):



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

      @slow
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        self._assert_generated_batch_equal_expected()






"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_					:						Any      = {
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						Union[str, Any]      = [
       'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TvltModel',
       'TvltForPreTraining',
       'TvltForAudioVisualClassification',
       'TvltPreTrainedModel',
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_					:						List[Any]      = ['TvltImageProcessor']


if TYPE_CHECKING:
   from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
   from .processing_tvlt import TvltProcessor
   from .feature_extraction_tvlt import TvltFeatureExtractor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tvlt import (
          TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TvltForAudioVisualClassification,
          TvltForPreTraining,
          TvltModel,
          TvltPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_tvlt import TvltImageProcessor


else:
   import sys

   lowerCamelCase_					:						Optional[int]      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)