




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase    : Optional[Any]		= logging.get_logger(__name__)

_lowerCAmelCase    : str		= {}
class       _UpperCamelCase   (					lowerCAmelCase		):
      UpperCAmelCase_          =		"""fsmt_decoder"""
      def __init__(      self :int			,   lowerCamelCase :Optional[int]=0			,   lowerCamelCase :List[str]=0   )  ->      Any:
       super().__init__()
       UpperCAmelCase__							=    vocab_size
       UpperCAmelCase__							=    bos_token_id



class       _UpperCamelCase   (					lowerCAmelCase		):
      UpperCAmelCase_          =		"""fsmt"""
      UpperCAmelCase_          =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
      def __init__(      self :Dict			,   lowerCamelCase :List[Any]=["en", "de"]			,   lowerCamelCase :Union[str, Any]=4_2024			,   lowerCamelCase :Dict=4_2024			,   lowerCamelCase :Optional[int]="relu"			,   lowerCamelCase :Tuple=1024			,   lowerCamelCase :Tuple=200			,   lowerCamelCase :Optional[int]=1024			,   lowerCamelCase :Tuple=4096			,   lowerCamelCase :List[str]=12			,   lowerCamelCase :Dict=16			,   lowerCamelCase :Tuple=0.0			,   lowerCamelCase :List[Any]=4096			,   lowerCamelCase :int=12			,   lowerCamelCase :List[str]=16			,   lowerCamelCase :Union[str, Any]=0.0			,   lowerCamelCase :Union[str, Any]=0.0			,   lowerCamelCase :List[Any]=0.1			,   lowerCamelCase :Union[str, Any]=0.0			,   lowerCamelCase :Any=0.02			,   lowerCamelCase :List[str]=2			,   lowerCamelCase :List[Any]=True			,   lowerCamelCase :List[str]=True			,   lowerCamelCase :int=False			,   lowerCamelCase :Union[str, Any]=5			,   lowerCamelCase :Any=1.0			,   lowerCamelCase :int=False			,   lowerCamelCase :Tuple=True			,   lowerCamelCase :List[str]=1			,   lowerCamelCase :Dict=0			,   lowerCamelCase :Optional[Any]=2			,   lowerCamelCase :Any=2			,   **lowerCamelCase :Union[str, Any]			,   )  ->      Dict:
       UpperCAmelCase__							=    langs
       UpperCAmelCase__							=    src_vocab_size
       UpperCAmelCase__							=    tgt_vocab_size
       UpperCAmelCase__							=    d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCAmelCase__							=    encoder_ffn_dim
       UpperCAmelCase__							=    UpperCAmelCase__							=    encoder_layers
       UpperCAmelCase__							=    encoder_attention_heads
       UpperCAmelCase__							=    encoder_layerdrop
       UpperCAmelCase__							=    decoder_layerdrop
       UpperCAmelCase__							=    decoder_ffn_dim
       UpperCAmelCase__							=    decoder_layers
       UpperCAmelCase__							=    decoder_attention_heads
       UpperCAmelCase__							=    max_position_embeddings
       UpperCAmelCase__							=    init_std  # Normal(0, this parameter)
       UpperCAmelCase__							=    activation_function

       UpperCAmelCase__							=    DecoderConfig(vocab_size=lowerCamelCase			,   bos_token_id=lowerCamelCase   )
       if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

       UpperCAmelCase__							=    scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCAmelCase__							=    attention_dropout
       UpperCAmelCase__							=    activation_dropout
       UpperCAmelCase__							=    dropout

       UpperCAmelCase__							=    use_cache
       super().__init__(
           pad_token_id=lowerCamelCase			,   bos_token_id=lowerCamelCase			,   eos_token_id=lowerCamelCase			,   decoder_start_token_id=lowerCamelCase			,   is_encoder_decoder=lowerCamelCase			,   tie_word_embeddings=lowerCamelCase			,   forced_eos_token_id=lowerCamelCase			,   max_length=lowerCamelCase			,   num_beams=lowerCamelCase			,   length_penalty=lowerCamelCase			,   early_stopping=lowerCamelCase			,   **lowerCamelCase			,   )







      def 						UpperCAmelCase_    (      self :Tuple   )  ->      Optional[Any]:
       UpperCAmelCase__							=    copy.deepcopy(self.__dict__   )
       UpperCAmelCase__							=    self.decoder.to_dict()
       UpperCAmelCase__							=    self.__class__.model_type
       return output











from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
  from transformers.models.albert.modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertModel,
  )
class       _UpperCamelCase   :
      def __init__(      self :Optional[int]			,   lowerCamelCase :Tuple			,   lowerCamelCase :Union[str, Any]=13			,   lowerCamelCase :List[str]=7			,   lowerCamelCase :List[Any]=True			,   lowerCamelCase :Any=True			,   lowerCamelCase :Any=True			,   lowerCamelCase :Optional[Any]=True			,   lowerCamelCase :Optional[int]=99			,   lowerCamelCase :Tuple=16			,   lowerCamelCase :Optional[Any]=32			,   lowerCamelCase :Optional[Any]=2			,   lowerCamelCase :List[Any]=4			,   lowerCamelCase :Dict=37			,   lowerCamelCase :Optional[Any]="gelu"			,   lowerCamelCase :Dict=0.1			,   lowerCamelCase :Optional[Any]=0.1			,   lowerCamelCase :Tuple=512			,   lowerCamelCase :Optional[int]=16			,   lowerCamelCase :Any=2			,   lowerCamelCase :Tuple=0.02			,   lowerCamelCase :Union[str, Any]=3			,   lowerCamelCase :str=4			,   lowerCamelCase :Dict=None			,   )  ->      List[Any]:
       UpperCAmelCase__							=    parent
       UpperCAmelCase__							=    13
       UpperCAmelCase__							=    7
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    True
       UpperCAmelCase__							=    99
       UpperCAmelCase__							=    16
       UpperCAmelCase__							=    32
       UpperCAmelCase__							=    2
       UpperCAmelCase__							=    4
       UpperCAmelCase__							=    37
       UpperCAmelCase__							=    "gelu"
       UpperCAmelCase__							=    0.1
       UpperCAmelCase__							=    0.1
       UpperCAmelCase__							=    512
       UpperCAmelCase__							=    16
       UpperCAmelCase__							=    2
       UpperCAmelCase__							=    0.02
       UpperCAmelCase__							=    3
       UpperCAmelCase__							=    4
       UpperCAmelCase__							=    None
      def 						UpperCAmelCase_    (      self :Optional[int]   )  ->      Dict:
       UpperCAmelCase__							=    ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size   )

       UpperCAmelCase__							=    None
       if self.use_input_mask:
        UpperCAmelCase__							=    random_attention_mask([self.batch_size, self.seq_length]   )

       UpperCAmelCase__							=    None
       if self.use_token_type_ids:
        UpperCAmelCase__							=    ids_tensor([self.batch_size, self.seq_length]			,   self.type_vocab_size   )

       UpperCAmelCase__							=    None
       UpperCAmelCase__							=    None
       UpperCAmelCase__							=    None
       if self.use_labels:
        UpperCAmelCase__							=    ids_tensor([self.batch_size]			,   self.type_sequence_label_size   )
        UpperCAmelCase__							=    ids_tensor([self.batch_size, self.seq_length]			,   self.num_labels   )
        UpperCAmelCase__							=    ids_tensor([self.batch_size]			,   self.num_choices   )

       UpperCAmelCase__							=    AlbertConfig(
           vocab_size=self.vocab_size			,   hidden_size=self.hidden_size			,   embedding_size=self.embedding_size			,   num_hidden_layers=self.num_hidden_layers			,   num_attention_heads=self.num_attention_heads			,   intermediate_size=self.intermediate_size			,   hidden_act=self.hidden_act			,   hidden_dropout_prob=self.hidden_dropout_prob			,   attention_probs_dropout_prob=self.attention_probs_dropout_prob			,   max_position_embeddings=self.max_position_embeddings			,   type_vocab_size=self.type_vocab_size			,   initializer_range=self.initializer_range			,   )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :str			,   lowerCamelCase :Tuple			,   lowerCamelCase :Tuple			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :int			,   lowerCamelCase :str   )  ->      Optional[Any]:
       UpperCAmelCase__							=    TFAlbertModel(config=lowerCamelCase   )
       # inputs = {'input_ids': input_ids,
       #           'attention_mask': input_mask,
       #           'token_type_ids': token_type_ids}
       # sequence_output, pooled_output = model(**inputs)
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )

       UpperCAmelCase__							=    [input_ids, input_mask]
       UpperCAmelCase__							=    model(lowerCamelCase   )

       UpperCAmelCase__							=    model(lowerCamelCase   )

       self.parent.assertEqual(result.last_hidden_state.shape			,   (self.batch_size, self.seq_length, self.hidden_size)   )
       self.parent.assertEqual(result.pooler_output.shape			,   (self.batch_size, self.hidden_size)   )
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Any			,   lowerCamelCase :List[str]			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Tuple   )  ->      Optional[int]:
       UpperCAmelCase__							=    self.num_labels
       UpperCAmelCase__							=    TFAlbertForPreTraining(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.prediction_logits.shape			,   (self.batch_size, self.seq_length, self.vocab_size)   )
       self.parent.assertEqual(result.sop_logits.shape			,   (self.batch_size, self.num_labels)   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Dict			,   lowerCamelCase :List[Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :Tuple			,   lowerCamelCase :Any   )  ->      Optional[Any]:
       UpperCAmelCase__							=    TFAlbertForMaskedLM(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.seq_length, self.vocab_size)   )
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :str			,   lowerCamelCase :Dict			,   lowerCamelCase :Dict			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Optional[Any]   )  ->      str:
       UpperCAmelCase__							=    self.num_labels
       UpperCAmelCase__							=    TFAlbertForSequenceClassification(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels)   )
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :str			,   lowerCamelCase :Dict			,   lowerCamelCase :List[Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :int			,   lowerCamelCase :Optional[Any]   )  ->      Dict:
       UpperCAmelCase__							=    TFAlbertForQuestionAnswering(config=lowerCamelCase   )
       UpperCAmelCase__							=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertEqual(result.start_logits.shape			,   (self.batch_size, self.seq_length)   )
       self.parent.assertEqual(result.end_logits.shape			,   (self.batch_size, self.seq_length)   )
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Dict			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Tuple			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Union[str, Any]   )  ->      int:
       UpperCAmelCase__							=    self.num_choices
       UpperCAmelCase__							=    TFAlbertForMultipleChoice(config=lowerCamelCase   )
       UpperCAmelCase__							=    tf.tile(tf.expand_dims(lowerCamelCase			,   1   )			,   (1, self.num_choices, 1)   )
       UpperCAmelCase__							=    tf.tile(tf.expand_dims(lowerCamelCase			,   1   )			,   (1, self.num_choices, 1)   )
       UpperCAmelCase__							=    tf.tile(tf.expand_dims(lowerCamelCase			,   1   )			,   (1, self.num_choices, 1)   )
       UpperCAmelCase__							=    {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertListEqual(list(result["logits"].shape   )			,   [self.batch_size, self.num_choices]   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :int			,   lowerCamelCase :int			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Dict			,   lowerCamelCase :Dict   )  ->      List[str]:
       UpperCAmelCase__							=    self.num_labels
       UpperCAmelCase__							=    TFAlbertForTokenClassification(config=lowerCamelCase   )
       UpperCAmelCase__							=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       UpperCAmelCase__							=    model(lowerCamelCase   )
       self.parent.assertListEqual(list(result["logits"].shape   )			,   [self.batch_size, self.seq_length, self.num_labels]   )







      def 						UpperCAmelCase_    (      self :str   )  ->      Any:
       UpperCAmelCase__							=    self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			(
           UpperCAmelCase__
       )			,			
       )							=    config_and_inputs
       UpperCAmelCase__							=    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_tf
class       _UpperCamelCase   (					lowerCAmelCase						,							lowerCAmelCase						,							unittest.TestCase		):
      UpperCAmelCase_          =		(
          (
              TFAlbertModel,
              TFAlbertForPreTraining,
              TFAlbertForMaskedLM,
              TFAlbertForSequenceClassification,
              TFAlbertForQuestionAnswering,
              TFAlbertForTokenClassification,
              TFAlbertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCAmelCase_          =		(
          {
              """feature-extraction""": TFAlbertModel,
              """fill-mask""": TFAlbertForMaskedLM,
              """question-answering""": TFAlbertForQuestionAnswering,
              """text-classification""": TFAlbertForSequenceClassification,
              """token-classification""": TFAlbertForTokenClassification,
              """zero-shot""": TFAlbertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCAmelCase_          =		False
      UpperCAmelCase_          =		False
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Any			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :Optional[int]=False   )  ->      str:
       UpperCAmelCase__							=    super()._prepare_for_class(lowerCamelCase			,   lowerCamelCase			,   return_labels=lowerCamelCase   )

       if return_labels:
        if model_class in get_values(lowerCamelCase   ):
         UpperCAmelCase__							=    tf.zeros(self.model_tester.batch_size			,   dtype=tf.intaa   )

       return inputs_dict
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    TFAlbertModelTester(self   )
       UpperCAmelCase__							=    ConfigTester(self			,   config_class=lowerCamelCase			,   hidden_size=37   )
      def 						UpperCAmelCase_    (      self :Dict   )  ->      Union[str, Any]:
       self.config_tester.run_common_tests()
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      Tuple:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_model(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Dict:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_pretraining(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      str:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_masked_lm(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Dict   )  ->      int:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_multiple_choice(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Dict:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_sequence_classification(*lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str   )  ->      int:
       UpperCAmelCase__							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_albert_for_question_answering(*lowerCamelCase   )







      @slow
      def 						UpperCAmelCase_    (      self :int   )  ->      str:
       for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							=    TFAlbertModel.from_pretrained(lowerCamelCase   )
        self.assertIsNotNone(lowerCamelCase   )



@require_tf
class       _UpperCamelCase   (					unittest.TestCase		):
      @slow
      def 						UpperCAmelCase_    (      self :List[str]   )  ->      List[str]:
       UpperCAmelCase__							=    TFAlbertForPreTraining.from_pretrained("albert-base-v2"   )
       UpperCAmelCase__							=    tf.constant([[0, 1, 2, 3, 4, 5]]   )
       UpperCAmelCase__							=    model(lowerCamelCase   )[0]

       UpperCAmelCase__							=    [1, 6, 3_0000]
       self.assertEqual(output.shape			,   lowerCamelCase   )

       UpperCAmelCase__							=    tf.constant(
           [
               [
                   [4.59_56_68, 0.74_46_27_54, -1.81_81_47],
                   [4.5_95_43_47, 0.7_45_41_84, -1.8_18_82_58],
                   [4.5_95_49_05, 0.7_44_82_35, -1.8_18_23_16],
               ]
           ]   )
       tf.debugging.assert_near(output[:, :3, :3]			,   lowerCamelCase			,   atol=1e-4   )





