
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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					   ="""platform"""

      import jax
      import jax.numpy as jnp

      from transformers import BlenderbotTokenizer
      from transformers.models.blenderbot.modeling_flax_blenderbot import (
          FlaxBlenderbotForConditionalGeneration,
          FlaxBlenderbotModel,
          shift_tokens_right,
      )





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	lowerCamelCase=None,	):
 if attention_mask is None:
  lowerCamelCase		:	Union[str, Any]							=      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		:	str							=      np.ones((config.encoder_layers, config.encoder_attention_heads)							)
 if decoder_head_mask is None:
  lowerCamelCase		:	Optional[Any]							=      np.ones((config.decoder_layers, config.decoder_attention_heads)							)
 if cross_attn_head_mask is None:
  lowerCamelCase		:	Tuple							=      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    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=9_9    ,  __magic_name__=1_6    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__=4    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=3_2    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=0.02    ,  ):
       lowerCamelCase		:	Optional[Any]							=      parent
       lowerCamelCase		:	Tuple							=      batch_size
       lowerCamelCase		:	Dict							=      seq_length
       lowerCamelCase		:	List[Any]							=      is_training
       lowerCamelCase		:	Any							=      use_labels
       lowerCamelCase		:	Optional[int]							=      vocab_size
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	Optional[int]							=      num_hidden_layers
       lowerCamelCase		:	List[Any]							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      intermediate_size
       lowerCamelCase		:	Optional[Any]							=      hidden_act
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	int							=      attention_probs_dropout_prob
       lowerCamelCase		:	List[Any]							=      max_position_embeddings
       lowerCamelCase		:	Any							=      eos_token_id
       lowerCamelCase		:	Any							=      pad_token_id
       lowerCamelCase		:	Tuple							=      bos_token_id
       lowerCamelCase		:	List[str]							=      initializer_range
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size      )    ,  3    ,  self.vocab_size      )
       lowerCamelCase		:	List[Any]							=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,  dtype=np.intaa      ))    ,  -1      )

       lowerCamelCase		:	Dict							=      shift_tokens_right(__magic_name__    ,  1    ,  2      )

       lowerCamelCase		:	str							=      BlenderbotConfig(
           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=__magic_name__    ,  )
       lowerCamelCase		:	List[str]							=      prepare_blenderbot_inputs_dict(__magic_name__    ,  __magic_name__    ,  __magic_name__      )
       return config, inputs_dict
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	Any							=      self.prepare_config_and_inputs()
       return config, inputs_dict
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Dict							=      2_0
       lowerCamelCase		:	Tuple							=      model_class_name(__magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      model.encode(inputs_dict["""input_ids"""]      )

       lowerCamelCase			,    lowerCamelCase		:	List[str]							=      (
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

       lowerCamelCase		:	int							=      model.init_cache(decoder_input_ids.shape[0]    ,  __magic_name__    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,  dtype="""i4"""      )

       lowerCamelCase		:	int							=      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]    ,  __magic_name__    ,  decoder_attention_mask=__magic_name__    ,  past_key_values=__magic_name__    ,  decoder_position_ids=__magic_name__    ,  )

       lowerCamelCase		:	str							=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,  dtype="""i4"""      )
       lowerCamelCase		:	Optional[int]							=      model.decode(
           decoder_input_ids[:, -1:]    ,  __magic_name__    ,  decoder_attention_mask=__magic_name__    ,  past_key_values=outputs_cache.past_key_values    ,  decoder_position_ids=__magic_name__    ,  )

       lowerCamelCase		:	int							=      model.decode(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	Union[str, 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 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      2_0
       lowerCamelCase		:	Tuple							=      model_class_name(__magic_name__      )

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

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

       lowerCamelCase		:	Dict							=      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]    ,  __magic_name__    ,  __magic_name__      )
       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		:	List[str]							=      model.decode(
           decoder_input_ids[:, :-1]    ,  __magic_name__    ,  decoder_attention_mask=__magic_name__    ,  past_key_values=__magic_name__    ,  decoder_position_ids=__magic_name__    ,  )
       lowerCamelCase		:	Any							=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,  dtype="""i4"""      )
       lowerCamelCase		:	Optional[Any]							=      model.decode(
           decoder_input_ids[:, -1:]    ,  __magic_name__    ,  past_key_values=outputs_cache.past_key_values    ,  decoder_attention_mask=__magic_name__    ,  decoder_position_ids=__magic_name__    ,  )

       lowerCamelCase		:	str							=      model.decode(__magic_name__    ,  __magic_name__    ,  decoder_attention_mask=__magic_name__      )

       lowerCamelCase		:	Union[str, 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__		(	unittest.TestCase):
      _UpperCAmelCase				:    str           =			99
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      np.array(
           [
               [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
               [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
               [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
               [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
               [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
               [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
               [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
               [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
               [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
               [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
               [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
               [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
               [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
           ]    ,  dtype=np.intaa    ,  )

       lowerCamelCase		:	Dict							=      input_ids.shape[0]
       lowerCamelCase		:	Optional[Any]							=      BlenderbotConfig(
           vocab_size=self.vocab_size    ,  d_model=2_4    ,  encoder_layers=2    ,  decoder_layers=2    ,  encoder_attention_heads=2    ,  decoder_attention_heads=2    ,  encoder_ffn_dim=3_2    ,  decoder_ffn_dim=3_2    ,  max_position_embeddings=4_8    ,  eos_token_id=2    ,  pad_token_id=1    ,  bos_token_id=0    ,  )
       return config, input_ids, batch_size
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	int							=      self._get_config_and_data()
       lowerCamelCase		:	Optional[Any]							=      FlaxBlenderbotForConditionalGeneration(__magic_name__      )
       lowerCamelCase		:	List[Any]							=      lm_model(input_ids=__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      BlenderbotConfig(
           vocab_size=self.vocab_size    ,  d_model=1_4    ,  encoder_layers=2    ,  decoder_layers=2    ,  encoder_attention_heads=2    ,  decoder_attention_heads=2    ,  encoder_ffn_dim=8    ,  decoder_ffn_dim=8    ,  max_position_embeddings=4_8    ,  )
       lowerCamelCase		:	Union[str, Any]							=      FlaxBlenderbotForConditionalGeneration(__magic_name__      )
       lowerCamelCase		:	str							=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]    ,  dtype=np.intaa      )
       lowerCamelCase		:	int							=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,  dtype=np.intaa      )
       lowerCamelCase		:	Optional[int]							=      lm_model(input_ids=__magic_name__    ,  decoder_input_ids=__magic_name__      )
       lowerCamelCase		:	Tuple							=      (*summary.shape, config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape    ,  __magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]    ,  dtype=np.intaa      )
       lowerCamelCase		:	Any							=      shift_tokens_right(__magic_name__    ,  1    ,  2      )
       lowerCamelCase		:	Optional[Any]							=      np.equal(__magic_name__    ,  1      ).astype(np.floataa      ).sum()
       lowerCamelCase		:	int							=      np.equal(__magic_name__    ,  1      ).astype(np.floataa      ).sum()
       self.assertEqual(shifted.shape    ,  input_ids.shape      )
       self.assertEqual(__magic_name__    ,  n_pad_before - 1      )
       self.assertTrue(np.equal(shifted[:, 0]    ,  2      ).all()      )





@require_flax
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   unittest.TestCase  ,   __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Optional[Any]           =			True
      _UpperCAmelCase				:    Union[str, Any]           =			(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      _UpperCAmelCase				:    Optional[Any]           =			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      FlaxBlenderbotModelTester(self      )
      def 						UpperCamelCase__							(      self      ):
       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(__magic_name__    ,  __magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	List[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(__magic_name__    ,  __magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__      ):
         lowerCamelCase		:	str							=      self._prepare_for_class(__magic_name__    ,  __magic_name__      )
         lowerCamelCase		:	Union[str, Any]							=      model_class(__magic_name__      )

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

         with self.subTest("""JIT Enabled"""      ):
          lowerCamelCase		:	Union[str, Any]							=      encode_jitted(**__magic_name__      ).to_tuple()

         with self.subTest("""JIT Disabled"""      ):
          with jax.disable_jit():
           lowerCamelCase		:	List[str]							=      encode_jitted(**__magic_name__      ).to_tuple()

         self.assertEqual(len(__magic_name__      )    ,  len(__magic_name__      )      )
         for jitted_output, output in zip(__magic_name__    ,  __magic_name__      ):
          self.assertEqual(jitted_output.shape    ,  output.shape      )
      def 						UpperCamelCase__							(      self      ):
       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		:	Any							=      model_class(__magic_name__      )
         lowerCamelCase		:	Dict							=      model.encode(inputs_dict["""input_ids"""]    ,  inputs_dict["""attention_mask"""]      )

         lowerCamelCase		:	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(__magic_name__    ,  __magic_name__    ,  __magic_name__      ):
          return model.decode(
              decoder_input_ids=__magic_name__    ,  decoder_attention_mask=__magic_name__    ,  encoder_outputs=__magic_name__    ,  )

         with self.subTest("""JIT Enabled"""      ):
          lowerCamelCase		:	Union[str, Any]							=      decode_jitted(**__magic_name__      ).to_tuple()

         with self.subTest("""JIT Disabled"""      ):
          with jax.disable_jit():
           lowerCamelCase		:	Union[str, Any]							=      decode_jitted(**__magic_name__      ).to_tuple()

         self.assertEqual(len(__magic_name__      )    ,  len(__magic_name__      )      )
         for jitted_output, output in zip(__magic_name__    ,  __magic_name__      ):
          self.assertEqual(jitted_output.shape    ,  output.shape      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       for model_class_name in self.all_model_classes:
        lowerCamelCase		:	Union[str, Any]							=      model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""      )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        lowerCamelCase		:	Any							=      np.ones((1, 1)      ) * model.config.eos_token_id
        lowerCamelCase		:	Tuple							=      model(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )






      @unittest.skipUnless(jax_device != """cpu"""    ,  """3B test too slow on CPU."""      )
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
       lowerCamelCase		:	Tuple							=      {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

       lowerCamelCase		:	Dict							=      FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""    ,  from_pt=__magic_name__      )
       lowerCamelCase		:	List[Any]							=      BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""      )

       lowerCamelCase		:	Union[str, Any]							=      ["""Sam"""]
       lowerCamelCase		:	Optional[Any]							=      tokenizer(__magic_name__    ,  return_tensors="""jax"""      )

       lowerCamelCase		:	Optional[Any]							=      model.generate(**__magic_name__    ,  **__magic_name__      )
       lowerCamelCase		:	Optional[int]							=      """Sam is a great name. It means \"sun\" in Gaelic."""

       lowerCamelCase		:	Tuple							=      tokenizer.batch_decode(__magic_name__    ,  **__magic_name__      )
       assert generated_txt[0].strip() == tgt_text






import copy
import os
from typing import Union

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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower_vision_model"""
      def __init__(      self    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=3    ,  __magic_name__=1_6    ,  __magic_name__=2_8_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__=True    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	str							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	List[str]							=      patch_size
       lowerCamelCase		:	Tuple							=      image_size
       lowerCamelCase		:	Any							=      initializer_factor
       lowerCamelCase		:	Tuple							=      layer_norm_eps
       lowerCamelCase		:	Tuple							=      stop_gradient
       lowerCamelCase		:	Optional[int]							=      share_layernorm
       lowerCamelCase		:	str							=      remove_last_layer






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	str							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""bridgetower_text_model"""
      def __init__(      self    ,  __magic_name__=5_0_2_6_5    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=1    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_4    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	Any							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      hidden_act
       lowerCamelCase		:	Optional[int]							=      initializer_factor
       lowerCamelCase		:	Any							=      intermediate_size
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	Union[str, Any]							=      type_vocab_size
       lowerCamelCase		:	Optional[int]							=      layer_norm_eps
       lowerCamelCase		:	Optional[int]							=      position_embedding_type
       lowerCamelCase		:	List[str]							=      use_cache
       lowerCamelCase		:	List[str]							=      pad_token_id
       lowerCamelCase		:	List[str]							=      bos_token_id
       lowerCamelCase		:	Optional[int]							=      eos_token_id






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	Optional[int]							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower"""
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__="gelu"    ,  __magic_name__=7_6_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__="add"    ,  __magic_name__=1_2    ,  __magic_name__=6    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       # TODO: remove this once the Hub files are updated.
       lowerCamelCase		:	int							=      kwargs.pop("""text_config_dict"""    ,  __magic_name__      )
       lowerCamelCase		:	str							=      kwargs.pop("""vision_config_dict"""    ,  __magic_name__      )

       super().__init__(**__magic_name__      )
       lowerCamelCase		:	str							=      share_cross_modal_transformer_layers
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Tuple							=      initializer_factor
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	int							=      share_link_tower_layers
       lowerCamelCase		:	List[Any]							=      link_tower_type
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      tie_word_embeddings
       lowerCamelCase		:	Tuple							=      init_layernorm_from_vision_encoder

       if text_config is None:
        lowerCamelCase		:	Any							=      {}
        logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""      )

       if vision_config is None:
        lowerCamelCase		:	int							=      {}
        logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""      )

       lowerCamelCase		:	Any							=      BridgeTowerTextConfig(**__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      BridgeTowerVisionConfig(**__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       return cls(text_config=text_config.to_dict()    ,  vision_config=vision_config.to_dict()    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      copy.deepcopy(self.__dict__      )
       lowerCamelCase		:	int							=      self.text_config.to_dict()
       lowerCamelCase		:	Dict							=      self.vision_config.to_dict()
       lowerCamelCase		:	List[str]							=      self.__class__.model_type
       return output




