



'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
 import jax.numpy as jnp

if is_tf_available():
 import tensorflow as tf

if is_torch_available():
 import torch


class 				snake_case (unittest.TestCase   ):



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						{
               "task_specific_params": {
                   "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
                   "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
                   "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
               }
           }
           lowercase__		       =						{
               "task_specific_params.summarization.length_penalty": 1.0,
               "task_specific_params.summarization.max_length": 128,
               "task_specific_params.summarization.min_length": 12,
               "task_specific_params.summarization.num_beams": 4,
               "task_specific_params.summarization_cnn.length_penalty": 2.0,
               "task_specific_params.summarization_cnn.max_length": 142,
               "task_specific_params.summarization_cnn.min_length": 56,
               "task_specific_params.summarization_cnn.num_beams": 4,
               "task_specific_params.summarization_xsum.length_penalty": 1.0,
               "task_specific_params.summarization_xsum.max_length": 62,
               "task_specific_params.summarization_xsum.min_length": 11,
               "task_specific_params.summarization_xsum.num_beams": 6,
           }

           self.assertEqual(flatten_dict(UpperCAmelCase_      )     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						np.random.randn(3     ,4      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_      )     ,x.transpose()      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      )     ,x.transpose((1, 2, 0)      )      )      )



       @require_torch
       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_      )     ,transpose(UpperCAmelCase_      ).numpy()      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      )     ,transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      ).numpy()      )      )



       @require_tf
       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_      )     ,transpose(UpperCAmelCase_      ).numpy()      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      )     ,transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      ).numpy()      )      )



       @require_flax
       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_      )     ,np.asarray(transpose(UpperCAmelCase_      )      )      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      )     ,np.asarray(transpose(UpperCAmelCase_     ,axes=(1, 2, 0)      )      )      )      )



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						np.random.randn(3     ,4      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(4, 3)      )     ,np.reshape(UpperCAmelCase_     ,(4, 3)      )      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(12, 5)      )     ,np.reshape(UpperCAmelCase_     ,(12, 5)      )      )      )



       @require_torch
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(4, 3)      )     ,reshape(UpperCAmelCase_     ,(4, 3)      ).numpy()      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(12, 5)      )     ,reshape(UpperCAmelCase_     ,(12, 5)      ).numpy()      )      )



       @require_tf
       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(4, 3)      )     ,reshape(UpperCAmelCase_     ,(4, 3)      ).numpy()      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(12, 5)      )     ,reshape(UpperCAmelCase_     ,(12, 5)      ).numpy()      )      )



       @require_flax
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(4, 3)      )     ,np.asarray(reshape(UpperCAmelCase_     ,(4, 3)      )      )      )      )

           lowercase__		       =						np.random.randn(3     ,4     ,5      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(reshape(UpperCAmelCase_     ,(12, 5)      )     ,np.asarray(reshape(UpperCAmelCase_     ,(12, 5)      )      )      )      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						np.random.randn(1     ,3     ,4      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_      )     ,np.squeeze(UpperCAmelCase_      )      )      )

           lowercase__		       =						np.random.randn(1     ,4     ,1     ,5      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_     ,axis=2      )     ,np.squeeze(UpperCAmelCase_     ,axis=2      )      )      )



       @require_torch
       def 							_a      (				self      )  ->							int:
           lowercase__		       =						np.random.randn(1     ,3     ,4      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_      )     ,squeeze(UpperCAmelCase_      ).numpy()      )      )

           lowercase__		       =						np.random.randn(1     ,4     ,1     ,5      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_     ,axis=2      )     ,squeeze(UpperCAmelCase_     ,axis=2      ).numpy()      )      )



       @require_tf
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						np.random.randn(1     ,3     ,4      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_      )     ,squeeze(UpperCAmelCase_      ).numpy()      )      )

           lowercase__		       =						np.random.randn(1     ,4     ,1     ,5      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_     ,axis=2      )     ,squeeze(UpperCAmelCase_     ,axis=2      ).numpy()      )      )



       @require_flax
       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						np.random.randn(1     ,3     ,4      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_      )     ,np.asarray(squeeze(UpperCAmelCase_      )      )      )      )

           lowercase__		       =						np.random.randn(1     ,4     ,1     ,5      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(squeeze(UpperCAmelCase_     ,axis=2      )     ,np.asarray(squeeze(UpperCAmelCase_     ,axis=2      )      )      )      )



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						np.random.randn(3     ,4      )
           self.assertTrue(np.allclose(expand_dims(UpperCAmelCase_     ,axis=1      )     ,np.expand_dims(UpperCAmelCase_     ,axis=1      )      )      )



       @require_torch
       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						torch.tensor(UpperCAmelCase_      )
           self.assertTrue(np.allclose(expand_dims(UpperCAmelCase_     ,axis=1      )     ,expand_dims(UpperCAmelCase_     ,axis=1      ).numpy()      )      )



       @require_tf
       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						tf.constant(UpperCAmelCase_      )
           self.assertTrue(np.allclose(expand_dims(UpperCAmelCase_     ,axis=1      )     ,expand_dims(UpperCAmelCase_     ,axis=1      ).numpy()      )      )







       @require_flax
       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						np.random.randn(3     ,4      )
           lowercase__		       =						jnp.array(UpperCAmelCase_      )
           self.assertTrue(np.allclose(expand_dims(UpperCAmelCase_     ,axis=1      )     ,np.asarray(expand_dims(UpperCAmelCase_     ,axis=1      )      )      )      )









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						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(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_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(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




