

'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roformer.modeling_flax_roformer import (
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
      )


class        A__   (   unittest.TestCase      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=13		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=5		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=16		,					UpperCamelCase__=2		,					UpperCamelCase__=0.02		,					UpperCamelCase__=4		,					)    ->   Any:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_attention_mask
           A_						       =   use_token_type_ids
           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_						       =   type_vocab_size
           A_						       =   type_sequence_label_size
           A_						       =   initializer_range
           A_						       =   num_choices



    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

           A_						       =   None
           if self.use_attention_mask:
                  A_						       =   random_attention_mask([self.batch_size, self.seq_length]							)

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

           A_						       =   RoFormerConfig(
               vocab_size=self.vocab_size		,					hidden_size=self.hidden_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		,					is_decoder=UpperCamelCase__		,					initializer_range=self.initializer_range		,					)

           return config, input_ids, token_type_ids, attention_mask







    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           A_       ,      A_       ,      A_       ,      A_						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict


@require_flax
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		True

    lowercase						       =		(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   FlaxRoFormerModelTester(self							)







    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           for model_class_name in self.all_model_classes:
                  A_						       =   model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		,					from_pt=UpperCamelCase__							)
                  A_						       =   model(np.ones((1, 1)							)							)
                  self.assertIsNotNone(UpperCamelCase__							)


@require_flax
class        A__   (   unittest.TestCase      ):



    @slow
    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""							)
           A_						       =   jnp.array([[0, 1, 2, 3, 4, 5]]							)
           A_						       =   model(UpperCamelCase__							)[0]

           A_						       =   50000

           A_						       =   (1, 6, vocab_size)
           self.assertEqual(output.shape		,					UpperCamelCase__							)

           A_						       =   jnp.array(
               [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]							)

           self.assertTrue(jnp.allclose(output[:, :3, :3]		,					UpperCamelCase__		,					atol=1e-4							)							)



'''simple docstring'''







import copy
import os
from typing import Union

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


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_text_model"
    lowercase						       =		["past_key_values"]
    lowercase						       =		{
        "hidden_size": "hidden_size",
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }



    def __init__(       self		,					UpperCamelCase__=50244		,					UpperCamelCase__=768		,					UpperCamelCase__=64		,					UpperCamelCase__=2048		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					UpperCamelCase__=0.1		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=1.0		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=0		,					UpperCamelCase__=False		,					UpperCamelCase__=0		,					UpperCamelCase__=1		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   d_kv
           A_						       =   d_ff
           A_						       =   num_layers
           A_						       =   num_heads
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   dropout_rate
           A_						       =   layer_norm_epsilon
           A_						       =   initializer_factor
           A_						       =   use_cache

           A_						       =   eos_token_id
           A_						       =   decoder_start_token_id

           # for backwards compatibility
           A_						       =   dense_act_fn

           super().__init__(
               pad_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					decoder_start_token_id=UpperCamelCase__		,					tie_word_embeddings=UpperCamelCase__		,					is_decoder=UpperCamelCase__		,					**UpperCamelCase__		,					)







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  A_						       =   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        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_vision_model"



    def __init__(       self		,					UpperCamelCase__=768		,					UpperCamelCase__=768		,					UpperCamelCase__=2048		,					UpperCamelCase__=64		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=1e-1_0		,					UpperCamelCase__=1.0		,					UpperCamelCase__=4096		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					**UpperCamelCase__		,					)    ->   int:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)

           A_						       =   hidden_size
           A_						       =   patch_embed_hidden_size
           A_						       =   d_ff
           A_						       =   dropout_rate
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   initializer_range
           A_						       =   initializer_factor
           A_						       =   attention_dropout
           A_						       =   layer_norm_eps
           A_						       =   dense_act_fn
           A_						       =   seq_len
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   d_kv







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  A_						       =   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        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct"
    lowercase						       =		True



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=1.0		,					UpperCamelCase__=0.02		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   str:





           '''simple docstring'''
           super().__init__(tie_word_embeddings=UpperCamelCase__		,					is_encoder_decoder=UpperCamelCase__		,					**UpperCamelCase__							)

           if text_config is None:
                  A_						       =   {}
                  logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""							)

           if vision_config is None:
                  A_						       =   {}
                  logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""							)

           A_						       =   PixaStructTextConfig(**UpperCamelCase__							)
           A_						       =   PixaStructVisionConfig(**UpperCamelCase__							)

           A_						       =   self.text_config.decoder_start_token_id
           A_						       =   self.text_config.pad_token_id
           A_						       =   self.text_config.eos_token_id

           A_						       =   initializer_factor
           A_						       =   initializer_range

           A_						       =   self.initializer_range
           A_						       =   self.initializer_range

           A_						       =   is_vqa



    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           return cls(text_config=text_config.to_dict()		,					vision_config=vision_config.to_dict()		,					**UpperCamelCase__							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   copy.deepcopy(self.__dict__							)
           A_						       =   self.text_config.to_dict()
           A_						       =   self.vision_config.to_dict()
           A_						       =   self.__class__.model_type
           return output
