





import copy
import os
from typing import Union

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


_UpperCAmelCase				:       Union[str, Any]	   =				logging.get_logger(__name__)

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

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""pix2struct_text_model"""
 UpperCAmelCase__													=		["""past_key_values"""]
 UpperCAmelCase__													=		{
     """hidden_size""": """hidden_size""",
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(      self  :      str			,     UpperCAmelCase  :      Any=50244			,     UpperCAmelCase  :      Union[str, Any]=768			,     UpperCAmelCase  :      List[Any]=64			,     UpperCAmelCase  :      str=2048			,     UpperCAmelCase  :      Optional[int]=12			,     UpperCAmelCase  :      Optional[Any]=12			,     UpperCAmelCase  :      Tuple=32			,     UpperCAmelCase  :      Optional[Any]=128			,     UpperCAmelCase  :      List[str]=0.1			,     UpperCAmelCase  :      List[Any]=1e-6			,     UpperCAmelCase  :      List[str]=1.0			,     UpperCAmelCase  :      Any="gelu_new"			,     UpperCAmelCase  :      Optional[int]=0			,     UpperCAmelCase  :      Optional[int]=False			,     UpperCAmelCase  :      List[Any]=0			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      Dict=False			,     UpperCAmelCase  :      List[str]=True			,     **UpperCAmelCase  :      Optional[int]			,     )			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      List[str]					 =		hidden_size
     lowerCamelCase__    :      Optional[int]					 =		d_kv
     lowerCamelCase__    :      Union[str, Any]					 =		d_ff
     lowerCamelCase__    :      Dict					 =		num_layers
     lowerCamelCase__    :      Tuple					 =		num_heads
     lowerCamelCase__    :      str					 =		relative_attention_num_buckets
     lowerCamelCase__    :      Optional[Any]					 =		relative_attention_max_distance
     lowerCamelCase__    :      Tuple					 =		dropout_rate
     lowerCamelCase__    :      str					 =		layer_norm_epsilon
     lowerCamelCase__    :      str					 =		initializer_factor
     lowerCamelCase__    :      Optional[Any]					 =		use_cache

     lowerCamelCase__    :      Dict					 =		eos_token_id
     lowerCamelCase__    :      Dict					 =		decoder_start_token_id

     # for backwards compatibility
     lowerCamelCase__    :      Tuple					 =		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      A_	(      cls  :      Tuple			,     UpperCAmelCase  :      Union[str, os.PathLike]			,     **UpperCAmelCase  :      str	)			->	"PretrainedConfig":
     cls._set_token_in_kwargs(UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		cls.get_config_dict(UpperCAmelCase			,     **UpperCAmelCase	)

     # get the text config dict if we are loading from Pix2StructConfig
     if config_dict.get('model_type'	) == "pix2struct":
         lowerCamelCase__    :      Union[str, Any]					 =		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    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""pix2struct_vision_model"""
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      int=768			,     UpperCAmelCase  :      Optional[Any]=768			,     UpperCAmelCase  :      Optional[Any]=2048			,     UpperCAmelCase  :      str=64			,     UpperCAmelCase  :      Union[str, Any]=12			,     UpperCAmelCase  :      List[str]=12			,     UpperCAmelCase  :      Any="gelu_new"			,     UpperCAmelCase  :      List[Any]=1e-6			,     UpperCAmelCase  :      Any=0.0			,     UpperCAmelCase  :      List[Any]=0.0			,     UpperCAmelCase  :      Union[str, Any]=1e-10			,     UpperCAmelCase  :      List[Any]=1.0			,     UpperCAmelCase  :      Optional[int]=4096			,     UpperCAmelCase  :      Union[str, Any]=32			,     UpperCAmelCase  :      Union[str, Any]=128			,     **UpperCAmelCase  :      List[str]			,     )			->	Tuple:
     super().__init__(**UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		hidden_size
     lowerCamelCase__    :      Any					 =		patch_embed_hidden_size
     lowerCamelCase__    :      Any					 =		d_ff
     lowerCamelCase__    :      int					 =		dropout_rate
     lowerCamelCase__    :      Optional[Any]					 =		num_hidden_layers
     lowerCamelCase__    :      Optional[int]					 =		num_attention_heads
     lowerCamelCase__    :      int					 =		initializer_range
     lowerCamelCase__    :      Optional[int]					 =		initializer_factor
     lowerCamelCase__    :      int					 =		attention_dropout
     lowerCamelCase__    :      str					 =		layer_norm_eps
     lowerCamelCase__    :      List[Any]					 =		dense_act_fn
     lowerCamelCase__    :      int					 =		seq_len
     lowerCamelCase__    :      str					 =		relative_attention_num_buckets
     lowerCamelCase__    :      List[str]					 =		relative_attention_max_distance
     lowerCamelCase__    :      Dict					 =		d_kv







 @classmethod
 def      A_	(      cls  :      Any			,     UpperCAmelCase  :      Union[str, os.PathLike]			,     **UpperCAmelCase  :      int	)			->	"PretrainedConfig":
     cls._set_token_in_kwargs(UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		cls.get_config_dict(UpperCAmelCase			,     **UpperCAmelCase	)

     # get the vision config dict if we are loading from Pix2StructConfig
     if config_dict.get('model_type'	) == "pix2struct":
         lowerCamelCase__    :      Optional[Any]					 =		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    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""pix2struct"""
 UpperCAmelCase__													=		True
 def __init__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]=None			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      str=1.0			,     UpperCAmelCase  :      str=0.0_2			,     UpperCAmelCase  :      int=False			,     UpperCAmelCase  :      Optional[int]=False			,     UpperCAmelCase  :      List[Any]=True			,     **UpperCAmelCase  :      Dict			,     )			->	Optional[int]:
     super().__init__(tie_word_embeddings=UpperCAmelCase			,     is_encoder_decoder=UpperCAmelCase			,     **UpperCAmelCase	)

     if text_config is None:
         lowerCamelCase__    :      List[Any]					 =		{}
         logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'	)

     if vision_config is None:
         lowerCamelCase__    :      Tuple					 =		{}
         logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'	)

     lowerCamelCase__    :      List[Any]					 =		PixaStructTextConfig(**UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		PixaStructVisionConfig(**UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		self.text_config.decoder_start_token_id
     lowerCamelCase__    :      int					 =		self.text_config.pad_token_id
     lowerCamelCase__    :      Any					 =		self.text_config.eos_token_id

     lowerCamelCase__    :      Optional[int]					 =		initializer_factor
     lowerCamelCase__    :      Any					 =		initializer_range

     lowerCamelCase__    :      Union[str, Any]					 =		self.initializer_range
     lowerCamelCase__    :      Optional[Any]					 =		self.initializer_range

     lowerCamelCase__    :      List[str]					 =		is_vqa
 @classmethod
 def      A_	(      cls  :      Dict			,     UpperCAmelCase  :      PixaStructTextConfig			,     UpperCAmelCase  :      PixaStructVisionConfig			,     **UpperCAmelCase  :      Union[str, Any]	)			->	str:
     return cls(text_config=text_config.to_dict()			,     vision_config=vision_config.to_dict()			,     **UpperCAmelCase	)







 def      A_	(      self  :      int	)			->	Optional[int]:
     lowerCamelCase__    :      Dict					 =		copy.deepcopy(self.__dict__	)
     lowerCamelCase__    :      List[Any]					 =		self.text_config.to_dict()
     lowerCamelCase__    :      Any					 =		self.vision_config.to_dict()
     lowerCamelCase__    :      List[Any]					 =		self.__class__.model_type
     return output












import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_UpperCAmelCase				:       str	   =				pytest.mark.integration

@require_faiss
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      List[Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      List[Any]					 =		Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase	) for x in np.arange(30	).tolist()]}	)
     return dset
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     lowerCamelCase__    :      List[Any]					 =		dset.map(
         lambda UpperCAmelCase			,     UpperCAmelCase	: {"vecs": i * np.ones(5			,     dtype=np.floataa	)}			,     with_indices=UpperCAmelCase			,     keep_in_memory=UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		dset.add_faiss_index('vecs'			,     batch_size=100			,     metric_type=faiss.METRIC_INNER_PRODUCT	)
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		dset.get_nearest_examples('vecs'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
     dset.drop_index('vecs'	)
 def      A_	(      self  :      Union[str, Any]	)			->	int:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)	) * np.arange(30	).reshape(-1			,     1	)			,     index_name='vecs'			,     batch_size=100			,     metric_type=faiss.METRIC_INNER_PRODUCT			,     )
     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		dset.get_nearest_examples('vecs'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
 def      A_	(      self  :      List[str]	)			->	Tuple:
     import faiss

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)	) * np.arange(30	).reshape(-1			,     1	)			,     index_name='vecs'			,     metric_type=faiss.METRIC_INNER_PRODUCT			,     )

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=UpperCAmelCase	) as tmp_file:
         dset.save_faiss_index('vecs'			,     tmp_file.name	)
         dset.load_faiss_index('vecs2'			,     tmp_file.name	)
     os.unlink(tmp_file.name	)

     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		dset.get_nearest_examples('vecs2'			,     np.ones(5			,     dtype=np.floataa	)	)
     self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)
 def      A_	(      self  :      Any	)			->	Optional[Any]:
     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((30, 5)	) * np.arange(30	).reshape(-1			,     1	)			,     index_name='vecs'	)
     dset.drop_index('vecs'	)
     self.assertRaises(UpperCAmelCase			,     partial(dset.get_nearest_examples			,     'vecs2'			,     np.ones(5			,     dtype=np.floataa	)	)	)







 def      A_	(      self  :      Dict	)			->	Dict:
     from elasticsearch import Elasticsearch

     lowerCamelCase__    :      Dataset					 =		self._create_dummy_dataset()
     with patch('elasticsearch.Elasticsearch.search'	) as mocked_search, patch(
         'elasticsearch.client.IndicesClient.create'	) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'	) as mocked_bulk:
         lowerCamelCase__    :      List[Any]					 =		{'acknowledged': True}
         mocked_bulk.return_value([(True, None)] * 30	)
         lowerCamelCase__    :      int					 =		{'hits': {'hits': [{'_score': 1, '_id': 29}]}}
         lowerCamelCase__    :      List[str]					 =		Elasticsearch()

         dset.add_elasticsearch_index('filename'			,     es_client=UpperCAmelCase	)
         lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		dset.get_nearest_examples('filename'			,     'my_name-train_29'	)
         self.assertEqual(examples['filename'][0]			,     'my_name-train_29'	)


@require_faiss
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Any	)			->	Dict:
     import faiss

     lowerCamelCase__    :      Tuple					 =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT	)

     # add vectors
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsNotNone(index.faiss_index	)
     self.assertEqual(index.faiss_index.ntotal			,     5	)
     index.add_vectors(np.zeros((5, 5)			,     dtype=np.floataa	)	)
     self.assertEqual(index.faiss_index.ntotal			,     10	)

     # single query
     lowerCamelCase__    :      int					 =		np.zeros(5			,     dtype=np.floataa	)
     lowerCamelCase__    :      Any					 =		1
     lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		index.search(UpperCAmelCase	)
     self.assertRaises(UpperCAmelCase			,     index.search			,     query.reshape(-1			,     1	)	)
     self.assertGreater(scores[0]			,     0	)
     self.assertEqual(indices[0]			,     1	)

     # batched queries
     lowerCamelCase__    :      str					 =		np.eye(5			,     dtype=np.floataa	)[::-1]
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		index.search_batch(UpperCAmelCase	)
     self.assertRaises(UpperCAmelCase			,     index.search_batch			,     queries[0]	)
     lowerCamelCase__    :      str					 =		[scores[0] for scores in total_scores]
     lowerCamelCase__    :      List[str]					 =		[indices[0] for indices in total_indices]
     self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
     self.assertListEqual([4, 3, 2, 1, 0]			,     UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	List[Any]:
     import faiss

     lowerCamelCase__    :      Any					 =		FaissIndex(string_factory='Flat'	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexFlat	)
     lowerCamelCase__    :      Tuple					 =		FaissIndex(string_factory='LSH'	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexLSH	)
     with self.assertRaises(UpperCAmelCase	):
         lowerCamelCase__    :      List[str]					 =		FaissIndex(string_factory='Flat'			,     custom_index=faiss.IndexFlat(5	)	)
 def      A_	(      self  :      List[str]	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Optional[Any]					 =		faiss.IndexFlat(5	)
     lowerCamelCase__    :      int					 =		FaissIndex(custom_index=UpperCAmelCase	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)
     self.assertIsInstance(index.faiss_index			,     faiss.IndexFlat	)







 def      A_	(      self  :      Any	)			->	Optional[int]:
     import faiss

     lowerCamelCase__    :      Any					 =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT	)
     index.add_vectors(np.eye(5			,     dtype=np.floataa	)	)

     # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
     # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
     # see https://bugs.python.org/issue14243 and
     # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
     with tempfile.NamedTemporaryFile(delete=UpperCAmelCase	) as tmp_file:
         index.save(tmp_file.name	)
         lowerCamelCase__    :      List[Any]					 =		FaissIndex.load(tmp_file.name	)
     os.unlink(tmp_file.name	)

     lowerCamelCase__    :      List[str]					 =		np.zeros(5			,     dtype=np.floataa	)
     lowerCamelCase__    :      Tuple					 =		1
     lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search(UpperCAmelCase	)
     self.assertGreater(scores[0]			,     0	)
     self.assertEqual(indices[0]			,     1	)


@require_faiss
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Any:
    import faiss

    lowerCamelCase__    :      Optional[Any]					 =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
    index.add_vectors(np.eye(5						,	dtype=np.floataa ) )

    lowerCamelCase__    :      Optional[int]					 =		'index.faiss'
    lowerCamelCase__    :      Optional[Any]					 =		F"""mock://{index_name}"""
    index.save(_UpperCAmelCase						,	storage_options=mockfs.storage_options )
    lowerCamelCase__    :      Tuple					 =		FaissIndex.load(_UpperCAmelCase						,	storage_options=mockfs.storage_options )

    lowerCamelCase__    :      Optional[int]					 =		np.zeros(5						,	dtype=np.floataa )
    lowerCamelCase__    :      Dict					 =		1
    lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search(_UpperCAmelCase )
    assert scores[0] > 0
    assert indices[0] == 1


@require_elasticsearch
class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      Dict	)			->	List[Any]:
     from elasticsearch import Elasticsearch

     with patch('elasticsearch.Elasticsearch.search'	) as mocked_search, patch(
         'elasticsearch.client.IndicesClient.create'	) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'	) as mocked_bulk:
         lowerCamelCase__    :      Any					 =		Elasticsearch()
         lowerCamelCase__    :      Tuple					 =		{'acknowledged': True}
         lowerCamelCase__    :      Tuple					 =		ElasticSearchIndex(es_client=UpperCAmelCase	)
         mocked_bulk.return_value([(True, None)] * 3	)
         index.add_documents(['foo', 'bar', 'foobar']	)

         # single query
         lowerCamelCase__    :      Optional[int]					 =		'foo'
         lowerCamelCase__    :      str					 =		{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		index.search(UpperCAmelCase	)
         self.assertEqual(scores[0]			,     1	)
         self.assertEqual(indices[0]			,     0	)

         # single query with timeout
         lowerCamelCase__    :      Any					 =		'foo'
         lowerCamelCase__    :      List[str]					 =		{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		index.search(UpperCAmelCase			,     request_timeout=30	)
         self.assertEqual(scores[0]			,     1	)
         self.assertEqual(indices[0]			,     0	)

         # batched queries
         lowerCamelCase__    :      List[str]					 =		['foo', 'bar', 'foobar']
         lowerCamelCase__    :      str					 =		{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		index.search_batch(UpperCAmelCase	)
         lowerCamelCase__    :      List[str]					 =		[scores[0] for scores in total_scores]
         lowerCamelCase__    :      List[Any]					 =		[indices[0] for indices in total_indices]
         self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
         self.assertListEqual([1, 1, 1]			,     UpperCAmelCase	)

         # batched queries with timeout
         lowerCamelCase__    :      str					 =		['foo', 'bar', 'foobar']
         lowerCamelCase__    :      List[Any]					 =		{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
         lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		index.search_batch(UpperCAmelCase			,     request_timeout=30	)
         lowerCamelCase__    :      Optional[Any]					 =		[scores[0] for scores in total_scores]
         lowerCamelCase__    :      Dict					 =		[indices[0] for indices in total_indices]
         self.assertGreater(np.min(UpperCAmelCase	)			,     0	)
         self.assertListEqual([1, 1, 1]			,     UpperCAmelCase	)





