'''simple docstring'''




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class       UpperCAmelCase__  (       __A			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a = None       ,				_a = None       ,				_a = None       ,				_a = False       ,				_a = False       ,				_a = None       ,				_a = None       ,				**_a       ,				)    ->     Optional[Any]:
        super().__init__(
            UpperCamelCase__       ,				split=UpperCamelCase__       ,				features=UpperCamelCase__       ,				cache_dir=UpperCamelCase__       ,				keep_in_memory=UpperCamelCase__       ,				streaming=UpperCamelCase__       ,				num_proc=UpperCamelCase__       ,				**UpperCamelCase__       ,				)
        lowercase_					:       Any     				=					field
        lowercase_					:       str     				=					path_or_paths if isinstance(UpperCamelCase__       ,				UpperCamelCase__				) else {self.split: path_or_paths}
        lowercase_					:       Optional[Any]     				=					Json(
            cache_dir=UpperCamelCase__       ,				data_files=UpperCamelCase__       ,				features=UpperCamelCase__       ,				field=UpperCamelCase__       ,				**UpperCamelCase__       ,				)





   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # Build iterable dataset
        if self.streaming:
             lowercase_					:       Union[str, Any]     				=					self.builder.as_streaming_dataset(split=self.split				)
        # Build regular (map-style) dataset
        else:
             lowercase_					:       List[Any]     				=					None
             lowercase_					:       List[str]     				=					None
             lowercase_					:       int     				=					None
             lowercase_					:       Tuple     				=					None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__       ,				download_mode=UpperCamelCase__       ,				verification_mode=UpperCamelCase__       ,				base_path=UpperCamelCase__       ,				num_proc=self.num_proc       ,				)
             lowercase_					:       List[str]     				=					self.builder.as_dataset(
                 split=self.split       ,				verification_mode=UpperCamelCase__       ,				in_memory=self.keep_in_memory				)
        return dataset



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a       ,				_a = None       ,				_a = None       ,				**_a       ,				)    ->     List[Any]:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''				)

        lowercase_					:       int     				=					dataset
        lowercase_					:       Dict     				=					path_or_buf
        lowercase_					:       str     				=					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        lowercase_					:       Union[str, Any]     				=					num_proc
        lowercase_					:       Union[str, Any]     				=					'utf-8'
        lowercase_					:       Optional[Any]     				=					to_json_kwargs
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Union[str, Any]     				=					self.to_json_kwargs.pop('path_or_buf'       ,				UpperCamelCase__				)
        lowercase_					:       List[Any]     				=					self.to_json_kwargs.pop('orient'       ,				'records'				)
        lowercase_					:       Union[str, Any]     				=					self.to_json_kwargs.pop('lines'       ,				True if orient == 'records' else False				)
        lowercase_					:       int     				=					self.to_json_kwargs.pop('index'       ,				False if orient in ['split', 'table'] else True				)
        lowercase_					:       List[str]     				=					self.to_json_kwargs.pop('compression'       ,				UpperCamelCase__				)

        if compression not in [None, "infer", "gzip", "bz2", "xz"]:
             raise NotImplementedError(f'''`datasets` currently does not support {compression} compression'''				)

        if isinstance(self.path_or_buf       ,				(str, bytes, os.PathLike)				):
             with fsspec.open(self.path_or_buf       ,				'wb'       ,				compression=UpperCamelCase__				) as buffer:
                  lowercase_					:       Dict     				=					self._write(file_obj=UpperCamelCase__       ,				orient=UpperCamelCase__       ,				lines=UpperCamelCase__       ,				index=UpperCamelCase__       ,				**self.to_json_kwargs				)
        else:
             if compression:
                  raise NotImplementedError(
                      f'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
                      ' was passed. Please provide a local path instead.'				)
             lowercase_					:       List[Any]     				=					self._write(
                 file_obj=self.path_or_buf       ,				orient=UpperCamelCase__       ,				lines=UpperCamelCase__       ,				index=UpperCamelCase__       ,				**self.to_json_kwargs				)
        return written
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        lowercase_					:       Tuple     				=					args

        lowercase_					:       Union[str, Any]     				=					query_table(
            table=self.dataset.data       ,				key=slice(UpperCamelCase__       ,				offset + self.batch_size				)       ,				indices=self.dataset._indices       ,				)
        lowercase_					:       Optional[Any]     				=					batch.to_pandas().to_json(
            path_or_buf=UpperCamelCase__       ,				orient=UpperCamelCase__       ,				lines=UpperCamelCase__       ,				index=UpperCamelCase__       ,				**UpperCamelCase__				)
        if not json_str.endswith('\n'				):
             json_str += "\n"
        return json_str.encode(self.encoding				)





   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				**_a       ,				)    ->     List[Any]:
        lowercase_					:       str     				=					0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0       ,				len(self.dataset				)       ,				self.batch_size				)       ,				unit='ba'       ,				disable=not logging.is_progress_bar_enabled()       ,				desc='Creating json from Arrow format'       ,				):
                  lowercase_					:       List[Any]     				=					self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
                  written += file_obj.write(UpperCamelCase__				)
        else:
             lowercase_					:       str     				=					len(self.dataset				), self.batch_size
             with multiprocessing.Pool(self.num_proc				) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json       ,				[(offset, orient, lines, index, to_json_kwargs) for offset in range(0       ,				UpperCamelCase__       ,				UpperCamelCase__				)]       ,				)       ,				total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size       ,				unit='ba'       ,				disable=not logging.is_progress_bar_enabled()       ,				desc='Creating json from Arrow format'       ,				):
                       written += file_obj.write(UpperCamelCase__				)

        return written





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

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


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=12       ,				_a=7       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=99       ,				_a=32       ,				_a=32       ,				_a=2       ,				_a=4       ,				_a=37       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=0.02       ,				_a=0       ,				_a=None       ,				)    ->     Optional[Any]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       List[Any]     				=					batch_size
        lowercase_					:       Optional[Any]     				=					seq_length
        lowercase_					:       Optional[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_input_mask
        lowercase_					:       Any     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					projection_dim
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					dropout
        lowercase_					:       Tuple     				=					attention_dropout
        lowercase_					:       Optional[Any]     				=					max_position_embeddings
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					scope
        lowercase_					:       Optional[Any]     				=					bos_token_id
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_input_mask:
             lowercase_					:       Dict     				=					random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
             lowercase_					:       Dict     				=					input_mask.numpy()
             lowercase_				,lowercase_					:       int     				=					input_mask.shape
             lowercase_					:       int     				=					np.random.randint(1       ,				seq_length - 1       ,				size=(batch_size,)				)
             for batch_idx, start_index in enumerate(_a				):
                  lowercase_					:       Tuple     				=					1
                  lowercase_					:       List[Any]     				=					0

        lowercase_					:       List[Any]     				=					self.get_config()

        return config, input_ids, tf.convert_to_tensor(_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        return BlipTextConfig(
            vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				projection_dim=self.projection_dim       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				dropout=self.dropout       ,				attention_dropout=self.attention_dropout       ,				max_position_embeddings=self.max_position_embeddings       ,				initializer_range=self.initializer_range       ,				bos_token_id=self.bos_token_id       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFBlipTextModel(config=_a				)
        lowercase_					:       List[str]     				=					model(_a       ,				attention_mask=_a       ,				training=_a				)
        lowercase_					:       Union[str, Any]     				=					model(_a       ,				training=_a				)
        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 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Any     =    (TFBlipTextModel,) if is_tf_available() else ()
   A							:       Union[str, Any]     =    False
   A							:       int     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					BlipTextModelTester(self				)
        lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        pass
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='Blip does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[str]     				=					TFBlipTextModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)





   def 		_lowerCamelCase (self       ,				_a=True				)    ->     Optional[int]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=_a				)




