

import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   List[str]					  =							np.inf

	def set_batch_size(_a) -> None:
		nonlocal batch_size
		if isinstance(_a				,      _a):
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							min(_a				,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS)
		elif isinstance(_a				,      _a):
			SCREAMING_SNAKE_CASE :   Dict					  =							min(_a				,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS)
		elif isinstance(_a				,      _a) and feature.dtype == "binary":
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							min(_a				,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS)

	_visit(_a				,      _a)

	return None if batch_size is np.inf else batch_size






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			NestedDataStructureLike[PathLike]   ,   a				:			Optional[NamedSplit] = None   ,   a				:			Optional[Features] = None   ,   a				:			str = None   ,   a				:			bool = False   ,   a				:			bool = False   ,   a				:			Optional[int] = None   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(
				    a   ,   split=a   ,   features=a   ,   cache_dir=a   ,   keep_in_memory=a   ,   streaming=a   ,   num_proc=a   ,   **a   ,   )
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							path_or_paths if isinstance(a   ,   a						) else {self.split: path_or_paths}
				SCREAMING_SNAKE_CASE :   Any					  =							_PACKAGED_DATASETS_MODULES["parquet"][1]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							Parquet(
				    cache_dir=a   ,   data_files=a   ,   features=a   ,   hash=a   ,   **a   ,   )






			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				if self.streaming:
					SCREAMING_SNAKE_CASE :   List[str]					  =							self.builder.as_streaming_dataset(split=self.split						)
				# Build regular (map-style) dataset
				else:
					SCREAMING_SNAKE_CASE :   int					  =							None
					SCREAMING_SNAKE_CASE :   Any					  =							None
					SCREAMING_SNAKE_CASE :   Dict					  =							None
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							None

					self.builder.download_and_prepare(
					    download_config=a   ,   download_mode=a   ,   verification_mode=a   ,   base_path=a   ,   num_proc=self.num_proc   ,   )
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.builder.as_dataset(
					    split=self.split   ,   verification_mode=a   ,   in_memory=self.keep_in_memory						)
				return dataset






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Union[str, Any]   ,   a				:			Dataset   ,   a				:			Union[PathLike, BinaryIO]   ,   a				:			Optional[int] = None   ,   **a				:			Optional[Any]   ,   )				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							dataset
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							path_or_buf
				SCREAMING_SNAKE_CASE :   str					  =							batch_size or get_writer_batch_size(dataset.features						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							parquet_writer_kwargs
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

				if isinstance(self.path_or_buf   ,   (str, bytes, os.PathLike)						):
					with open(self.path_or_buf   ,   "wb+"						) as buffer:
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							self._write(file_obj=a   ,   batch_size=a   ,   **self.parquet_writer_kwargs						)
				else:
					SCREAMING_SNAKE_CASE :   Dict					  =							self._write(file_obj=self.path_or_buf   ,   batch_size=a   ,   **self.parquet_writer_kwargs						)
				return written






			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			BinaryIO   ,   a				:			int   ,   **a				:			str						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							0
				SCREAMING_SNAKE_CASE :   Tuple					  =							parquet_writer_kwargs.pop("path_or_buf"   ,   a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dataset.features.arrow_schema

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pq.ParquetWriter(a   ,   schema=a   ,   **a						)

				for offset in logging.tqdm(
				    range(0   ,   len(self.dataset						)   ,   a						)   ,   unit="ba"   ,   disable=not logging.is_progress_bar_enabled()   ,   desc="Creating parquet from Arrow format"   ,   ):
					SCREAMING_SNAKE_CASE :   Dict					  =							query_table(
					    table=self.dataset._data   ,   key=slice(a   ,   offset + batch_size						)   ,   indices=self.dataset._indices if self.dataset._indices is not None else None   ,   )
					writer.write_table(a						)
					written += batch.nbytes
				writer.close()
				return written





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFResNetForImageClassification, TFResNetModel
						from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class    lowercase_		:



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Tuple=3					,							snake_case__	:   Union[str, Any]=32					,							snake_case__	:   Union[str, Any]=3					,							snake_case__	:   str=10					,							snake_case__	:   List[Any]=[10, 20, 30, 40]					,							snake_case__	:   Optional[Any]=[1, 1, 2, 1]					,							snake_case__	:   str=True					,							snake_case__	:   str=True					,							snake_case__	:   List[str]="relu"					,							snake_case__	:   Tuple=3					,							snake_case__	:   str=None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent
								SCREAMING_SNAKE_CASE_					      =							batch_size
								SCREAMING_SNAKE_CASE_					      =							image_size
								SCREAMING_SNAKE_CASE_					      =							num_channels
								SCREAMING_SNAKE_CASE_					      =							embeddings_size
								SCREAMING_SNAKE_CASE_					      =							hidden_sizes
								SCREAMING_SNAKE_CASE_					      =							depths
								SCREAMING_SNAKE_CASE_					      =							is_training
								SCREAMING_SNAKE_CASE_					      =							use_labels
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							num_labels
								SCREAMING_SNAKE_CASE_					      =							scope
								SCREAMING_SNAKE_CASE_					      =							len(snake_case__  )



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								SCREAMING_SNAKE_CASE_					      =							None
								if self.use_labels:
										SCREAMING_SNAKE_CASE_					      =							ids_tensor([self.batch_size]					,							self.num_labels  )

								SCREAMING_SNAKE_CASE_					      =							self.get_config()

								return config, pixel_values, labels



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								return ResNetConfig(
								    num_channels=self.num_channels					,							embeddings_size=self.embeddings_size					,							hidden_sizes=self.hidden_sizes					,							depths=self.depths					,							hidden_act=self.hidden_act					,							num_labels=self.num_labels					,							image_size=self.image_size					,							)



						def 	__a					(			self	:   str					,							snake_case__	:   str					,							snake_case__	:   Tuple					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModel(config=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__  )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape					,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,							)



						def 	__a					(			self	:   int					,							snake_case__	:   Optional[Any]					,							snake_case__	:   int					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.num_labels
								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							labels=snake_case__  )
								self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_labels)  )






						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.prepare_config_and_inputs()
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							config_and_inputs
								SCREAMING_SNAKE_CASE_					      =							{'pixel_values': pixel_values}
								return config, inputs_dict



@require_tf
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						lowerCAmelCase__			      =(
						    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__					,							has_text_modality=snake_case__  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return



						@unittest.skip(reason='ResNet does not use inputs_embeds'  )
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								pass



						@unittest.skip(reason='ResNet does not support input and output embeddings'  )
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								pass



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_					      =							model_class(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							inspect.signature(model.call  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										SCREAMING_SNAKE_CASE_					      =							[*signature.parameters.keys()]

										SCREAMING_SNAKE_CASE_					      =							['pixel_values']
										self.assertListEqual(arg_names[:1]					,							snake_case__  )



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								def check_hidden_states_output(snake_case__	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   Dict  ):
										SCREAMING_SNAKE_CASE_					      =							model_class(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							model(**self._prepare_for_class(snake_case__					,							snake_case__  )  )

										SCREAMING_SNAKE_CASE_					      =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										SCREAMING_SNAKE_CASE_					      =							self.model_tester.num_stages
										self.assertEqual(len(snake_case__  )					,							expected_num_stages + 1  )

										# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]  )					,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,							)

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE_					      =							['basic', 'bottleneck']
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												SCREAMING_SNAKE_CASE_					      =							layer_type
												SCREAMING_SNAKE_CASE_					      =							True
												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												SCREAMING_SNAKE_CASE_					      =							True

												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__  )






						@slow
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_					      =							TFResNetModel.from_pretrained(snake_case__  )
										self.assertIsNotNone(snake_case__  )


def 					_a							(					)->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image




@require_tf
@require_vision
class    lowercase_		(unittest.TestCase ):



						@cached_property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
								    if is_vision_available()
								    else None
								)






						@slow
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

								SCREAMING_SNAKE_CASE_					      =							self.default_image_processor
								SCREAMING_SNAKE_CASE_					      =							prepare_img()
								SCREAMING_SNAKE_CASE_					      =							image_processor(images=snake_case__					,							return_tensors='tf'  )

								# forward pass
								SCREAMING_SNAKE_CASE_					      =							model(**snake_case__  )

								# verify the logits
								SCREAMING_SNAKE_CASE_					      =							tf.TensorShape((1, 10_00)  )
								self.assertEqual(outputs.logits.shape					,							snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tf.constant([-11.10_69, -9.78_77, -8.37_77]  )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()					,							snake_case__					,							atol=1e-4  )  )