




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_snake_case										=       datasets.utils.logging.get_logger(__name__)







@dataclass
class 						lowercase							(   datasets.BuilderConfig		):
     _a							 =						None
     _a							 =						"utf-8"
     _a							 =						None
     _a							 =						None
     _a							 =						True  # deprecated
     _a							 =						None  # deprecated
     _a							 =						1_0 << 2_0  # 10MB
     _a							 =						None







class 						lowercase							(   datasets.ArrowBasedBuilder		):
     _a							 =						JsonConfig

     def 					a__	(  self				)						->							Union[str, Any]:
           if self.config.block_size is not None:
                 logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""				)
                 _A							:     Optional[Any]			=						self.config.block_size
           if self.config.use_threads is not True:
                 logger.warning(
                     """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""				)
           if self.config.newlines_in_values is not None:
                 raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""				)
           return datasets.DatasetInfo(features=self.config.features				)

     def 					a__	(  self		,     _a				)						->							Union[str, Any]:
           if not self.config.data_files:
                 raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''				)
           _A							:     Optional[Any]			=						dl_manager.download_and_extract(self.config.data_files				)
           if isinstance(_a		,     (str, list, tuple)				):
                 _A							:     List[str]			=						data_files
                 if isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						[files]
                 _A							:     str			=						[dl_manager.iter_files(_a				) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,     gen_kwargs={"""files""": files}				)]
           _A							:     Tuple			=						[]
           for split_name, files in data_files.items():
                 if isinstance(_a		,     _a				):
                       _A							:     Optional[int]			=						[files]
                 _A							:     Tuple			=						[dl_manager.iter_files(_a				) for file in files]
                 splits.append(datasets.SplitGenerator(name=_a		,     gen_kwargs={"""files""": files}				)				)
           return splits

     def 					a__	(  self		,     _a				)						->							pa.Table:
           if self.config.features is not None:
                 # adding missing columns
                 for column_name in set(self.config.features				) - set(pa_table.column_names				):
                       _A							:     List[Any]			=						self.config.features.arrow_schema.field(_a				).type
                       _A							:     str			=						pa_table.append_column(_a		,     pa.array([None] * len(_a				)		,     type=_a				)				)
                 # more expensive cast to support nested structures with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 _A							:     Optional[Any]			=						table_cast(_a		,     self.config.features.arrow_schema				)
           return pa_table



     def 					a__	(  self		,     _a				)						->							Optional[int]:
           for file_idx, file in enumerate(itertools.chain.from_iterable(_a				)				):
                 # If the file is one json object and if we need to look at the list of items in one specific field
                 if self.config.field is not None:
                       with open(_a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                             _A							:     Any			=						json.load(_a				)

                       # We keep only the field we are interested in
                       _A							:     Optional[int]			=						dataset[self.config.field]

                       # We accept two format: a list of dicts or a dict of lists
                       if isinstance(_a		,     (list, tuple)				):
                             _A							:     Union[str, Any]			=						set().union(*[row.keys() for row in dataset]				)
                             _A							:     Union[str, Any]			=						{col: [row.get(_a				) for row in dataset] for col in keys}
                       else:
                             _A							:     List[str]			=						dataset
                       _A							:     Optional[Any]			=						pa.Table.from_pydict(_a				)
                       yield file_idx, self._cast_table(_a				)

                 # If the file has one json object per line
                 else:
                       with open(_a		,     """rb"""				) as f:
                             _A							:     Dict			=						0
                             # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                             # Set a default minimum value of 16kB if the chunk size is really small
                             _A							:     List[str]			=						max(self.config.chunksize // 32		,     16 << 10				)
                             _A							:     Any			=						(
                                 self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                             )
                             while True:
                                   _A							:     List[Any]			=						f.read(self.config.chunksize				)
                                   if not batch:
                                         break
                                   # Finish current line
                                   try:
                                         batch += f.readline()
                                   except (AttributeError, io.UnsupportedOperation):
                                         batch += readline(_a				)
                                   # PyArrow only accepts utf-8 encoded bytes
                                   if self.config.encoding != "utf-8":
                                         _A							:     List[str]			=						batch.decode(self.config.encoding		,     errors=_a				).encode("""utf-8"""				)
                                   try:
                                         while True:
                                               try:
                                                     _A							:     Union[str, Any]			=						paj.read_json(
                                                         io.BytesIO(_a				)		,     read_options=paj.ReadOptions(block_size=_a				)				)
                                                     break
                                               except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                     if (
                                                         isinstance(_a		,     pa.ArrowInvalid				)
                                                         and "straddling" not in str(_a				)
                                                         or block_size > len(_a				)
                                                     ):
                                                           raise
                                                     else:
                                                           # Increase the block size in case it was too small.
                                                           # The block size will be reset for the next file.
                                                           logger.debug(
                                                               F'''Batch of {len(_a				)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''				)
                                                           block_size *= 2
                                   except pa.ArrowInvalid as e:
                                         try:
                                               with open(
                                                   _a		,     encoding=self.config.encoding		,     errors=self.config.encoding_errors				) as f:
                                                     _A							:     List[str]			=						json.load(_a				)
                                         except json.JSONDecodeError:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                               raise e
                                         # If possible, parse the file as a list of json objects and exit the loop
                                         if isinstance(_a		,     _a				):  # list is the only sequence type supported in JSON
                                               try:
                                                     _A							:     str			=						set().union(*[row.keys() for row in dataset]				)
                                                     _A							:     Dict			=						{col: [row.get(_a				) for row in dataset] for col in keys}
                                                     _A							:     List[Any]			=						pa.Table.from_pydict(_a				)
                                               except (pa.ArrowInvalid, AttributeError) as e:
                                                     logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                                     raise ValueError(F'''Not able to read records in the JSON file at {file}.'''				) from None
                                               yield file_idx, self._cast_table(_a				)
                                               break
                                         else:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a				)}: {e}'''				)
                                               raise ValueError(
                                                   F'''Not able to read records in the JSON file at {file}. '''
                                                   F'''You should probably indicate the field of the JSON file containing your records. '''
                                                   F'''This JSON file contain the following fields: {str(list(dataset.keys()				)				)}. '''
                                                   F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''				) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                   yield (file_idx, batch_idx), self._cast_table(_a				)
                                   batch_idx += 1






'''simple docstring'''





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     UpperCAmelCase_       :
    """simple docstring"""







    def __init__(    self					:     int ,     snake_case_					:     Tuple ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Tuple=32 ,     snake_case_					:     List[Any]=3 ,     snake_case_					:     List[str]=10 ,     snake_case_					:     List[str]=[10, 20, 30, 40] ,     snake_case_					:     Tuple=[1, 1, 2, 1] ,     snake_case_					:     Tuple=True ,     snake_case_					:     str=True ,     snake_case_					:     int="relu" ,     snake_case_					:     List[Any]=3 ,     snake_case_					:     str=None ,     ):
          snake_case__  :    List[Any]    						=      parent
          snake_case__  :    List[Any]    						=      batch_size
          snake_case__  :    int    						=      image_size
          snake_case__  :    List[Any]    						=      num_channels
          snake_case__  :    Optional[Any]    						=      embeddings_size
          snake_case__  :    Optional[int]    						=      hidden_sizes
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Any    						=      is_training
          snake_case__  :    Optional[int]    						=      use_labels
          snake_case__  :    Optional[int]    						=      hidden_act
          snake_case__  :    Optional[int]    						=      num_labels
          snake_case__  :    int    						=      scope
          snake_case__  :    Tuple    						=      len(snake_case_						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Union[str, Any]    						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

          snake_case__  :    Union[str, Any]    						=      None
          if self.use_labels:
                snake_case__  :    Optional[Any]    						=      ids_tensor([self.batch_size] ,     self.num_labels						)

          snake_case__  :    List[str]    						=      self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase       (    self					:     int						):
          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 lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     Tuple ,     snake_case_					:     List[Any] ,     snake_case_					:     Optional[int]						):
          snake_case__  :    Optional[Any]    						=      TFResNetModel(config=snake_case_						)
          snake_case__  :    int    						=      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 lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     List[str] ,     snake_case_					:     str ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    str    						=      self.num_labels
          snake_case__  :    Optional[int]    						=      TFResNetForImageClassification(snake_case_						)
          snake_case__  :    Tuple    						=      model(snake_case_ ,     labels=snake_case_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    List[Any]    						=      self.prepare_config_and_inputs()
          snake_case__						,				snake_case__						,				snake_case__  :    str    						=      config_and_inputs
          snake_case__  :    int    						=      {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_tf
class     UpperCAmelCase_       (       _a		,		_a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
    lowercase					=		(
        {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
        if is_tf_available()
        else {}
    )

    lowercase					=		False
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Tuple    						=      TFResNetModelTester(self						)
          snake_case__  :    List[str]    						=      ConfigTester(self ,     config_class=snake_case_ ,     has_text_modality=snake_case_						)




    def lowerCamelCase       (    self					:     Dict						):
          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 lowerCamelCase       (    self					:     str						):
          return




    @unittest.skip(reason="""ResNet does not use inputs_embeds"""						)
    def lowerCamelCase       (    self					:     int						):
          pass




    @unittest.skip(reason="""ResNet does not support input and output embeddings"""						)
    def lowerCamelCase       (    self					:     List[Any]						):
          pass




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__						,				snake_case__  :    List[Any]    						=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__  :    Dict    						=      model_class(snake_case_						)
                snake_case__  :    Optional[Any]    						=      inspect.signature(model.call						)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__  :    Union[str, Any]    						=      [*signature.parameters.keys()]

                snake_case__  :    Optional[int]    						=      ["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,     snake_case_						)




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Optional[int]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_						)




    def lowerCamelCase       (    self					:     List[str]						):
          def check_hidden_states_output(snake_case_					:     Any ,     snake_case_					:     Any ,     snake_case_					:     List[str]						):
                snake_case__  :    List[Any]    						=      model_class(snake_case_						)
                snake_case__  :    Dict    						=      model(**self._prepare_for_class(snake_case_ ,     snake_case_						)						)

                snake_case__  :    str    						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                snake_case__  :    List[Any]    						=      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] ,     )

          snake_case__						,				snake_case__  :    Any    						=      self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__  :    List[Any]    						=      ["""basic""", """bottleneck"""]
          for model_class in self.all_model_classes:
                for layer_type in layers_type:
                      snake_case__  :    Dict    						=      layer_type
                      snake_case__  :    Optional[int]    						=      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"]
                      snake_case__  :    List[Any]    						=      True

                      check_hidden_states_output(snake_case_ ,     snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Tuple    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     Optional[Any]						):
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__  :    str    						=      TFResNetModel.from_pretrained(snake_case_						)
                self.assertIsNotNone(snake_case_						)





def 						__snake_case(				)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
      return image







@require_tf
@require_vision
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @cached_property
    def lowerCamelCase       (    self					:     List[Any]						):
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
              if is_vision_available()
              else None
          )




    @slow
    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    List[str]    						=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

          snake_case__  :    List[Any]    						=      self.default_image_processor
          snake_case__  :    List[Any]    						=      prepare_img()
          snake_case__  :    List[str]    						=      image_processor(images=snake_case_ ,     return_tensors="""tf"""						)

          # forward pass
          snake_case__  :    Optional[Any]    						=      model(**snake_case_						)

          # verify the logits
          snake_case__  :    Union[str, Any]    						=      tf.TensorShape((1, 1_000)						)
          self.assertEqual(outputs.logits.shape ,     snake_case_						)

          snake_case__  :    List[str]    						=      tf.constant([-11.1069, -9.7877, -8.3777]						)

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