





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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = None,**__lowerCamelCase,):
        super().__init__(
            __lowerCamelCase,split=__lowerCamelCase,features=__lowerCamelCase,cache_dir=__lowerCamelCase,keep_in_memory=__lowerCamelCase,streaming=__lowerCamelCase,num_proc=__lowerCamelCase,**__lowerCamelCase,)
        A__											=						field
        A__											=						path_or_paths if isinstance(__lowerCamelCase,__lowerCamelCase ) else {self.split: path_or_paths}
        A__											=						Json(
            cache_dir=__lowerCamelCase,data_files=__lowerCamelCase,features=__lowerCamelCase,field=__lowerCamelCase,**__lowerCamelCase,)




 def 				UpperCamelCase			(							self ):
        # Build iterable dataset
        if self.streaming:
               A__											=						self.builder.as_streaming_dataset(split=self.split )
        # Build regular (map-style) dataset
        else:
               A__											=						None
               A__											=						None
               A__											=						None
               A__											=						None

               self.builder.download_and_prepare(
                   download_config=__lowerCamelCase,download_mode=__lowerCamelCase,verification_mode=__lowerCamelCase,base_path=__lowerCamelCase,num_proc=self.num_proc,)
               A__											=						self.builder.as_dataset(
                   split=self.split,verification_mode=__lowerCamelCase,in_memory=self.keep_in_memory )
        return dataset




class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,**__lowerCamelCase,):
        if num_proc is not None and num_proc <= 0:
               raise ValueError(f"num_proc {num_proc} must be an integer > 0." )

        A__											=						dataset
        A__											=						path_or_buf
        A__											=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A__											=						num_proc
        A__											=						'''utf-8'''
        A__											=						to_json_kwargs



 def 				UpperCamelCase			(							self ):
        A__											=						self.to_json_kwargs.pop('''path_or_buf''',__lowerCamelCase )
        A__											=						self.to_json_kwargs.pop('''orient''','''records''' )
        A__											=						self.to_json_kwargs.pop('''lines''',True if orient == '''records''' else False )
        A__											=						self.to_json_kwargs.pop('''index''',False if orient in ['''split''', '''table'''] else True )
        A__											=						self.to_json_kwargs.pop('''compression''',__lowerCamelCase )

        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=__lowerCamelCase ) as buffer:
                      A__											=						self._write(file_obj=__lowerCamelCase,orient=__lowerCamelCase,lines=__lowerCamelCase,index=__lowerCamelCase,**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.''' )
               A__											=						self._write(
                   file_obj=self.path_or_buf,orient=__lowerCamelCase,lines=__lowerCamelCase,index=__lowerCamelCase,**self.to_json_kwargs )
        return written



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__					,	A__					,	A__											=						args

        A__											=						query_table(
            table=self.dataset.data,key=slice(__lowerCamelCase,offset + self.batch_size ),indices=self.dataset._indices,)
        A__											=						batch.to_pandas().to_json(
            path_or_buf=__lowerCamelCase,orient=__lowerCamelCase,lines=__lowerCamelCase,index=__lowerCamelCase,**__lowerCamelCase )
        if not json_str.endswith('''\n''' ):
               json_str += "\n"
        return json_str.encode(self.encoding )




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase,):
        A__											=						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''',):
                      A__											=						self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                      written += file_obj.write(__lowerCamelCase )
        else:
               A__					,	A__											=						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,__lowerCamelCase,__lowerCamelCase )],),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(__lowerCamelCase )

        return written












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=3,__lowerCamelCase=32,__lowerCamelCase=3,__lowerCamelCase=10,__lowerCamelCase=[10, 20, 30, 40],__lowerCamelCase=[1, 1, 2, 1],__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=3,__lowerCamelCase=None,):
        A__											=						parent
        A__											=						batch_size
        A__											=						image_size
        A__											=						num_channels
        A__											=						embeddings_size
        A__											=						hidden_sizes
        A__											=						depths
        A__											=						is_training
        A__											=						use_labels
        A__											=						hidden_act
        A__											=						num_labels
        A__											=						scope
        A__											=						len(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        A__											=						self.get_config()

        return config, pixel_values



 def 				UpperCamelCase			(							self ):
        return RegNetConfig(
            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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						FlaxRegNetModel(config=__lowerCamelCase )
        A__											=						model(__lowerCamelCase )

        # Output shape (b, c, h, w)
        self.parent.assertEqual(
            result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.num_labels
        A__											=						FlaxRegNetForImageClassification(config=__lowerCamelCase )
        A__											=						model(__lowerCamelCase )
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) )




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




@require_flax
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						FlaxRegNetModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        return



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )



 @unittest.skip(reason='''RegNet does not use inputs_embeds''' )
 def 				UpperCamelCase			(							self ):
        pass



 @unittest.skip(reason='''RegNet does not support input and output embeddings''' )
 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
               A__											=						model_class(__lowerCamelCase )

               A__											=						model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) )

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

               A__											=						self.model_tester.num_stages
               self.assertEqual(len(__lowerCamelCase ),expected_num_stages + 1 )

        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               A__											=						True
               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )




 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                      A__											=						self._prepare_for_class(__lowerCamelCase,__lowerCamelCase )
                      A__											=						model_class(__lowerCamelCase )

                      @jax.jit
                      def model_jitted(__lowerCamelCase,**__lowerCamelCase ):
                             return model(pixel_values=__lowerCamelCase,**__lowerCamelCase )

                      with self.subTest('''JIT Enabled''' ):
                             A__											=						model_jitted(**__lowerCamelCase ).to_tuple()

                      with self.subTest('''JIT Disabled''' ):
                             with jax.disable_jit():
                                    A__											=						model_jitted(**__lowerCamelCase ).to_tuple()

                      self.assertEqual(len(__lowerCamelCase ),len(__lowerCamelCase ) )
                      for jitted_output, output in zip(__lowerCamelCase,__lowerCamelCase ):
                             self.assertEqual(jitted_output.shape,output.shape )


def 	UpperCamelCase__(       )->Optional[int]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




@require_flax
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 @cached_property
 def 				UpperCamelCase			(							self ):
        return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None




 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )

        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(images=__lowerCamelCase,return_tensors='''np''' )

        A__											=						model(**__lowerCamelCase )

        # verify the logits
        A__											=						(1, 1000)
        self.assertEqual(outputs.logits.shape,__lowerCamelCase )

        A__											=						jnp.array([-0.4180, -1.5051, -3.4836] )

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





