"""simple docstring"""



# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


__lowercase		:		Optional[int]														=							get_logger(__name__)



class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					Any							=					'dummy_data'
    UpperCamelCase_	:					List[str]							=					'datasets'
    UpperCamelCase_	:					Dict							=					False
    def __init__(		self							:     str	,      A_							:     str	,      A_							:     str	,      A_							:     Union[Version, str]	,      A_							:     Optional[str] = None	,      A_							:     bool = False	,      A_							:     bool = True	,      A_							:     Optional[List[Callable]] = None	,      )    ->       Optional[Any]:
         __snake_case				=							0
         __snake_case				=							dataset_name
         __snake_case				=							cache_dir
         __snake_case				=							use_local_dummy_data
         __snake_case				=							config
         # download_callbacks take a single url as input
         __snake_case				=							download_callbacks or []
         # if False, it doesn't load existing files and it returns the paths of the dummy files relative
         # to the dummy_data zip file root
         __snake_case				=							load_existing_dummy_data

         # TODO(PVP, QL) might need to make this more general
         __snake_case				=							str(UpperCAmelCase__    )
         # to be downloaded
         __snake_case				=							None
         __snake_case				=							None
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[int]:
         if self._dummy_file is None:
              __snake_case				=							self.download_dummy_data()
         return self._dummy_file
    @property
    def  lowercase					(		self							:     Tuple    )    ->       List[str]:
         if self.config is not None:
              # structure is dummy / config_name / version_name
              return os.path.join('''dummy'''	,      self.config.name	,      self.version_name    )
         # structure is dummy / version_name
         return os.path.join('''dummy'''	,      self.version_name    )
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Optional[Any]:
         return os.path.join(self.dummy_data_folder	,      '''dummy_data.zip'''    )
    def  lowercase					(		self							:     Any    )    ->       Optional[Any]:
         __snake_case				=							(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         __snake_case				=							cached_path(
             UpperCAmelCase__	,      cache_dir=self.cache_dir	,      extract_compressed_file=UpperCAmelCase__	,      force_extract=UpperCAmelCase__    )

         return os.path.join(UpperCAmelCase__	,      self.dummy_file_name    )
    @property
    def  lowercase					(		self							:     List[str]    )    ->       str:
         return os.path.join(self.datasets_scripts_dir	,      self.dataset_name	,      self.dummy_zip_file    )
    @property
    def  lowercase					(		self							:     int    )    ->       str:
         if self._bucket_url is None:
              __snake_case				=							hf_github_url(self.dataset_name	,      self.dummy_zip_file.replace(os.sep	,      '''/'''    )    )
         return self._bucket_url
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       str:
         # return full path if its a dir
         if os.path.isdir(self.dummy_file    ):
              return self.dummy_file
         # else cut off path to file -> example `xsum`.
         return "/".join(self.dummy_file.replace(os.sep	,      '''/'''    ).split('''/'''    )[:-1]    )
    def  lowercase					(		self							:     str	,      A_							:     Optional[int]	,      *A_							:     Any    )    ->       str:
         if self.load_existing_dummy_data:
              # dummy data is downloaded and tested
              __snake_case				=							self.dummy_file
         else:
              # dummy data cannot be downloaded and only the path to dummy file is returned
              __snake_case				=							self.dummy_file_name

         # special case when data_url is a dict
         if isinstance(UpperCAmelCase__	,      UpperCAmelCase__    ):
              return self.create_dummy_data_dict(UpperCAmelCase__	,      UpperCAmelCase__    )
         elif isinstance(UpperCAmelCase__	,      (list, tuple)    ):
              return self.create_dummy_data_list(UpperCAmelCase__	,      UpperCAmelCase__    )
         else:
              return self.create_dummy_data_single(UpperCAmelCase__	,      UpperCAmelCase__    )
    def  lowercase					(		self							:     Dict	,      A_							:     List[Any]	,      *A_							:     Optional[int]    )    ->       Dict:
         return self.download_and_extract(UpperCAmelCase__    )
    def  lowercase					(		self							:     Any	,      A_							:     int	,      A_							:     Optional[Any]    )    ->       Union[str, Any]:
         return self.download_and_extract(UpperCAmelCase__    )
    def  lowercase					(		self							:     int	,      A_							:     Optional[Any]	,      *A_							:     Dict	,      **A_							:     Any    )    ->       Optional[Any]:
         return path
    def  lowercase					(		self							:     List[str]    )    ->       List[Any]:
         return {}
    def  lowercase					(		self							:     List[str]	,      A_							:     int	,      A_							:     str    )    ->       Dict:
         __snake_case				=							{}
         for key, single_urls in data_url.items():
              for download_callback in self.download_callbacks:
                   if isinstance(UpperCAmelCase__	,      UpperCAmelCase__    ):
                        for single_url in single_urls:
                             download_callback(UpperCAmelCase__    )
                   else:
                        __snake_case				=							single_urls
                        download_callback(UpperCAmelCase__    )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
              if isinstance(UpperCAmelCase__	,      UpperCAmelCase__    ):
                   __snake_case				=							[os.path.join(UpperCAmelCase__	,      urllib.parse.quote_plus(Path(UpperCAmelCase__    ).name    )    ) for x in single_urls]
              else:
                   __snake_case				=							single_urls
                   __snake_case				=							os.path.join(UpperCAmelCase__	,      urllib.parse.quote_plus(Path(UpperCAmelCase__    ).name    )    )
              __snake_case				=							value

         # make sure that values are unique
         if all(isinstance(UpperCAmelCase__	,      UpperCAmelCase__    ) for i in dummy_data_dict.values()    ) and len(set(dummy_data_dict.values()    )    ) < len(
             dummy_data_dict.values()    ):
              # append key to value to make its name unique
              __snake_case				=							{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Optional[Any]	,      A_							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							[]
         # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
         __snake_case				=							all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''	,      UpperCAmelCase__    )    ) for url in data_url    )
         __snake_case				=							all(
             url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''    ) for url in data_url    )
         if data_url and (is_tf_records or is_pubmed_records):
              __snake_case				=							[data_url[0]] * len(UpperCAmelCase__    )
         for single_url in data_url:
              for download_callback in self.download_callbacks:
                   download_callback(UpperCAmelCase__    )
              # we force the name of each key to be the last file / folder name of the url path
              # if the url has arguments, we need to encode them with urllib.parse.quote_plus
              __snake_case				=							os.path.join(UpperCAmelCase__	,      urllib.parse.quote_plus(single_url.split('''/'''    )[-1]    )    )
              dummy_data_list.append(UpperCAmelCase__    )
         return dummy_data_list
    def  lowercase					(		self							:     Dict	,      A_							:     Optional[int]	,      A_							:     List[str]    )    ->       Tuple:
         for download_callback in self.download_callbacks:
              download_callback(UpperCAmelCase__    )
         # we force the name of each key to be the last file / folder name of the url path
         # if the url has arguments, we need to encode them with urllib.parse.quote_plus
         __snake_case				=							os.path.join(UpperCAmelCase__	,      urllib.parse.quote_plus(data_url.split('''/'''    )[-1]    )    )
         if os.path.exists(UpperCAmelCase__    ) or not self.load_existing_dummy_data:
              return value
         else:
              # Backward compatibility, maybe deprecate at one point.
              # For many datasets with single url calls to dl_manager.download_and_extract,
              # the dummy_data.zip file is actually the zipped downloaded file
              # while now we expected the dummy_data.zip file to be a directory containing
              # the downloaded file.
              return path_to_dummy_data
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         pass
    def  lowercase					(		self							:     str    )    ->       Tuple:
         pass
    def  lowercase					(		self							:     Dict	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         def _iter_archive_members(A_							:     Optional[Any]    ):
              # this preserves the order of the members inside the ZIP archive
              __snake_case				=							Path(self.dummy_file    ).parent
              __snake_case				=							path.relative_to(UpperCAmelCase__    )
              with ZipFile(self.local_path_to_dummy_data    ) as zip_file:
                   __snake_case				=							zip_file.namelist()
              for member in members:
                   if member.startswith(relative_path.as_posix()    ):
                        yield dummy_parent_path.joinpath(UpperCAmelCase__    )

         __snake_case				=							Path(UpperCAmelCase__    )
         __snake_case				=							_iter_archive_members(UpperCAmelCase__    ) if self.use_local_dummy_data else path.rglob('''*'''    )
         for file_path in file_paths:
              if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')    ):
                   yield file_path.relative_to(UpperCAmelCase__    ).as_posix(), file_path.open('''rb'''    )







    def  lowercase					(		self							:     Any	,      A_							:     Dict    )    ->       List[Any]:
         if not isinstance(UpperCAmelCase__	,      UpperCAmelCase__    ):
              __snake_case				=							[paths]
         for path in paths:
              if os.path.isfile(UpperCAmelCase__    ):
                   if os.path.basename(UpperCAmelCase__    ).startswith(('''.''', '''__''')    ):
                        return
                   yield path
              else:
                   for dirpath, dirnames, filenames in os.walk(UpperCAmelCase__    ):
                        if os.path.basename(UpperCAmelCase__    ).startswith(('''.''', '''__''')    ):
                             continue
                        dirnames.sort()
                        for filename in sorted(UpperCAmelCase__    ):
                             if filename.startswith(('''.''', '''__''')    ):
                                  continue
                             yield os.path.join(UpperCAmelCase__	,      UpperCAmelCase__    )
"""simple docstring"""



import inspect
import unittest

import numpy as np

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

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


if is_flax_available():
       import jax

       from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     List[str]	,      A_							:     List[Any]=100	,      A_							:     Any=13	,      A_							:     Dict=30	,      A_							:     Optional[int]=2	,      A_							:     str=3	,      A_							:     Tuple=True	,      A_							:     str=True	,      A_							:     Union[str, Any]=32	,      A_							:     int=5	,      A_							:     List[Any]=4	,      A_							:     Optional[Any]=37	,      A_							:     Any="gelu"	,      A_							:     List[str]=0.1	,      A_							:     int=0.1	,      A_							:     Tuple=10	,      A_							:     int=0.02	,      A_							:     Tuple=3	,      )    ->       Optional[int]:
         __snake_case				=							parent
         __snake_case				=							vocab_size
         __snake_case				=							batch_size
         __snake_case				=							image_size
         __snake_case				=							patch_size
         __snake_case				=							num_channels
         __snake_case				=							is_training
         __snake_case				=							use_labels
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __snake_case				=							(image_size // patch_size) ** 2
         __snake_case				=							num_patches + 1
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         __snake_case				=							None
         if self.use_labels:
              __snake_case				=							ids_tensor([self.batch_size]	,      self.type_sequence_label_size    )

         __snake_case				=							BeitConfig(
             vocab_size=self.vocab_size	,      image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      is_decoder=A_	,      initializer_range=self.initializer_range	,      )

         return config, pixel_values, labels
    def  lowercase					(		self							:     Optional[int]	,      A_							:     str	,      A_							:     str	,      A_							:     List[str]    )    ->       List[Any]:
         __snake_case				=							FlaxBeitModel(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any	,      A_							:     Any	,      A_							:     str    )    ->       str:
         __snake_case				=							FlaxBeitForMaskedImageModeling(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length - 1, self.vocab_size)    )
    def  lowercase					(		self							:     str	,      A_							:     List[Any]	,      A_							:     Optional[int]	,      A_							:     List[str]    )    ->       str:
         __snake_case				=							self.type_sequence_label_size
         __snake_case				=							FlaxBeitForImageClassification(config=A_    )
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __snake_case				=							1
         __snake_case				=							FlaxBeitForImageClassification(A_    )

         __snake_case				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         __snake_case				=							model(A_    )







    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )		,	(
             __snake_case
         )		,	(
             __snake_case
         )		,	
         )				=							config_and_inputs
         __snake_case				=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )
    def  lowercase					(		self							:     List[Any]    )    ->       None:
         __snake_case				=							FlaxBeitModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      has_text_modality=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     Tuple    )    ->       Union[str, Any]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

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

              __snake_case				=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case		,	__snake_case				=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__    ):
                   __snake_case				=							self._prepare_for_class(A_	,      A_    )
                   __snake_case				=							model_class(A_    )

                   @jax.jit
                   def model_jitted(A_							:     Union[str, Any]	,      **A_							:     Union[str, Any]    ):
                        return model(pixel_values=A_	,      **A_    )

                   with self.subTest('''JIT Enabled'''    ):
                        __snake_case				=							model_jitted(**A_    ).to_tuple()

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

                   self.assertEqual(len(A_    )	,      len(A_    )    )
                   for jitted_output, output in zip(A_	,      A_    ):
                        self.assertEqual(jitted_output.shape	,      output.shape    )
    def  lowercase					(		self							:     str    )    ->       List[Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Union[str, Any]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         for model_class_name in self.all_model_classes:
              __snake_case				=							model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''    )
              __snake_case				=							model(np.ones((1, 3, 224, 224)    )    )
              self.assertIsNotNone(A_    )






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image




@require_vision
@require_flax
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @cached_property
    def  lowercase					(		self							:     str    )    ->       str:
         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''    ) if is_vision_available() else None
    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    ).pixel_values

         # prepare bool_masked_pos
         __snake_case				=							np.ones((1, 196)	,      dtype=A_    )

         # forward pass
         __snake_case				=							model(pixel_values=A_	,      bool_masked_pos=A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 196, 8_192)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]    )

         self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]	,      A_	,      atol=1E-2    )    )
    @slow
    def  lowercase					(		self							:     List[Any]    )    ->       List[str]:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 1_000)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([-1.23_85, -1.09_87, -1.01_08]    )

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

         __snake_case				=							281
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )







    @slow
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''    )

         __snake_case				=							self.default_image_processor
         __snake_case				=							prepare_img()
         __snake_case				=							image_processor(images=A_	,      return_tensors='''np'''    )

         # forward pass
         __snake_case				=							model(**A_    )
         __snake_case				=							outputs.logits

         # verify the logits
         __snake_case				=							(1, 21_841)
         self.assertEqual(logits.shape	,      A_    )

         __snake_case				=							np.array([1.68_81, -0.27_87, 0.59_01]    )

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

         __snake_case				=							2_396
         self.assertEqual(logits.argmax(-1    ).item()	,      A_    )