




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
      )


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   int	,  A__  :   Dict=1_3	,  A__  :   Dict=3_2	,  A__  :   Optional[Any]=2	,  A__  :   List[str]=3	,  A__  :   str=1_6	,  A__  :   List[Any]=[1, 2, 1]	,  A__  :   Optional[Any]=[2, 2, 4]	,  A__  :   Union[str, Any]=2	,  A__  :   List[Any]=2.0	,  A__  :   Optional[int]=True	,  A__  :   Tuple=0.0	,  A__  :   str=0.0	,  A__  :   Optional[Any]=0.1	,  A__  :   str="gelu"	,  A__  :   Optional[int]=False	,  A__  :   List[str]=True	,  A__  :   Union[str, Any]=0.02	,  A__  :   str=1E-5	,  A__  :   Any=True	,  A__  :   Tuple=None	,  A__  :   Optional[Any]=True	,  A__  :   Tuple=1_0	,  A__  :   Tuple=8	,  )				->       None:
           lowerCamelCase__        =				parent
           lowerCamelCase__        =				batch_size
           lowerCamelCase__        =				image_size
           lowerCamelCase__        =				patch_size
           lowerCamelCase__        =				num_channels
           lowerCamelCase__        =				embed_dim
           lowerCamelCase__        =				depths
           lowerCamelCase__        =				num_heads
           lowerCamelCase__        =				window_size
           lowerCamelCase__        =				mlp_ratio
           lowerCamelCase__        =				qkv_bias
           lowerCamelCase__        =				hidden_dropout_prob
           lowerCamelCase__        =				attention_probs_dropout_prob
           lowerCamelCase__        =				drop_path_rate
           lowerCamelCase__        =				hidden_act
           lowerCamelCase__        =				use_absolute_embeddings
           lowerCamelCase__        =				patch_norm
           lowerCamelCase__        =				layer_norm_eps
           lowerCamelCase__        =				initializer_range
           lowerCamelCase__        =				is_training
           lowerCamelCase__        =				scope
           lowerCamelCase__        =				use_labels
           lowerCamelCase__        =				type_sequence_label_size
           lowerCamelCase__        =				encoder_stride




     def  __snake_case   (    self  :   List[str]			)				->       int:
           lowerCamelCase__        =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

           lowerCamelCase__        =				self.get_config()

           return config, pixel_values, labels




     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           return SwinConfig(
               image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  embed_dim=self.embed_dim	,  depths=self.depths	,  num_heads=self.num_heads	,  window_size=self.window_size	,  mlp_ratio=self.mlp_ratio	,  qkv_bias=self.qkv_bias	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  drop_path_rate=self.drop_path_rate	,  hidden_act=self.hidden_act	,  use_absolute_embeddings=self.use_absolute_embeddings	,  path_norm=self.patch_norm	,  layer_norm_eps=self.layer_norm_eps	,  initializer_range=self.initializer_range	,  encoder_stride=self.encoder_stride	,  )




     def  __snake_case   (    self  :   int	,  A__  :   Dict	,  A__  :   Optional[Any]	,  A__  :   List[str]			)				->       Optional[int]:
           lowerCamelCase__        =				TFSwinModel(config=A__			)
           lowerCamelCase__        =				model(A__			)

           lowerCamelCase__        =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
           lowerCamelCase__        =				int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

           self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, expected_seq_len, expected_dim)			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   Union[str, Any]	,  A__  :   str	,  A__  :   Union[str, Any]			)				->       Dict:
           lowerCamelCase__        =				TFSwinForMaskedImageModeling(config=A__			)
           lowerCamelCase__        =				model(A__			)
           self.parent.assertEqual(
               result.logits.shape	,  (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

           # test greyscale images
           lowerCamelCase__        =				1
           lowerCamelCase__        =				TFSwinForMaskedImageModeling(A__			)

           lowerCamelCase__        =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           lowerCamelCase__        =				model(A__			)
           self.parent.assertEqual(result.logits.shape	,  (self.batch_size, 1, self.image_size, self.image_size)			)




     def  __snake_case   (    self  :   Optional[Any]	,  A__  :   Tuple	,  A__  :   List[str]	,  A__  :   List[Any]			)				->       int:
           lowerCamelCase__        =				self.type_sequence_label_size
           lowerCamelCase__        =				TFSwinForImageClassification(A__			)
           lowerCamelCase__        =				model(A__	,  labels=A__			)
           self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)			)

           # test greyscale images
           lowerCamelCase__        =				1
           lowerCamelCase__        =				TFSwinForImageClassification(A__			)
           lowerCamelCase__        =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           lowerCamelCase__        =				model(A__			)
           self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)			)




     def  __snake_case   (    self  :   Any			)				->       str:
           lowerCamelCase__        =				self.prepare_config_and_inputs()
           lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				config_and_inputs
           lowerCamelCase__        =				{'pixel_values': pixel_values}
           return config, inputs_dict






@require_tf
class 			A			(		_lowerCAmelCase			,						_lowerCAmelCase			,						unittest.TestCase		):
     """simple docstring"""

     _snake_case:	Union[str, Any]     =(
         (
             TFSwinModel,
             TFSwinForImageClassification,
             TFSwinForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     _snake_case:	int     =(
         {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
         if is_tf_available()
         else {}
     )

     _snake_case:	Optional[int]     =False
     _snake_case:	Any     =False
     _snake_case:	int     =False
     _snake_case:	int     =False




     def  __snake_case   (    self  :   Any			)				->       Optional[Any]:
           lowerCamelCase__        =				TFSwinModelTester(self			)
           lowerCamelCase__        =				ConfigTester(self	,  config_class=A__	,  embed_dim=3_7			)




     def  __snake_case   (    self  :   str			)				->       List[str]:
           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  __snake_case   (    self  :   Tuple			)				->       str:
           return




     def  __snake_case   (    self  :   List[str]			)				->       str:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__			)




     def  __snake_case   (    self  :   List[Any]			)				->       Union[str, Any]:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*A__			)




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Union[str, Any]:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A__			)




     @unittest.skip(reason='Swin does not use inputs_embeds'			)
     def  __snake_case   (    self  :   List[str]			)				->       List[Any]:
           pass




     def  __snake_case   (    self  :   List[Any]			)				->       int:
           lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__        =				model_class(A__			)
                 self.assertIsInstance(model.get_input_embeddings()	,  tf.keras.layers.Layer			)
                 lowerCamelCase__        =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(A__	,  tf.keras.layers.Dense			)			)




     def  __snake_case   (    self  :   List[Any]			)				->       List[Any]:
           lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs_for_common()

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

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




     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__        =				True

           for model_class in self.all_model_classes:
                 lowerCamelCase__        =				True
                 lowerCamelCase__        =				False
                 lowerCamelCase__        =				True
                 lowerCamelCase__        =				model_class(A__			)
                 lowerCamelCase__        =				model(**self._prepare_for_class(A__	,  A__			)			)
                 lowerCamelCase__        =				outputs.attentions
                 lowerCamelCase__        =				len(self.model_tester.depths			)
                 self.assertEqual(len(A__			)	,  A__			)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 lowerCamelCase__        =				True
                 lowerCamelCase__        =				config.window_size**2
                 lowerCamelCase__        =				model_class(A__			)
                 lowerCamelCase__        =				model(**self._prepare_for_class(A__	,  A__			)			)
                 lowerCamelCase__        =				outputs.attentions
                 self.assertEqual(len(A__			)	,  A__			)

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]			)	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )
                 lowerCamelCase__        =				len(A__			)

                 # Check attention is always last and order is fine
                 lowerCamelCase__        =				True
                 lowerCamelCase__        =				True
                 lowerCamelCase__        =				model_class(A__			)
                 lowerCamelCase__        =				model(**self._prepare_for_class(A__	,  A__			)			)

                 if hasattr(self.model_tester	,  'num_hidden_states_types'			):
                       lowerCamelCase__        =				self.model_tester.num_hidden_states_types
                 else:
                       # also another +1 for reshaped_hidden_states
                       lowerCamelCase__        =				2
                 self.assertEqual(out_len + added_hidden_states	,  len(A__			)			)

                 lowerCamelCase__        =				outputs.attentions

                 self.assertEqual(len(A__			)	,  A__			)

                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]			)	,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,  )




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   Tuple	,  A__  :   Union[str, Any]	,  A__  :   str	,  A__  :   str			)				->       List[Any]:
           lowerCamelCase__        =				model_class(A__			)
           lowerCamelCase__        =				model(**self._prepare_for_class(A__	,  A__			)			)
           lowerCamelCase__        =				outputs.hidden_states

           lowerCamelCase__        =				getattr(
               self.model_tester	,  'expected_num_hidden_layers'	,  len(self.model_tester.depths			) + 1			)
           self.assertEqual(len(A__			)	,  A__			)

           # Swin has a different seq_length
           lowerCamelCase__        =				to_atuple(config.patch_size			)

           lowerCamelCase__        =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]			)	,  [num_patches, self.model_tester.embed_dim]	,  )

           lowerCamelCase__        =				outputs.reshaped_hidden_states
           self.assertEqual(len(A__			)	,  A__			)

           lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				reshaped_hidden_states[0].shape

           lowerCamelCase__        =				tf.reshape(reshaped_hidden_states[0]	,  (batch_size, num_channels, height * width)			)
           lowerCamelCase__        =				tf.transpose(A__	,  (0, 2, 1)			)

           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]			)	,  [num_patches, self.model_tester.embed_dim]	,  )




     def  __snake_case   (    self  :   Any			)				->       Optional[int]:
           lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase__        =				to_atuple(self.model_tester.image_size			)

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

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

                 self.check_hidden_states_output(A__	,  A__	,  A__	,  A__			)




     def  __snake_case   (    self  :   List[Any]			)				->       str:
           lowerCamelCase__  ,		lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__        =				3

           lowerCamelCase__        =				to_atuple(self.model_tester.image_size			)
           lowerCamelCase__        =				to_atuple(config.patch_size			)

           lowerCamelCase__        =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           lowerCamelCase__        =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                 lowerCamelCase__        =				True
                 self.check_hidden_states_output(A__	,  A__	,  A__	,  (padded_height, padded_width)			)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase__        =				True
                 self.check_hidden_states_output(A__	,  A__	,  A__	,  (padded_height, padded_width)			)




     @slow
     def  __snake_case   (    self  :   Dict			)				->       Optional[int]:
           for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__        =				TFSwinModel.from_pretrained(A__			)
                 self.assertIsNotNone(A__			)






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

     @cached_property
     def  __snake_case   (    self  :   List[str]			)				->       Tuple:
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
               if is_vision_available()
               else None
           )




     @slow
     def  __snake_case   (    self  :   Optional[Any]			)				->       Union[str, Any]:
           lowerCamelCase__        =				TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
           lowerCamelCase__        =				self.default_image_processor

           lowerCamelCase__        =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
           lowerCamelCase__        =				image_processor(images=A__	,  return_tensors='tf'			)

           # forward pass
           lowerCamelCase__        =				model(A__			)

           # verify the logits
           lowerCamelCase__        =				tf.TensorShape((1, 1_0_0_0)			)
           self.assertEqual(outputs.logits.shape	,  A__			)
           lowerCamelCase__        =				tf.constant([-0.0948, -0.6454, -0.0921]			)
           self.assertTrue(np.allclose(outputs.logits[0, :3]	,  A__	,  atol=1E-4			)			)





import json
import os
import pickle
import subprocess
from hashlib import mda
from pathlib import Path
from tempfile import gettempdir
from textwrap import dedent
from types import CodeType, FunctionType
from unittest import TestCase
from unittest.mock import patch

import pytest
from multiprocess import Pool
from packaging import version

import datasets
from datasets.fingerprint import Hasher, fingerprint_transform
from datasets.table import InMemoryTable

from .utils import (
    require_regex,
    require_spacy,
    require_spacy_model,
    require_tiktoken,
    require_torch,
    require_transformers,
)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   Optional[int]	,  A__  :   Optional[Any]			)				->       Dict:
           lowerCamelCase__        =				foo




     def __call__(    self  :   Dict			)				->       Optional[int]:
           return self.foo






class 			A			(		datasets.Dataset		):
     """simple docstring"""

     @fingerprint_transform(inplace=A__			)
     def  __snake_case   (    self  :   Optional[Any]	,  A__  :   Tuple	,  *A__  :   List[str]	,  **A__  :   List[Any]			)				->       Dict:
           return DatasetChild(self.data	,  fingerprint=A__			)




     @fingerprint_transform(inplace=A__			)
     def  __snake_case   (    self  :   Any	,  A__  :   Optional[int]	,  *A__  :   List[str]	,  **A__  :   Union[str, Any]			)				->       List[Any]:
           return DatasetChild(self.data	,  fingerprint=A__			)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   Tuple			)				->       List[Any]:
           lowerCamelCase__        =				callable




     def __call__(    self  :   Dict	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       List[str]:
           if self.callable is not None:
                 return self.callable(*A__	,  **A__			)




     def __getstate__(    self  :   Optional[Any]			)				->       Union[str, Any]:
           raise pickle.PicklingError()






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     @require_transformers
     @pytest.mark.integration
     def  __snake_case   (    self  :   str			)				->       int:
           from transformers import AutoTokenizer

           def encode(A__  :   List[Any]			):
                 return tokenizer(A__			)

           # TODO: add hash consistency tests across sessions
           lowerCamelCase__        =				AutoTokenizer.from_pretrained('bert-base-uncased'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(lambda A__			: tokenizer(A__			)			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				AutoTokenizer.from_pretrained('bert-base-cased'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(lambda A__			: tokenizer(A__			)			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				AutoTokenizer.from_pretrained('bert-base-uncased'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(lambda A__			: tokenizer(A__			)			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     @require_transformers
     @pytest.mark.integration
     def  __snake_case   (    self  :   int			)				->       List[str]:
           from transformers import AutoTokenizer

           lowerCamelCase__        =				AutoTokenizer.from_pretrained('gpt2'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           tokenizer('Hello world !'			)  # call once to change the tokenizer's cache
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)




     @require_regex
     def  __snake_case   (    self  :   Dict			)				->       Dict:
           import regex

           lowerCamelCase__        =				regex.Regex('foo'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				regex.Regex('bar'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				regex.Regex('foo'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Tuple			)				->       List[Any]:
           def func():
                 return foo

           lowerCamelCase__        =				[0]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				[1]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				[0]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   Optional[int]			)				->       str:
           def func():
                 pass

           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()

           def func():
                 pass

           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)




     def  __snake_case   (    self  :   List[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(Foo([0]			)			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(Foo([1]			)			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(Foo([0]			)			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   Union[str, Any]			)				->       str:
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(Foo([0]			).__call__			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(Foo([1]			).__call__			)			).hexdigest()
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(Foo([0]			).__call__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				(
               'co_argcount',
               'co_kwonlyargcount',
               'co_nlocals',
               'co_stacksize',
               'co_flags',
               'co_code',
               'co_consts',
               'co_names',
               'co_varnames',
               'co_filename',
               'co_name',
               'co_firstlineno',
               'co_lnotab',
               'co_freevars',
               'co_cellvars',
           )

           def _create_code_pyaa(*A__  :   Any			):
                 if hasattr(args[-3]	,  'encode'			):
                       lowerCamelCase__        =				list(A__			)
                       lowerCamelCase__        =				args[-3].encode()  # co_lnotab
                       lowerCamelCase__        =				args[-1_0].encode()  # co_code
                 if hasattr(A__	,  'co_posonlyargcount'			):
                       if len(A__			) == 1_6:
                             return CodeType(*A__			)
                       elif len(A__			) == 1_5:
                             return CodeType(args[0]	,  0	,  *args[1:]			)
                       return CodeType(args[0]	,  0	,  0	,  *args[1:]			)
                 elif hasattr(A__	,  'co_kwonlyargcount'			):
                       if len(A__			) == 1_6:
                             return CodeType(args[0]	,  *args[2:]			)
                       elif len(A__			) == 1_5:
                             return CodeType(*A__			)
                       return CodeType(args[0]	,  0	,  *args[1:]			)
                 if len(A__			) == 1_6:
                       return CodeType(args[0]	,  *args[3:]			)
                 elif len(A__			) == 1_5:
                       return CodeType(args[0]	,  *args[2:]			)
                 return CodeType(*A__			)

           def create_ipython_func(A__  :   Any	,  A__  :   Tuple			):
                 def func():
                       return returned_obj

                 lowerCamelCase__        =				func.__code__
                 # Use _create_code from dill in order to make it work for different python versions
                 if datasets.config.PY_VERSION < version.parse('3.8'			):
                       lowerCamelCase__        =				_create_code_pyaa(
                           *[getattr(A__	,  A__			) if k != 'co_filename' else co_filename for k in code_args_pyaa]			)
                 else:
                       lowerCamelCase__        =				code.replace(co_filename=A__			)
                 return FunctionType(A__	,  func.__globals__	,  func.__name__	,  func.__defaults__	,  func.__closure__			)

           lowerCamelCase__  ,		lowerCamelCase__        =				'<ipython-input-2-e0383a102aae>', [0]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(create_ipython_func(A__	,  A__			)			)			).hexdigest()
           lowerCamelCase__  ,		lowerCamelCase__        =				'<ipython-input-2-e0383a102aae>', [1]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(create_ipython_func(A__	,  A__			)			)			).hexdigest()
           lowerCamelCase__  ,		lowerCamelCase__        =				'<ipython-input-5-713f6613acf3>', [0]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(create_ipython_func(A__	,  A__			)			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)

           lowerCamelCase__  ,		lowerCamelCase__        =				os.path.join(gettempdir()	,  'ipykernel_12345'	,  '321456789.py'			), [0]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(create_ipython_func(A__	,  A__			)			)			).hexdigest()
           lowerCamelCase__  ,		lowerCamelCase__        =				os.path.join(gettempdir()	,  'ipykernel_12345'	,  '321456789.py'			), [1]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(create_ipython_func(A__	,  A__			)			)			).hexdigest()
           lowerCamelCase__  ,		lowerCamelCase__        =				os.path.join(gettempdir()	,  'ipykernel_12345'	,  '654123987.py'			), [0]
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(create_ipython_func(A__	,  A__			)			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   Optional[Any]			)				->       List[Any]:
           lowerCamelCase__  ,		lowerCamelCase__        =				[0], [1]

           def func():
                 return foo, bar

           lowerCamelCase__        =				'__main__'

           def globalvars_mocka_side_effect(A__  :   Optional[Any]	,  *A__  :   str	,  **A__  :   Optional[int]			):
                 return {"foo": foo, "bar": bar}

           def globalvars_mocka_side_effect(A__  :   Any	,  *A__  :   Optional[int]	,  **A__  :   Tuple			):
                 return {"bar": bar, "foo": foo}

           with patch('dill.detect.globalvars'	,  side_effect=A__			) as globalvars_mocka:
                 lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
                 self.assertGreater(globalvars_mocka.call_count	,  0			)
           with patch('dill.detect.globalvars'	,  side_effect=A__			) as globalvars_mocka:
                 lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
                 self.assertGreater(globalvars_mocka.call_count	,  0			)
           self.assertEqual(A__	,  A__			)






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   str			)				->       List[str]:
           lowerCamelCase__        =				Hasher.hash('hello'			)
           lowerCamelCase__        =				Hasher.hash('hello'			)
           lowerCamelCase__        =				Hasher.hash('there'			)
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   List[str]			)				->       Optional[Any]:
           lowerCamelCase__        =				Hasher.hash(Foo('hello'			)			)
           lowerCamelCase__        =				Hasher.hash(Foo('hello'			)			)
           lowerCamelCase__        =				Hasher.hash(Foo('there'			)			)
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   Tuple			)				->       str:
           lowerCamelCase__        =				Hasher()
           for x in ["hello", Foo('hello'			)]:
                 hasher.update(A__			)
           lowerCamelCase__        =				hasher.hexdigest()
           lowerCamelCase__        =				Hasher()
           for x in ["hello", Foo('hello'			)]:
                 hasher.update(A__			)
           lowerCamelCase__        =				hasher.hexdigest()
           lowerCamelCase__        =				Hasher()
           for x in ["there", Foo('there'			)]:
                 hasher.update(A__			)
           lowerCamelCase__        =				hasher.hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     def  __snake_case   (    self  :   Any			)				->       Tuple:
           with self.assertRaises(pickle.PicklingError			):
                 Hasher.hash(UnpicklableCallable(Foo('hello'			)			)			)




     def  __snake_case   (    self  :   Optional[int]			)				->       Optional[Any]:
           lowerCamelCase__        =				'abc'
           lowerCamelCase__        =				[string, string]  # two strings have the same ids
           lowerCamelCase__        =				[string, string]
           lowerCamelCase__        =				json.loads(F"""[\"{string}\", \"{string}\"]"""			)  # two strings have different ids
           self.assertIs(obja[0]	,  A__			)
           self.assertIs(obja[0]	,  obja[1]			)
           self.assertIs(obja[0]	,  A__			)
           self.assertIs(obja[0]	,  obja[1]			)
           self.assertIsNot(obja[0]	,  A__			)
           self.assertIsNot(obja[0]	,  obja[1]			)
           lowerCamelCase__        =				Hasher.hash(A__			)
           lowerCamelCase__        =				Hasher.hash(A__			)
           lowerCamelCase__        =				Hasher.hash(A__			)
           self.assertEqual(A__	,  A__			)
           self.assertEqual(A__	,  A__			)




     @require_tiktoken
     def  __snake_case   (    self  :   Union[str, Any]			)				->       str:
           import tiktoken

           lowerCamelCase__        =				tiktoken.get_encoding('gpt2'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				tiktoken.get_encoding('r50k_base'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				tiktoken.get_encoding('gpt2'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     @require_torch
     def  __snake_case   (    self  :   Optional[Any]			)				->       List[Any]:
           import torch

           lowerCamelCase__        =				torch.tensor([1.0]			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				torch.tensor([2.0]			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				torch.tensor([1.0]			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)




     @require_spacy
     @require_spacy_model('en_core_web_sm'			)
     @require_spacy_model('fr_core_news_sm'			)
     @pytest.mark.integration
     def  __snake_case   (    self  :   Tuple			)				->       List[str]:
           import spacy

           lowerCamelCase__        =				spacy.load('en_core_web_sm'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				spacy.load('fr_core_news_sm'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           lowerCamelCase__        =				spacy.load('en_core_web_sm'			)
           lowerCamelCase__        =				mda(datasets.utils.py_utils.dumps(A__			)			).hexdigest()
           self.assertEqual(A__	,  A__			)
           self.assertNotEqual(A__	,  A__			)

@pytest.mark.integration
def 			lowerCAmelCase_			(  lowerCamelCase:       Path						)					->			Optional[int]:


      '''simple docstring'''




      lowerCamelCase__        =				tmp_path / 'dir1'
      lowerCamelCase__        =				tmp_path / 'dir2'
      dira.mkdir()
      dira.mkdir()
      lowerCamelCase__        =				'script.py'
      lowerCamelCase__        =				dedent(
          '\n    from datasets.fingerprint import Hasher\n    def foo():\n        pass\n    print(Hasher.hash(foo))\n    '						)
      lowerCamelCase__        =				dira / script_filename
      lowerCamelCase__        =				dira / script_filename
      with script_patha.open('w'						) as f:
            f.write(lowerCamelCase						)
      with script_patha.open('w'						) as f:
            f.write(lowerCamelCase						)
      lowerCamelCase__        =				subprocess.check_output(['python', str(lowerCamelCase						)]						)
      lowerCamelCase__        =				subprocess.check_output(['python', str(lowerCamelCase						)]						)
      assert fingerprinta == fingerprinta

def 			lowerCAmelCase_			(  )					->			int:


      '''simple docstring'''




      lowerCamelCase__        =				{'a': [0, 1, 2]}
      lowerCamelCase__        =				DatasetChild(InMemoryTable.from_pydict(lowerCamelCase						)						)
      lowerCamelCase__        =				dataset.funca()._fingerprint
      assert expected_fingerprint == dataset.funca()._fingerprint
      assert expected_fingerprint != dataset.funca()._fingerprint

      with Pool(2						) as p:
            assert expected_fingerprint == p.apply_async(dataset.funca						).get()._fingerprint
            assert expected_fingerprint != p.apply_async(dataset.funca						).get()._fingerprint

def 			lowerCAmelCase_			(  )					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				{'a': [0, 1, 2]}






      class 			A			(		datasets.Dataset		):
           """simple docstring"""

           @fingerprint_transform(inplace=A__			)
           def  __snake_case   (    self  :   Any	,  A__  :   Union[str, Any]			)				->       List[Any]:
                 return DummyDatasetChild(self.data	,  fingerprint=A__			)
      lowerCamelCase__        =				DummyDatasetChild(InMemoryTable.from_pydict(lowerCamelCase						)						).func()






      class 			A			(		datasets.Dataset		):
           """simple docstring"""

           @fingerprint_transform(inplace=A__	,  version='1.0.0'			)
           def  __snake_case   (    self  :   str	,  A__  :   List[Any]			)				->       List[str]:
                 return DummyDatasetChild(self.data	,  fingerprint=A__			)
      lowerCamelCase__        =				DummyDatasetChild(InMemoryTable.from_pydict(lowerCamelCase						)						).func()






      class 			A			(		datasets.Dataset		):
           """simple docstring"""

           @fingerprint_transform(inplace=A__	,  version='2.0.0'			)
           def  __snake_case   (    self  :   List[str]	,  A__  :   int			)				->       int:
                 return DummyDatasetChild(self.data	,  fingerprint=A__			)
      lowerCamelCase__        =				DummyDatasetChild(InMemoryTable.from_pydict(lowerCamelCase						)						).func()

      assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}						) == 3


def 			lowerCAmelCase_			(  )					->			int:


      '''simple docstring'''




      lowerCamelCase__        =				Hasher()
      hasher.update(lambda lowerCamelCase						: x						)