


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=3_0     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=2     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   __snake_case=3     ,   __snake_case=0.6     ,   __snake_case=None     ,   ):
         snake_case											=							parent
         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
         snake_case											=							mask_ratio
         snake_case											=							scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         snake_case											=							(image_size // patch_size) ** 2
         snake_case											=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )
    def 						a_     (			self       ):
         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											=							self.get_config()

         return config, pixel_values, labels
    def 						a_     (			self       ):
         return ViTMAEConfig(
             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     ,   decoder_hidden_size=self.hidden_size     ,   decoder_num_hidden_layers=self.num_hidden_layers     ,   decoder_num_attention_heads=self.num_attention_heads     ,   decoder_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=lowerCAmelCase__     ,   initializer_range=self.initializer_range     ,   mask_ratio=self.mask_ratio     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFViTMAEModel(config=lowerCAmelCase__       )
         snake_case											=							model(lowerCAmelCase__     ,   training=lowerCAmelCase__       )
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFViTMAEForPreTraining(lowerCAmelCase__       )
         snake_case											=							model(lowerCAmelCase__     ,   training=lowerCAmelCase__       )
         # expected sequence length = num_patches
         snake_case											=							(self.image_size // self.patch_size) ** 2
         snake_case											=							self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, num_patches, expected_num_channels)       )

         # test greyscale images
         snake_case											=							1
         snake_case											=							TFViTMAEForPreTraining(lowerCAmelCase__       )

         snake_case											=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case											=							model(lowerCAmelCase__     ,   training=lowerCAmelCase__       )
         snake_case											=							self.patch_size**2
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, num_patches, expected_num_channels)       )




    def 						a_     (			self       ):
         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_tf
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    __magic_name__         =						{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self       ):
         snake_case											=							TFViTMAEModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=lowerCAmelCase__     ,   has_text_modality=lowerCAmelCase__     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         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(lowerCAmelCase__       )
              self.assertIsInstance(model.get_input_embeddings()     ,   (tf.keras.layers.Layer)       )
              snake_case											=							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCAmelCase__     ,   tf.keras.layers.Layer       )       )
    def 						a_     (			self       ):
         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(lowerCAmelCase__       )
              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]     ,   lowerCAmelCase__       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase__       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__       )
    def 						a_     (			self       ):
         np.random.seed(2       )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							int((config.image_size // config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         for model_class in self.all_model_classes:
              snake_case											=							model_class(lowerCAmelCase__       )
              snake_case											=							self._prepare_for_class(lowerCAmelCase__     ,   lowerCAmelCase__       )

              snake_case											=							model(lowerCAmelCase__     ,   noise=lowerCAmelCase__       )

              snake_case											=							copy.deepcopy(self._prepare_for_class(lowerCAmelCase__     ,   lowerCAmelCase__       )       )
              snake_case											=							model(**lowerCAmelCase__     ,   noise=lowerCAmelCase__       )
              snake_case											=							outputs_dict[0].numpy()
              snake_case											=							outputs_keywords[0].numpy()

              self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       )     ,   1E-6       )
    def 						a_     (			self       ):
         np.random.seed(2       )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							int((config.image_size // config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         def prepare_numpy_arrays(__snake_case       ):
              snake_case											=							{}
              for k, v in inputs_dict.items():
                   if tf.is_tensor(lowerCAmelCase__       ):
                        snake_case											=							v.numpy()
                   else:
                        snake_case											=							np.array(lowerCAmelCase__       )

              return inputs_np_dict

         for model_class in self.all_model_classes:
              snake_case											=							model_class(lowerCAmelCase__       )

              snake_case											=							self._prepare_for_class(lowerCAmelCase__     ,   lowerCAmelCase__       )
              snake_case											=							prepare_numpy_arrays(lowerCAmelCase__       )

              snake_case											=							model(lowerCAmelCase__     ,   noise=lowerCAmelCase__       )
              snake_case											=							model(**lowerCAmelCase__     ,   noise=lowerCAmelCase__       )
              self.assert_outputs_same(lowerCAmelCase__     ,   lowerCAmelCase__       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         np.random.seed(2       )

         snake_case											=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
         snake_case											=							tf.constant(lowerCAmelCase__       )

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         snake_case											=							tf_noise

         super().check_pt_tf_models(lowerCAmelCase__     ,   lowerCAmelCase__     ,   lowerCAmelCase__       )
    def 						a_     (			self       ):
         np.random.seed(2       )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							{
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__       ),)
             for module_member_name in dir(lowerCAmelCase__       )
             if module_member_name.endswith('''MainLayer'''       )
             # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
             and module_member_name[: -len('''MainLayer'''       )] == model_class.__name__[: -len('''Model'''       )]
             for module_member in (getattr(lowerCAmelCase__     ,   lowerCAmelCase__       ),)
             if isinstance(lowerCAmelCase__     ,   lowerCAmelCase__       )
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(lowerCAmelCase__     ,   '''_keras_serializable'''     ,   lowerCAmelCase__       )
         }

         snake_case											=							int((config.image_size // config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
         snake_case											=							tf.convert_to_tensor(lowerCAmelCase__       )
         inputs_dict.update({'''noise''': noise}       )

         for main_layer_class in tf_main_layer_classes:
              snake_case											=							main_layer_class(lowerCAmelCase__       )

              snake_case											=							{
                  name: tf.keras.Input(tensor.shape[1:]     ,   dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
              }

              snake_case											=							tf.keras.Model(lowerCAmelCase__     ,   outputs=main_layer(lowerCAmelCase__       )       )
              snake_case											=							model(lowerCAmelCase__       )

              with tempfile.TemporaryDirectory() as tmpdirname:
                   snake_case											=							os.path.join(lowerCAmelCase__     ,   '''keras_model.h5'''       )
                   model.save(lowerCAmelCase__       )
                   snake_case											=							tf.keras.models.load_model(
                       lowerCAmelCase__     ,   custom_objects={main_layer_class.__name__: main_layer_class}       )
                   assert isinstance(lowerCAmelCase__     ,   tf.keras.Model       )
                   snake_case											=							model(lowerCAmelCase__       )
                   self.assert_outputs_same(lowerCAmelCase__     ,   lowerCAmelCase__       )
    @slow
    def 						a_     (			self       ):
         np.random.seed(2       )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							int((config.image_size // config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         for model_class in self.all_model_classes:
              snake_case											=							model_class(lowerCAmelCase__       )
              snake_case											=							self._prepare_for_class(lowerCAmelCase__     ,   lowerCAmelCase__       )
              snake_case											=							model(lowerCAmelCase__     ,   noise=lowerCAmelCase__       )

              if model_class.__name__ == "TFViTMAEModel":
                   snake_case											=							outputs.last_hidden_state.numpy()
                   snake_case											=							0
              else:
                   snake_case											=							outputs.logits.numpy()
                   snake_case											=							0

              with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(lowerCAmelCase__     ,   saved_model=lowerCAmelCase__       )
                   snake_case											=							model_class.from_pretrained(lowerCAmelCase__       )
                   snake_case											=							model(lowerCAmelCase__     ,   noise=lowerCAmelCase__       )

                   if model_class.__name__ == "TFViTMAEModel":
                        snake_case											=							after_outputs['''last_hidden_state'''].numpy()
                        snake_case											=							0
                   else:
                        snake_case											=							after_outputs['''logits'''].numpy()
                        snake_case											=							0

                   snake_case											=							np.amax(np.abs(out_a - out_a       )       )
                   self.assertLessEqual(lowerCAmelCase__     ,   1E-5       )
    def 						a_     (			self       ):
         np.random.seed(2       )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         snake_case											=							int((config.image_size // config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         for model_class in self.all_model_classes:
              snake_case											=							model_class(lowerCAmelCase__       )
              snake_case											=							self._prepare_for_class(lowerCAmelCase__     ,   lowerCAmelCase__       )

              snake_case											=							model(lowerCAmelCase__     ,   noise=lowerCAmelCase__       )
              snake_case											=							model.get_config()
              # make sure that returned config is jsonifiable, which is required by keras
              json.dumps(lowerCAmelCase__       )
              snake_case											=							model_class.from_config(model.get_config()       )
              # make sure it also accepts a normal config
              snake_case											=							model_class.from_config(model.config       )
              snake_case											=							new_model(lowerCAmelCase__       )  # Build model
              new_model.set_weights(model.get_weights()       )
              snake_case											=							new_model(lowerCAmelCase__     ,   noise=lowerCAmelCase__       )

              self.assert_outputs_same(lowerCAmelCase__     ,   lowerCAmelCase__       )
    @unittest.skip(
        reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''       )
    def 						a_     (			self       ):
         pass
    @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''       )
    def 						a_     (			self       ):
         pass




    @slow
    def 						a_     (			self       ):
         snake_case											=							TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''       )
         self.assertIsNotNone(lowerCAmelCase__       )






def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image

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







    """simple docstring"""

    @cached_property
    def 						a_     (			self       ):
         return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''       ) if is_vision_available() else None




    @slow
    def 						a_     (			self       ):
         np.random.seed(2       )

         snake_case											=							TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''       )

         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(images=lowerCAmelCase__     ,   return_tensors='''tf'''       )

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         snake_case											=							ViTMAEConfig()
         snake_case											=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
         snake_case											=							np.random.uniform(size=(1, num_patches)       )

         # forward pass
         snake_case											=							model(**lowerCAmelCase__     ,   noise=lowerCAmelCase__       )

         # verify the logits
         snake_case											=							tf.convert_to_tensor([1, 1_9_6, 7_6_8]       )
         self.assertEqual(outputs.logits.shape     ,   lowerCAmelCase__       )

         snake_case											=							tf.convert_to_tensor(
             [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

         tf.debugging.assert_near(outputs.logits[0, :3, :3]     ,   lowerCAmelCase__     ,   atol=1E-4       )











'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def    a__							(							lowercase   :	Tuple,   lowercase   :	List[str],   lowercase   :	Optional[int],   lowercase   :	List[str],   lowercase   :	List[str]=True,   lowercase   :	str="pt" )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			{'''add_prefix_space''': True} if isinstance(lowercase,   lowercase ) and not line.startswith(''' ''' ) else {}
     _UpperCamelCase							       =			padding_side
     return tokenizer(
         [line],   max_length=lowercase,   padding='''max_length''' if pad_to_max_length else None,   truncation=lowercase,   return_tensors=lowercase,   add_special_tokens=lowercase,   **lowercase,   )







def    a__							(							lowercase   :	str,   lowercase   :	int,   lowercase   :	Tuple=None,   )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			input_ids.ne(lowercase ).any(dim=0 )
     if attention_mask is None:
          return input_ids[:, keep_column_mask]
     else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[int]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						str="train"			,					lowerCAmelCase__  :						List[str]=None			,					lowerCAmelCase__  :						Dict=None			,					lowerCAmelCase__  :						Optional[Any]=None			,					lowerCAmelCase__  :						Dict=""			,					)       ->	Any:

            '''simple docstring'''




            super().__init__()
            _UpperCamelCase							       =			Path(lowerCAmelCase__	).joinpath(type_path + '''.source'''	)
            _UpperCamelCase							       =			Path(lowerCAmelCase__	).joinpath(type_path + '''.target'''	)
            _UpperCamelCase							       =			self.get_char_lens(self.src_file	)
            _UpperCamelCase							       =			max_source_length
            _UpperCamelCase							       =			max_target_length
            assert min(self.src_lens	) > 0, f"""found empty line in {self.src_file}"""
            _UpperCamelCase							       =			tokenizer
            _UpperCamelCase							       =			prefix
            if n_obs is not None:
                 _UpperCamelCase							       =			self.src_lens[:n_obs]
            _UpperCamelCase							       =			src_lang
            _UpperCamelCase							       =			tgt_lang





       def __len__(				self  :						Optional[Any]	)       ->	List[Any]:

            '''simple docstring'''




            return len(self.src_lens	)





       def __getitem__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Any	)       ->	Dict[str, torch.Tensor]:

            '''simple docstring'''




            _UpperCamelCase							       =			index + 1  # linecache starts at 1
            _UpperCamelCase							       =			self.prefix + linecache.getline(str(self.src_file	)			,					lowerCAmelCase__	).rstrip('''\n'''	)
            _UpperCamelCase							       =			linecache.getline(str(self.tgt_file	)			,					lowerCAmelCase__	).rstrip('''\n'''	)
            assert source_line, f"""empty source line for index {index}"""
            assert tgt_line, f"""empty tgt line for index {index}"""

            # Need to add eos token manually for T5
            if isinstance(self.tokenizer			,					lowerCAmelCase__	):
                 source_line += self.tokenizer.eos_token
                 tgt_line += self.tokenizer.eos_token

            # Pad source and target to the right
            _UpperCamelCase							       =			(
                self.tokenizer.question_encoder if isinstance(self.tokenizer			,					lowerCAmelCase__	) else self.tokenizer
            )
            _UpperCamelCase							       =			self.tokenizer.generator if isinstance(self.tokenizer			,					lowerCAmelCase__	) else self.tokenizer

            _UpperCamelCase							       =			encode_line(lowerCAmelCase__			,					lowerCAmelCase__			,					self.max_source_length			,					'''right'''	)
            _UpperCamelCase							       =			encode_line(lowerCAmelCase__			,					lowerCAmelCase__			,					self.max_target_length			,					'''right'''	)

            _UpperCamelCase							       =			source_inputs['''input_ids'''].squeeze()
            _UpperCamelCase							       =			target_inputs['''input_ids'''].squeeze()
            _UpperCamelCase							       =			source_inputs['''attention_mask'''].squeeze()
            return {
                "input_ids": source_ids,
                "attention_mask": src_mask,
                "decoder_input_ids": target_ids,
            }





       @staticmethod
       def snake_case__		(				lowerCAmelCase__  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            return [len(lowerCAmelCase__	) for x in Path(lowerCAmelCase__	).open().readlines()]







       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						List[str]	)       ->	Dict[str, torch.Tensor]:

            '''simple docstring'''




            _UpperCamelCase							       =			torch.stack([x['''input_ids'''] for x in batch]	)
            _UpperCamelCase							       =			torch.stack([x['''attention_mask'''] for x in batch]	)
            _UpperCamelCase							       =			torch.stack([x['''decoder_input_ids'''] for x in batch]	)
            _UpperCamelCase							       =			(
                self.tokenizer.generator.pad_token_id
                if isinstance(self.tokenizer			,					lowerCAmelCase__	)
                else self.tokenizer.pad_token_id
            )
            _UpperCamelCase							       =			(
                self.tokenizer.question_encoder.pad_token_id
                if isinstance(self.tokenizer			,					lowerCAmelCase__	)
                else self.tokenizer.pad_token_id
            )
            _UpperCamelCase							       =			trim_batch(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase       ,							_UpperCamelCase							       =			trim_batch(lowerCAmelCase__			,					lowerCAmelCase__			,					attention_mask=lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''input_ids''': source_ids,
                '''attention_mask''': source_mask,
                '''decoder_input_ids''': y,
            }
            return batch

lowercase__		:     Union[str, Any]			=							getLogger(__name__)







def    a__							(							lowercase   :	List[List] )						->						Optional[int]:





     """simple docstring"""

     return list(itertools.chain.from_iterable(lowercase ) )







def    a__							(							lowercase   :	str )						->						None:





     """simple docstring"""

     _UpperCamelCase							       =			get_git_info()
     save_json(lowercase,   os.path.join(lowercase,   '''git_log.json''' ) )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	List[str],   lowercase   :	str=4,   **lowercase   :	Optional[Any] )						->						Any:





     """simple docstring"""

     with open(lowercase,   '''w''' ) as f:
          json.dump(lowercase,   lowercase,   indent=lowercase,   **lowercase )







def    a__							(							lowercase   :	Union[str, Any] )						->						Optional[int]:





     """simple docstring"""

     with open(lowercase ) as f:
          return json.load(lowercase )







def    a__							(							)						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			git.Repo(search_parent_directories=lowercase )
     _UpperCamelCase							       =			{
         '''repo_id''': str(lowercase ),
         '''repo_sha''': str(repo.head.object.hexsha ),
         '''repo_branch''': str(repo.active_branch ),
         '''hostname''': str(socket.gethostname() ),
     }
     return repo_infos







def    a__							(							lowercase   :	Callable,   lowercase   :	Iterable )						->						List:





     """simple docstring"""

     return list(map(lowercase,   lowercase ) )







def    a__							(							lowercase   :	List[Any],   lowercase   :	Dict )						->						Optional[int]:





     """simple docstring"""

     with open(lowercase,   '''wb''' ) as f:
          return pickle.dump(lowercase,   lowercase )







def    a__							(							lowercase   :	Tuple )						->						Tuple:





     """simple docstring"""

     def remove_articles(lowercase   :	Tuple ):
          return re.sub(r'''\b(a|an|the)\b''',   ''' ''',   lowercase )

     def white_space_fix(lowercase   :	Tuple ):
          return " ".join(text.split() )

     def remove_punc(lowercase   :	Optional[int] ):
          _UpperCamelCase							       =			set(string.punctuation )
          return "".join(ch for ch in text if ch not in exclude )

     def lower(lowercase   :	Tuple ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowercase ) ) ) )







def    a__							(							lowercase   :	Tuple,   lowercase   :	Union[str, Any] )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			normalize_answer(lowercase ).split()
     _UpperCamelCase							       =			normalize_answer(lowercase ).split()
     _UpperCamelCase							       =			Counter(lowercase ) & Counter(lowercase )
     _UpperCamelCase							       =			sum(common.values() )
     if num_same == 0:
          return 0
     _UpperCamelCase							       =			1.0 * num_same / len(lowercase )
     _UpperCamelCase							       =			1.0 * num_same / len(lowercase )
     _UpperCamelCase							       =			(2 * precision * recall) / (precision + recall)
     return fa







def    a__							(							lowercase   :	Tuple,   lowercase   :	Any )						->						List[str]:





     """simple docstring"""

     return normalize_answer(lowercase ) == normalize_answer(lowercase )







def    a__							(							lowercase   :	List[str],   lowercase   :	List[str] )						->						Dict:





     """simple docstring"""

     assert len(lowercase ) == len(lowercase )
     _UpperCamelCase							       =			0
     for hypo, pred in zip(lowercase,   lowercase ):
          em += exact_match_score(lowercase,   lowercase )
     if len(lowercase ) > 0:
          em /= len(lowercase )
     return {"em": em}







def    a__							(							lowercase   :	Tuple )						->						Union[str, Any]:





     """simple docstring"""

     return model_prefix.startswith('''rag''' )







def    a__							(							lowercase   :	int,   lowercase   :	List[Any],   lowercase   :	List[Any] )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     _UpperCamelCase							       =			'''dropout_rate'''
     for p in extra_params:
          if getattr(lowercase,   lowercase,   lowercase ):
               if not hasattr(lowercase,   lowercase ) and not hasattr(lowercase,   equivalent_param[p] ):
                    logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase ) )
                    delattr(lowercase,   lowercase )
                    continue
               _UpperCamelCase							       =			p if hasattr(lowercase,   lowercase ) else equivalent_param[p]
               setattr(lowercase,   lowercase,   getattr(lowercase,   lowercase ) )
               delattr(lowercase,   lowercase )
     return hparams, config



