

import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__UpperCAmelCase    				=     logging.get_logger(__name__)

__UpperCAmelCase    				=     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__UpperCAmelCase    				=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(A )} )
 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
 _lowercase     :					int      =						field(
     default=128  ,		metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     }  ,		)
 _lowercase     :					int      =						field(
     default=128  ,		metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}  ,		)
 _lowercase     :					int      =						field(
     default=64  ,		metadata={
         '''help''': (
             '''The maximum number of tokens for the question. Questions longer than this will '''
             '''be truncated to this length.'''
         )
     }  ,		)
 _lowercase     :					int      =						field(
     default=30  ,		metadata={
         '''help''': (
             '''The maximum length of an answer that can be generated. This is needed because the start '''
             '''and end predictions are not conditioned on one another.'''
         )
     }  ,		)
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
 _lowercase     :					float      =						field(
     default=0.0  ,		metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
 _lowercase     :					int      =						field(
     default=20  ,		metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
 _lowercase     :					int      =						field(
     default=0  ,		metadata={
         '''help''': (
             '''language id of input for language-specific xlm models (see'''
             ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
         )
     }  ,		)
 _lowercase     :					int      =						field(default=1  ,		metadata={'''help''': '''multiple threads for converting example to features'''} )





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						'''train'''
 _lowercase     :					Optional[Any]      =						'''dev'''





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					SquadDataTrainingArguments
 _lowercase     :					List[SquadFeatures]
 _lowercase     :					Split
 _lowercase     :					bool

 def __init__(		self :	List[str]     ,						A_ :	SquadDataTrainingArguments     ,						A_ :	PreTrainedTokenizer     ,						A_ :	Optional[int] = None     ,						A_ :	Union[str, Split] = Split.train     ,						A_ :	Optional[bool] = False     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = "pt"     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    args
      _lowerCAmelCase   :					str        =    is_language_sensitive
      _lowerCAmelCase   :					Optional[Any]        =    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
      if isinstance(A_     ,						A_       ):
           try:
                _lowerCAmelCase   :					str        =    Split[mode]
           except KeyError:
                raise KeyError("mode is not a valid split name"       )
      _lowerCAmelCase   :					Any        =    mode
      # Load data features from cache or dataset file
      _lowerCAmelCase   :					Any        =    "v2" if args.version_2_with_negative else "v1"
      _lowerCAmelCase   :					Dict        =    os.path.join(
          cache_dir if cache_dir is not None else args.data_dir     ,						F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'''     ,						)

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      _lowerCAmelCase   :					Tuple        =    cached_features_file + ".lock"
      with FileLock(A_       ):
           if os.path.exists(A_       ) and not args.overwrite_cache:
                _lowerCAmelCase   :					str        =    time.time()
                _lowerCAmelCase   :					Optional[int]        =    torch.load(A_       )

                # Legacy cache files have only features, while new cache files
                # will have dataset and examples also.
                _lowerCAmelCase   :					Union[str, Any]        =    self.old_features["features"]
                _lowerCAmelCase   :					Tuple        =    self.old_features.get("dataset"     ,						A_       )
                _lowerCAmelCase   :					str        =    self.old_features.get("examples"     ,						A_       )
                logger.info(
                    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''     ,						time.time() - start       )

                if self.dataset is None or self.examples is None:
                     logger.warning(
                         F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
                         " future run"       )
           else:
                if mode == Split.dev:
                     _lowerCAmelCase   :					Dict        =    self.processor.get_dev_examples(args.data_dir       )
                else:
                     _lowerCAmelCase   :					Union[str, Any]        =    self.processor.get_train_examples(args.data_dir       )

                _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    squad_convert_examples_to_features(
                    examples=self.examples     ,						tokenizer=A_     ,						max_seq_length=args.max_seq_length     ,						doc_stride=args.doc_stride     ,						max_query_length=args.max_query_length     ,						is_training=mode == Split.train     ,						threads=args.threads     ,						return_dataset=A_     ,						)

                _lowerCAmelCase   :					List[Any]        =    time.time()
                torch.save(
                    {"features": self.features, "dataset": self.dataset, "examples": self.examples}     ,						A_     ,						)
                # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                logger.info(
                    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''       )

 def __len__(		self :	str       ):






      '''simple docstring'''




      return len(self.features       )


 def __getitem__(		self :	List[str]     ,						A_ :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.features[i]

      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor(feature.input_ids     ,						dtype=torch.long       )
      _lowerCAmelCase   :					Any        =    torch.tensor(feature.attention_mask     ,						dtype=torch.long       )
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor(feature.token_type_ids     ,						dtype=torch.long       )
      _lowerCAmelCase   :					Dict        =    torch.tensor(feature.cls_index     ,						dtype=torch.long       )
      _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor(feature.p_mask     ,						dtype=torch.float       )
      _lowerCAmelCase   :					str        =    torch.tensor(feature.is_impossible     ,						dtype=torch.float       )

      _lowerCAmelCase   :					Tuple        =    {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "token_type_ids": token_type_ids,
      }

      if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
           del inputs["token_type_ids"]

      if self.args.model_type in ["xlnet", "xlm"]:
           inputs.update({"cls_index": cls_index, "p_mask": p_mask}       )
           if self.args.version_2_with_negative:
                inputs.update({"is_impossible": is_impossible}       )
           if self.is_language_sensitive:
                inputs.update({"langs": (torch.ones(input_ids.shape     ,						dtype=torch.intaa       ) * self.args.lang_id)}       )

      if self.mode == Split.train:
           _lowerCAmelCase   :					Any        =    torch.tensor(feature.start_position     ,						dtype=torch.long       )
           _lowerCAmelCase   :					Any        =    torch.tensor(feature.end_position     ,						dtype=torch.long       )
           inputs.update({"start_positions": start_positions, "end_positions": end_positions}       )

      return inputs









from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFCvtForImageClassification, TFCvtModel
  from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.config_class(**self.inputs_dict       )
      self.parent.assertTrue(hasattr(A_     ,						"embed_dim"       )       )
      self.parent.assertTrue(hasattr(A_     ,						"num_heads"       )       )





class A__    :



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	int     ,						A_ :	Dict=1_3     ,						A_ :	int=6_4     ,						A_ :	str=3     ,						A_ :	Optional[int]=[1_6, 4_8, 9_6]     ,						A_ :	int=[1, 3, 6]     ,						A_ :	Optional[int]=[1, 2, 1_0]     ,						A_ :	Any=[7, 3, 3]     ,						A_ :	Tuple=[4, 2, 2]     ,						A_ :	str=[2, 1, 1]     ,						A_ :	Optional[Any]=[2, 2, 2]     ,						A_ :	Union[str, Any]=[False, False, True]     ,						A_ :	Union[str, Any]=[0.0, 0.0, 0.0]     ,						A_ :	Any=0.02     ,						A_ :	Optional[int]=1E-12     ,						A_ :	str=True     ,						A_ :	List[Any]=True     ,						A_ :	Union[str, Any]=2     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    parent
      _lowerCAmelCase   :					int        =    batch_size
      _lowerCAmelCase   :					Union[str, Any]        =    image_size
      _lowerCAmelCase   :					Optional[Any]        =    patch_sizes
      _lowerCAmelCase   :					Optional[int]        =    patch_stride
      _lowerCAmelCase   :					List[str]        =    patch_padding
      _lowerCAmelCase   :					str        =    is_training
      _lowerCAmelCase   :					Any        =    use_labels
      _lowerCAmelCase   :					Dict        =    num_labels
      _lowerCAmelCase   :					Tuple        =    num_channels
      _lowerCAmelCase   :					Tuple        =    embed_dim
      _lowerCAmelCase   :					List[Any]        =    num_heads
      _lowerCAmelCase   :					Union[str, Any]        =    stride_kv
      _lowerCAmelCase   :					Tuple        =    depth
      _lowerCAmelCase   :					List[str]        =    cls_token
      _lowerCAmelCase   :					Tuple        =    attention_drop_rate
      _lowerCAmelCase   :					Optional[Any]        =    initializer_range
      _lowerCAmelCase   :					Optional[int]        =    layer_norm_eps

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _lowerCAmelCase   :					int        =    None
      if self.use_labels:
           # create a random int32 tensor of given shape
           _lowerCAmelCase   :					List[Any]        =    ids_tensor([self.batch_size]     ,						self.num_labels       )

      _lowerCAmelCase   :					Tuple        =    self.get_config()
      return config, pixel_values, labels

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      return CvtConfig(
          image_size=self.image_size     ,						num_labels=self.num_labels     ,						num_channels=self.num_channels     ,						embed_dim=self.embed_dim     ,						num_heads=self.num_heads     ,						patch_sizes=self.patch_sizes     ,						patch_padding=self.patch_padding     ,						patch_stride=self.patch_stride     ,						stride_kv=self.stride_kv     ,						depth=self.depth     ,						cls_token=self.cls_token     ,						attention_drop_rate=self.attention_drop_rate     ,						initializer_range=self.initializer_range     ,						)

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	str     ,						A_ :	Optional[int]     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    TFCvtModel(config=A_       )
      _lowerCAmelCase   :					str        =    model(A_     ,						training=A_       )
      _lowerCAmelCase   :					List[Any]        =    (self.image_size, self.image_size)
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    image_size[0], image_size[1]
      for i in range(len(self.depth       )       ):
           _lowerCAmelCase   :					Union[str, Any]        =    floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
           _lowerCAmelCase   :					List[Any]        =    floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.embed_dim[-1], height, width)       )

 def    __magic_name__       (		self :	Any     ,						A_ :	Tuple     ,						A_ :	int     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.num_labels
      _lowerCAmelCase   :					Optional[Any]        =    TFCvtForImageClassification(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    model(A_     ,						labels=A_     ,						training=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.prepare_config_and_inputs()
      _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    config_and_inputs
      _lowerCAmelCase   :					Optional[int]        =    {"pixel_values": pixel_values}
      return config, inputs_dict





@require_tf
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Any      =						(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
 _lowercase     :					Tuple      =						(
     {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
     if is_tf_available()
     else {}
 )
 _lowercase     :					Optional[int]      =						False
 _lowercase     :					Any      =						False
 _lowercase     :					int      =						False
 _lowercase     :					int      =						False
 _lowercase     :					Tuple      =						False

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    TFCvtModelTester(self       )
      _lowerCAmelCase   :					Optional[Any]        =    TFCvtConfigTester(self     ,						config_class=A_     ,						has_text_modality=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      self.config_tester.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()

 @unittest.skip(reason="Cvt does not output attentions"       )
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      pass

 @unittest.skip(reason="Cvt does not use inputs_embeds"       )
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      pass

 @unittest.skip(reason="Cvt does not support input and output embeddings"       )
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      pass

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0     ,						reason="TF does not support backprop for grouped convolutions on CPU."     ,						)
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      super().test_dataset_conversion()

 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"       )       ) == 0     ,						reason="TF does not support backprop for grouped convolutions on CPU."     ,						)
 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      super().test_keras_fit()

 @unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"       )
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    tf.keras.mixed_precision.Policy("mixed_float16"       )
      tf.keras.mixed_precision.set_global_policy(A_       )
      super().test_keras_fit()
      tf.keras.mixed_precision.set_global_policy("float32"       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _lowerCAmelCase   :					Dict        =    model_class(A_       )
           _lowerCAmelCase   :					Optional[int]        =    inspect.signature(model.call       )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowerCAmelCase   :					List[Any]        =    [*signature.parameters.keys()]

           _lowerCAmelCase   :					Union[str, Any]        =    ["pixel_values"]
           self.assertListEqual(arg_names[:1]     ,						A_       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      def check_hidden_states_output(A_ :	Any     ,						A_ :	Any     ,						A_ :	Dict       ):
           _lowerCAmelCase   :					Tuple        =    model_class(A_       )

           _lowerCAmelCase   :					List[Any]        =    model(**self._prepare_for_class(A_     ,						A_       )       )
           _lowerCAmelCase   :					Tuple        =    outputs.hidden_states

           _lowerCAmelCase   :					Optional[Any]        =    len(self.model_tester.depth       )
           self.assertEqual(len(A_       )     ,						A_       )

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]       )     ,						[
                   self.model_tester.embed_dim[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ]     ,						)

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _lowerCAmelCase   :					Union[str, Any]        =    True
           check_hidden_states_output(A_     ,						A_     ,						A_       )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowerCAmelCase   :					List[str]        =    True

           check_hidden_states_output(A_     ,						A_     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A_       )


 @slow
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					Optional[Any]        =    TFCvtModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )







def 	_snake_case		(    )       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image





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



 """simple docstring"""







 @cached_property
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )


 @slow
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

      _lowerCAmelCase   :					int        =    self.default_image_processor
      _lowerCAmelCase   :					Optional[Any]        =    prepare_img()
      _lowerCAmelCase   :					Optional[Any]        =    image_processor(images=A_     ,						return_tensors="tf"       )

      # forward pass
      _lowerCAmelCase   :					str        =    model(**A_       )

      # verify the logits
      _lowerCAmelCase   :					List[str]        =    tf.TensorShape((1, 1_0_0_0)       )
      self.assertEqual(outputs.logits.shape     ,						A_       )

      _lowerCAmelCase   :					Any        =    tf.constant([0.9285, 0.9015, -0.3150]       )
      self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     ,						A_     ,						atol=1E-4       )       )






