
"""simple docstring"""

import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import DonutSwinModel
   from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
class    _UpperCAmelCase							:




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




      def 			a_					(							self							)  ->    Dict:
          UpperCAmelCase  	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

          UpperCAmelCase  	=    self.get_config()

          return config, pixel_values, labels




      def 			a_					(							self							)  ->    Optional[int]:
          return DonutSwinConfig(
              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 			a_					(							self      ,				lowercase_      ,				lowercase_      ,				lowercase_							)  ->    Dict:
          UpperCAmelCase  	=    DonutSwinModel(config=lowercase_							)
          model.to(lowercase_							)
          model.eval()
          UpperCAmelCase  	=    model(lowercase_							)

          UpperCAmelCase  	=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
          UpperCAmelCase  	=    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 			a_					(							self							)  ->    Dict:
          UpperCAmelCase  	=    self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )	,							(
              UpperCAmelCase
          )	,							(
              UpperCAmelCase
          )	,							
          )  	=    config_and_inputs
          UpperCAmelCase  	=    {'pixel_values': pixel_values}
          return config, inputs_dict

@require_torch
class    _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_   ,	unittest.TestCase							):
      __SCREAMING_SNAKE_CASE      :							Union[str, Any]												=		(DonutSwinModel,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE      :							List[Any]												=		{"feature-extraction": DonutSwinModel} if is_torch_available() else {}
      __SCREAMING_SNAKE_CASE      :							List[Any]												=		True

      __SCREAMING_SNAKE_CASE      :							str												=		False
      __SCREAMING_SNAKE_CASE      :							Any												=		False
      __SCREAMING_SNAKE_CASE      :							Dict												=		False




      def 			a_					(							self							)  ->    Union[str, Any]:
          UpperCAmelCase  	=    DonutSwinModelTester(self							)
          UpperCAmelCase  	=    ConfigTester(self      ,				config_class=lowercase_      ,				embed_dim=3_7							)




      def 			a_					(							self							)  ->    List[Any]:
          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 			a_					(							self							)  ->    Any:
          return




      def 			a_					(							self							)  ->    Any:
          UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def 			a_					(							self							)  ->    Dict:
          # DonutSwin does not use inputs_embeds
          pass




      def 			a_					(							self							)  ->    int:
          UpperCAmelCase	,							UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase  	=    model_class(lowercase_							)
              self.assertIsInstance(model.get_input_embeddings()      ,				(nn.Module)							)
              UpperCAmelCase  	=    model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase_      ,				nn.Linear							)							)




      def 			a_					(							self							)  ->    Optional[Any]:
          UpperCAmelCase	,							UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase  	=    ['pixel_values']
              self.assertListEqual(arg_names[:1]      ,				lowercase_							)




      def 			a_					(							self							)  ->    Any:
          UpperCAmelCase	,							UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase  	=    True

          for model_class in self.all_model_classes:
              UpperCAmelCase  	=    True
              UpperCAmelCase  	=    False
              UpperCAmelCase  	=    True
              UpperCAmelCase  	=    model_class(lowercase_							)
              model.to(lowercase_							)
              model.eval()
              with torch.no_grad():
                  UpperCAmelCase  	=    model(**self._prepare_for_class(lowercase_      ,				lowercase_							)							)
              UpperCAmelCase  	=    outputs.attentions
              UpperCAmelCase  	=    len(self.model_tester.depths							)
              self.assertEqual(len(lowercase_							)      ,				lowercase_							)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              UpperCAmelCase  	=    True
              UpperCAmelCase  	=    config.window_size**2
              UpperCAmelCase  	=    model_class(lowercase_							)
              model.to(lowercase_							)
              model.eval()
              with torch.no_grad():
                  UpperCAmelCase  	=    model(**self._prepare_for_class(lowercase_      ,				lowercase_							)							)
              UpperCAmelCase  	=    outputs.attentions
              self.assertEqual(len(lowercase_							)      ,				lowercase_							)

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

              # Check attention is always last and order is fine
              UpperCAmelCase  	=    True
              UpperCAmelCase  	=    True
              UpperCAmelCase  	=    model_class(lowercase_							)
              model.to(lowercase_							)
              model.eval()
              with torch.no_grad():
                  UpperCAmelCase  	=    model(**self._prepare_for_class(lowercase_      ,				lowercase_							)							)

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

              UpperCAmelCase  	=    outputs.attentions

              self.assertEqual(len(lowercase_							)      ,				lowercase_							)

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




      def 			a_					(							self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_							)  ->    Tuple:
          UpperCAmelCase  	=    model_class(lowercase_							)
          model.to(lowercase_							)
          model.eval()

          with torch.no_grad():
              UpperCAmelCase  	=    model(**self._prepare_for_class(lowercase_      ,				lowercase_							)							)

          UpperCAmelCase  	=    outputs.hidden_states

          UpperCAmelCase  	=    getattr(
              self.model_tester      ,				'expected_num_hidden_layers'      ,				len(self.model_tester.depths							) + 1							)
          self.assertEqual(len(lowercase_							)      ,				lowercase_							)

          # DonutSwin has a different seq_length
          UpperCAmelCase  	=    (
              config.patch_size
              if isinstance(config.patch_size      ,				collections.abc.Iterable							)
              else (config.patch_size, config.patch_size)
          )

          UpperCAmelCase  	=    (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]      ,				)

          UpperCAmelCase  	=    outputs.reshaped_hidden_states
          self.assertEqual(len(lowercase_							)      ,				lowercase_							)

          UpperCAmelCase	,							UpperCAmelCase	,							UpperCAmelCase	,							UpperCAmelCase  	=    reshaped_hidden_states[0].shape
          UpperCAmelCase  	=    (
              reshaped_hidden_states[0].view(lowercase_      ,				lowercase_      ,				height * width							).permute(0      ,				2      ,				1							)
          )
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]							)      ,				[num_patches, self.model_tester.embed_dim]      ,				)




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase	,							UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase  	=    (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size      ,				collections.abc.Iterable							)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
              UpperCAmelCase  	=    True
              self.check_hidden_states_output(lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_							)

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

              self.check_hidden_states_output(lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_							)




      def 			a_					(							self							)  ->    Union[str, Any]:
          UpperCAmelCase	,							UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase  	=    3

          UpperCAmelCase  	=    (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size      ,				collections.abc.Iterable							)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          UpperCAmelCase  	=    (
              config.patch_size
              if isinstance(config.patch_size      ,				collections.abc.Iterable							)
              else (config.patch_size, config.patch_size)
          )

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

          for model_class in self.all_model_classes:
              UpperCAmelCase  	=    True
              self.check_hidden_states_output(lowercase_      ,				lowercase_      ,				lowercase_      ,				(padded_height, padded_width)							)

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




      @slow
      def 			a_					(							self							)  ->    Optional[Any]:
          for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase  	=    DonutSwinModel.from_pretrained(lowercase_							)
              self.assertIsNotNone(lowercase_							)




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase	,							UpperCAmelCase  	=    self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase  	=    _config_zero_init(lowercase_							)
          for model_class in self.all_model_classes:
              UpperCAmelCase  	=    model_class(config=lowercase_							)
              for name, param in model.named_parameters():
                  if "embeddings" not in name and param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()      ,				[0.0, 1.0]      ,				msg=F"Parameter {name} of model {model_class} seems not properly initialized"      ,				)





"""simple docstring"""

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list
class    _UpperCAmelCase							(						unittest.TestCase							):




      def 			a_					(							self							)  ->    Any:
          UpperCAmelCase  	=    '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' '							)
          UpperCAmelCase  	=    dict(zip(lowercase_      ,				range(len(lowercase_							)							)							)							)

          UpperCAmelCase  	=    {
              'pad_token': '<pad>',
              'unk_token': '<unk>',
              'bos_token': '<s>',
              'eos_token': '</s>',
          }
          UpperCAmelCase  	=    {
              'feature_size': 1,
              'padding_value': 0.0,
              'sampling_rate': 1_6_0_0_0,
              'return_attention_mask': False,
              'do_normalize': True,
          }

          UpperCAmelCase  	=    tempfile.mkdtemp()
          UpperCAmelCase  	=    os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES['vocab_file']							)
          UpperCAmelCase  	=    os.path.join(self.tmpdirname      ,				lowercase_							)
          with open(self.vocab_file      ,				'w'      ,				encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)

          with open(self.feature_extraction_file      ,				'w'      ,				encoding='utf-8'							) as fp:
              fp.write(json.dumps(lowercase_							) + '\n'							)




      def 			a_					(							self      ,				**lowercase_							)  ->    Any:
          UpperCAmelCase  	=    self.add_kwargs_tokens_map.copy()
          kwargs.update(lowercase_							)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self      ,				**lowercase_							)  ->    Dict:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,				**lowercase_							)




      def 			a_					(							self							)  ->    Any:
          shutil.rmtree(self.tmpdirname							)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_tokenizer()
          UpperCAmelCase  	=    self.get_feature_extractor()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          processor.save_pretrained(self.tmpdirname							)
          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(self.tmpdirname							)

          self.assertEqual(processor.tokenizer.get_vocab()      ,				tokenizer.get_vocab()							)
          self.assertIsInstance(processor.tokenizer      ,				lowercase_							)

          self.assertEqual(processor.feature_extractor.to_json_string()      ,				feature_extractor.to_json_string()							)
          self.assertIsInstance(processor.feature_extractor      ,				lowercase_							)




      def 			a_					(							self							)  ->    Dict:
          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,				feature_extractor=self.get_feature_extractor()							)
          processor.save_pretrained(self.tmpdirname							)

          UpperCAmelCase  	=    self.get_tokenizer(bos_token='(BOS)'      ,				eos_token='(EOS)'							)
          UpperCAmelCase  	=    self.get_feature_extractor(do_normalize=lowercase_      ,				padding_value=1.0							)

          UpperCAmelCase  	=    WavaVecaProcessor.from_pretrained(
              self.tmpdirname      ,				bos_token='(BOS)'      ,				eos_token='(EOS)'      ,				do_normalize=lowercase_      ,				padding_value=1.0							)

          self.assertEqual(processor.tokenizer.get_vocab()      ,				tokenizer_add_kwargs.get_vocab()							)
          self.assertIsInstance(processor.tokenizer      ,				lowercase_							)

          self.assertEqual(processor.feature_extractor.to_json_string()      ,				feature_extractor_add_kwargs.to_json_string()							)
          self.assertIsInstance(processor.feature_extractor      ,				lowercase_							)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    floats_list((3, 1_0_0_0)							)

          UpperCAmelCase  	=    feature_extractor(lowercase_      ,				return_tensors='np'							)
          UpperCAmelCase  	=    processor(lowercase_      ,				return_tensors='np'							)

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()      ,				input_processor[key].sum()      ,				delta=1E-2							)




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    'This is a test string'

          UpperCAmelCase  	=    processor(text=lowercase_							)

          UpperCAmelCase  	=    tokenizer(lowercase_							)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]      ,				encoded_processor[key]							)




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          UpperCAmelCase  	=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCAmelCase  	=    processor.batch_decode(lowercase_							)
          UpperCAmelCase  	=    tokenizer.batch_decode(lowercase_							)

          self.assertListEqual(lowercase_      ,				lowercase_							)




      def 			a_					(							self							)  ->    Union[str, Any]:
          UpperCAmelCase  	=    self.get_feature_extractor()
          UpperCAmelCase  	=    self.get_tokenizer()

          UpperCAmelCase  	=    WavaVecaProcessor(tokenizer=lowercase_      ,				feature_extractor=lowercase_							)

          self.assertListEqual(
              processor.model_input_names      ,				feature_extractor.model_input_names      ,				msg='`processor` and `feature_extractor` model input names do not match'      ,				)



