



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  snake_case__						:






     """simple docstring"""


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






     def        lowercase_       (	self  :  Dict )       ->Dict:
            snake_case__							:     str            =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

            snake_case__							:     int            =     None
            if self.use_labels:
                   snake_case__							:     Any            =     ids_tensor([self.batch_size],			self.type_sequence_label_size )

            snake_case__							:     Union[str, Any]            =     self.get_config()

            return config, pixel_values, labels






     def        lowercase_       (	self  :  Dict )       ->Any:
            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        lowercase_       (	self  :  Dict,			_snake_case  :  Union[str, Any],			_snake_case  :  str,			_snake_case  :  Dict )       ->Any:
            snake_case__							:     int            =     DonutSwinModel(config=_snake_case )
            model.to(_snake_case )
            model.eval()
            snake_case__							:     Dict            =     model(_snake_case )

            snake_case__							:     Dict            =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
            snake_case__							:     List[Any]            =     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        lowercase_       (	self  :  Any )       ->Dict:
            snake_case__							:     str            =     self.prepare_config_and_inputs()
            (
                snake_case__							
            )							:     Any            =     config_and_inputs
            snake_case__							:     str            =     {'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class  snake_case__						(	snake_case__    ,					snake_case__    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     (DonutSwinModel,) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE            =     {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
     _SCREAMING_SNAKE_CASE            =     True

     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     False






     def        lowercase_       (	self  :  Any )       ->Optional[Any]:
            snake_case__							:     Optional[int]            =     DonutSwinModelTester(self )
            snake_case__							:     Dict            =     ConfigTester(self,			config_class=_snake_case,			embed_dim=3_7 )






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






     def        lowercase_       (	self  :  List[Any] )       ->Any:
            snake_case__							:     Union[str, Any]            =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_snake_case )






     def        lowercase_       (	self  :  Union[str, Any] )       ->Union[str, Any]:
            # DonutSwin does not use inputs_embeds
            pass






     def        lowercase_       (	self  :  Union[str, Any] )       ->Optional[Any]:
            snake_case__							:     List[Any]            =     self.model_tester.prepare_config_and_inputs_for_common()

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






     def        lowercase_       (	self  :  int )       ->str:
            snake_case__							:     int            =     self.model_tester.prepare_config_and_inputs_for_common()

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

                   snake_case__							:     Any            =     ['pixel_values']
                   self.assertListEqual(arg_names[:1],			_snake_case )






     def        lowercase_       (	self  :  Union[str, Any] )       ->Dict:
            snake_case__							:     List[str]            =     self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:     List[str]            =     True

            for model_class in self.all_model_classes:
                   snake_case__							:     List[Any]            =     True
                   snake_case__							:     Any            =     False
                   snake_case__							:     Dict            =     True
                   snake_case__							:     Optional[int]            =     model_class(_snake_case )
                   model.to(_snake_case )
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:     Union[str, Any]            =     model(**self._prepare_for_class(_snake_case,			_snake_case ) )
                   snake_case__							:     Union[str, Any]            =     outputs.attentions
                   snake_case__							:     Optional[int]            =     len(self.model_tester.depths )
                   self.assertEqual(len(_snake_case ),			_snake_case )

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

                   self.assertListEqual(
                       list(attentions[0].shape[-3:] ),			[self.model_tester.num_heads[0], window_size_squared, window_size_squared],			)
                   snake_case__							:     List[Any]            =     len(_snake_case )

                   # Check attention is always last and order is fine
                   snake_case__							:     Dict            =     True
                   snake_case__							:     List[str]            =     True
                   snake_case__							:     Union[str, Any]            =     model_class(_snake_case )
                   model.to(_snake_case )
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:     int            =     model(**self._prepare_for_class(_snake_case,			_snake_case ) )

                   if hasattr(self.model_tester,			'num_hidden_states_types' ):
                          snake_case__							:     List[str]            =     self.model_tester.num_hidden_states_types
                   else:
                          # also another +1 for reshaped_hidden_states
                          snake_case__							:     Dict            =     2
                   self.assertEqual(out_len + added_hidden_states,			len(_snake_case ) )

                   snake_case__							:     Optional[Any]            =     outputs.attentions

                   self.assertEqual(len(_snake_case ),			_snake_case )

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






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  List[str],			_snake_case  :  List[str],			_snake_case  :  Dict,			_snake_case  :  Any )       ->Tuple:
            snake_case__							:     Dict            =     model_class(_snake_case )
            model.to(_snake_case )
            model.eval()

            with torch.no_grad():
                   snake_case__							:     int            =     model(**self._prepare_for_class(_snake_case,			_snake_case ) )

            snake_case__							:     Union[str, Any]            =     outputs.hidden_states

            snake_case__							:     Optional[int]            =     getattr(
                self.model_tester,			'expected_num_hidden_layers',			len(self.model_tester.depths ) + 1 )
            self.assertEqual(len(_snake_case ),			_snake_case )

            # DonutSwin has a different seq_length
            snake_case__							:     Optional[int]            =     (
                config.patch_size
                if isinstance(config.patch_size,			collections.abc.Iterable )
                else (config.patch_size, config.patch_size)
            )

            snake_case__							:     List[Any]            =     (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],			)

            snake_case__							:     List[str]            =     outputs.reshaped_hidden_states
            self.assertEqual(len(_snake_case ),			_snake_case )

            snake_case__							:     Dict            =     reshaped_hidden_states[0].shape
            snake_case__							:     List[Any]            =     (
                reshaped_hidden_states[0].view(_snake_case,			_snake_case,			height * width ).permute(0,			2,			1 )
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:] ),			[num_patches, self.model_tester.embed_dim],			)






     def        lowercase_       (	self  :  List[str] )       ->Dict:
            snake_case__							:     Any            =     self.model_tester.prepare_config_and_inputs_for_common()

            snake_case__							:     int            =     (
                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:
                   snake_case__							:     str            =     True
                   self.check_hidden_states_output(_snake_case,			_snake_case,			_snake_case,			_snake_case )

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

                   self.check_hidden_states_output(_snake_case,			_snake_case,			_snake_case,			_snake_case )






     def        lowercase_       (	self  :  int )       ->List[Any]:
            snake_case__							:     List[Any]            =     self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:     Optional[int]            =     3

            snake_case__							:     Dict            =     (
                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)
            )
            snake_case__							:     Tuple            =     (
                config.patch_size
                if isinstance(config.patch_size,			collections.abc.Iterable )
                else (config.patch_size, config.patch_size)
            )

            snake_case__							:     Dict            =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            snake_case__							:     Union[str, Any]            =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                   snake_case__							:     List[Any]            =     True
                   self.check_hidden_states_output(_snake_case,			_snake_case,			_snake_case,			(padded_height, padded_width) )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   snake_case__							:     Union[str, Any]            =     True
                   self.check_hidden_states_output(_snake_case,			_snake_case,			_snake_case,			(padded_height, padded_width) )






     @slow
     def        lowercase_       (	self  :  Optional[int] )       ->Optional[Any]:
            for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   snake_case__							:     List[str]            =     DonutSwinModel.from_pretrained(_snake_case )
                   self.assertIsNotNone(_snake_case )






     def        lowercase_       (	self  :  Optional[Any] )       ->str:
            snake_case__							:     List[str]            =     self.model_tester.prepare_config_and_inputs_for_common()

            snake_case__							:     Union[str, Any]            =     _config_zero_init(_snake_case )
            for model_class in self.all_model_classes:
                   snake_case__							:     int            =     model_class(config=_snake_case )
                   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''',			)











import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     BertweetTokenizer
     _SCREAMING_SNAKE_CASE            =     False






     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            snake_case__							:     List[Any]            =     ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
            snake_case__							:     Tuple            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )
            snake_case__							:     int            =     ['#version: 0.2', 'a m</w>']
            snake_case__							:     Dict            =     {'unk_token': '<unk>'}

            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['merges_file'] )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   for token in vocab_tokens:
                          fp.write(F'''{token} {vocab_tokens[token]}\n''' )
            with open(self.merges_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write('\n'.join(_snake_case ) )






     def        lowercase_       (	self  :  List[Any],			**_snake_case  :  List[Any] )       ->Optional[Any]:
            kwargs.update(self.special_tokens_map )
            return BertweetTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Tuple )       ->Union[str, Any]:
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
            return input_text, output_text






     def        lowercase_       (	self  :  str )       ->int:
            snake_case__							:     Dict            =     BertweetTokenizer(self.vocab_file,			self.merges_file,			**self.special_tokens_map )
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
            snake_case__							:     Optional[int]            =     tokenizer.tokenize(_snake_case )
            self.assertListEqual(_snake_case,			_snake_case )

            snake_case__							:     Tuple            =     tokens + [tokenizer.unk_token]

            snake_case__							:     Dict            =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ),			_snake_case )






