






import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import RegNetForImageClassification, RegNetModel
				from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Tuple=3,						lowerCamelCase		:					int=32,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					Dict=[10, 20, 30, 40],						lowerCamelCase		:					List[str]=[1, 1, 2, 1],						lowerCamelCase		:					List[Any]=True,						lowerCamelCase		:					int=True,						lowerCamelCase		:					Optional[Any]="relu",						lowerCamelCase		:					Dict=3,						lowerCamelCase		:					Optional[Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    image_size
								lowercase__     				=    num_channels
								lowercase__     				=    embeddings_size
								lowercase__     				=    hidden_sizes
								lowercase__     				=    depths
								lowercase__     				=    is_training
								lowercase__     				=    use_labels
								lowercase__     				=    hidden_act
								lowercase__     				=    num_labels
								lowercase__     				=    scope
								lowercase__     				=    len(lowerCamelCase						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								lowercase__     				=    None
								if self.use_labels:
												lowercase__     				=    ids_tensor([self.batch_size],						self.num_labels						)

								lowercase__     				=    self.get_config()

								return config, pixel_values, labels




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return RegNetConfig(
								    num_channels=self.num_channels,						embeddings_size=self.embeddings_size,						hidden_sizes=self.hidden_sizes,						depths=self.depths,						hidden_act=self.hidden_act,						num_labels=self.num_labels,						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    RegNetModel(config=lowerCamelCase						)
								model.to(lowerCamelCase						)
								model.eval()
								lowercase__     				=    model(lowerCamelCase						)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					int,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    RegNetForImageClassification(lowerCamelCase						)
								model.to(lowerCamelCase						)
								model.eval()
								lowercase__     				=    model(lowerCamelCase,						labels=lowerCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								lowercase__				,		lowercase__				,		lowercase__     				=    config_and_inputs
								lowercase__     				=    {'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class    _UpperCAmelCase      ( A__	,A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
				lowercase__       							= (
				    {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
				    if is_torch_available()
				    else {}
				)

				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    RegNetModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								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		:					int						):

								'''simple docstring'''
								return




				@unittest.skip(reason='''RegNet does not use inputs_embeds'''						)
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''RegNet does not support input and output embeddings'''						)
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase__     				=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1],						lowerCamelCase						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase						)




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

								'''simple docstring'''
								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__     				=    model_class(config=lowerCamelCase						)
												for name, module in model.named_modules():
																if isinstance(lowerCamelCase,						(nn.BatchNormad, nn.GroupNorm)						):
																				self.assertTrue(
																				    torch.all(module.weight == 1						),						msg=F"""Parameter {name} of model {model_class} seems not properly initialized""",						)
																				self.assertTrue(
																				    torch.all(module.bias == 0						),						msg=F"""Parameter {name} of model {model_class} seems not properly initialized""",						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase		:					str,						lowerCamelCase		:					str,						lowerCamelCase		:					Dict						):
												lowercase__     				=    model_class(lowerCamelCase						)
												model.to(lowerCamelCase						)
												model.eval()

												with torch.no_grad():
																lowercase__     				=    model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase						)						)

												lowercase__     				=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowercase__     				=    self.model_tester.num_stages
												self.assertEqual(len(lowerCamelCase						),						expected_num_stages + 1						)

												# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]						),						[self.model_tester.image_size // 2, self.model_tester.image_size // 2],						)

								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__     				=    ['''basic''', '''bottleneck''']
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																lowercase__     				=    layer_type
																lowercase__     				=    True
																check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

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

																check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)




				@slow
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    RegNetModel.from_pretrained(lowerCamelCase						)
												self.assertIsNotNone(lowerCamelCase						)






def 						a    (		):






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




@require_torch
@require_vision
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				@cached_property
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
								    if is_vision_available()
								    else None
								)




				@slow
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowerCamelCase						)

								lowercase__     				=    self.default_image_processor
								lowercase__     				=    prepare_img()
								lowercase__     				=    image_processor(images=lowerCamelCase,						return_tensors='''pt'''						).to(lowerCamelCase						)

								# forward pass
								with torch.no_grad():
												lowercase__     				=    model(**lowerCamelCase						)

								# verify the logits
								lowercase__     				=    torch.Size((1, 1_000)						)
								self.assertEqual(outputs.logits.shape,						lowerCamelCase						)

								lowercase__     				=    torch.tensor([-0.4180, -1.5051, -3.4836]						).to(lowerCamelCase						)

								self.assertTrue(torch.allclose(outputs.logits[0, :3],						lowerCamelCase,						atol=1E-4						)						)












import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
							import torch


if is_vision_available():
							from PIL import Image

							from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= ViTImageProcessor if is_vision_available() else None




				@property
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    (3, 32, 128)
								lowercase__     				=    tempfile.mkdtemp()

								# fmt: off
								lowercase__     				=    ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
								# fmt: on
								lowercase__     				=    dict(zip(lowerCamelCase,						range(len(lowerCamelCase						)						)						)						)

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

								lowercase__     				=    {
								    '''do_normalize''': False,
								    '''do_resize''': True,
								    '''image_processor_type''': '''ViTImageProcessor''',
								    '''resample''': 3,
								    '''size''': {'''height''': 32, '''width''': 128},
								}
								lowercase__     				=    os.path.join(self.tmpdirname,						lowerCamelCase						)
								with open(self.image_processor_file,						'''w''',						encoding='''utf-8'''						) as fp:
												json.dump(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								return MgpstrTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					str,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								return ViTImageProcessor.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								shutil.rmtree(self.tmpdirname						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.random.randint(255,						size=(3, 30, 400),						dtype=np.uinta						)

								lowercase__     				=    Image.fromarray(np.moveaxis(lowerCamelCase,						0,						-1						)						)

								return image_input




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)
								lowercase__     				=    MgpstrProcessor.from_pretrained(self.tmpdirname,						use_fast=lowerCamelCase						)

								self.assertEqual(processor.char_tokenizer.get_vocab(),						tokenizer.get_vocab()						)
								self.assertIsInstance(processor.char_tokenizer,						lowerCamelCase						)

								self.assertEqual(processor.image_processor.to_json_string(),						image_processor.to_json_string()						)
								self.assertIsInstance(processor.image_processor,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_image_processor()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)
								processor.save_pretrained(self.tmpdirname						)

								lowercase__     				=    self.get_tokenizer(bos_token='''(BOS)''',						eos_token='''(EOS)'''						)
								lowercase__     				=    self.get_image_processor(do_normalize=lowerCamelCase,						padding_value=1.0						)

								lowercase__     				=    MgpstrProcessor.from_pretrained(
								    self.tmpdirname,						bos_token='''(BOS)''',						eos_token='''(EOS)''',						do_normalize=lowerCamelCase,						padding_value=1.0						)

								self.assertEqual(processor.char_tokenizer.get_vocab(),						tokenizer_add_kwargs.get_vocab()						)
								self.assertIsInstance(processor.char_tokenizer,						lowerCamelCase						)

								self.assertEqual(processor.image_processor.to_json_string(),						image_processor_add_kwargs.to_json_string()						)
								self.assertIsInstance(processor.image_processor,						lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''np'''						)
								lowercase__     				=    processor(images=lowerCamelCase,						return_tensors='''np'''						)

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




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

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''

								lowercase__     				=    processor(text=lowerCamelCase						)

								lowercase__     				=    tokenizer(lowerCamelCase						)
								for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key],						encoded_processor[key]						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    '''test'''
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

								self.assertListEqual(list(inputs.keys()						),						['''pixel_values''', '''labels''']						)

								# test if it raises when no input is passed
								with pytest.raises(lowerCamelCase						):
												processor()




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

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

								lowercase__     				=    processor.char_decode(lowerCamelCase						)
								lowercase__     				=    tokenizer.batch_decode(lowerCamelCase						)
								lowercase__     				=    [seq.replace(''' ''',						''''''						) for seq in decoded_tok]

								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




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

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    None
								lowercase__     				=    self.prepare_image_inputs()

								lowercase__     				=    processor(text=lowerCamelCase,						images=lowerCamelCase						)

								self.assertListEqual(list(inputs.keys()						),						processor.model_input_names						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_image_processor()
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    MgpstrProcessor(tokenizer=lowerCamelCase,						image_processor=lowerCamelCase						)

								lowercase__     				=    torch.randn(1,						27,						38						)
								lowercase__     				=    torch.randn(1,						27,						50_257						)
								lowercase__     				=    torch.randn(1,						27,						30_522						)

								lowercase__     				=    processor.batch_decode([char_input, bpe_input, wp_input]						)

								self.assertListEqual(list(results.keys()						),						['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']						)




