






import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
							from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Dict  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Optional[int]=13  ,     lowerCamelCase__     :str=32  ,     lowerCamelCase__     :Optional[Any]=3  ,     lowerCamelCase__     :Union[str, Any]=4  ,     lowerCamelCase__     :str=[10, 20, 30, 40]  ,     lowerCamelCase__     :Any=[2, 2, 3, 2]  ,     lowerCamelCase__     :List[str]=True  ,     lowerCamelCase__     :List[Any]=True  ,     lowerCamelCase__     :Tuple=37  ,     lowerCamelCase__     :int="gelu"  ,     lowerCamelCase__     :Optional[Any]=10  ,     lowerCamelCase__     :Union[str, Any]=0.02  ,     lowerCamelCase__     :int=["stage2", "stage3", "stage4"]  ,     lowerCamelCase__     :Optional[int]=[2, 3, 4]  ,     lowerCamelCase__     :Optional[int]=None  ,     ):
					UpperCamelCase__ :Union[str, Any]						=      parent
					UpperCamelCase__ :List[str]						=      batch_size
					UpperCamelCase__ :Dict						=      image_size
					UpperCamelCase__ :Tuple						=      num_channels
					UpperCamelCase__ :Tuple						=      num_stages
					UpperCamelCase__ :Optional[Any]						=      hidden_sizes
					UpperCamelCase__ :Optional[int]						=      depths
					UpperCamelCase__ :Dict						=      is_training
					UpperCamelCase__ :Union[str, Any]						=      use_labels
					UpperCamelCase__ :Tuple						=      intermediate_size
					UpperCamelCase__ :Tuple						=      hidden_act
					UpperCamelCase__ :Optional[Any]						=      num_labels
					UpperCamelCase__ :Optional[Any]						=      initializer_range
					UpperCamelCase__ :Union[str, Any]						=      out_features
					UpperCamelCase__ :Any						=      out_indices
					UpperCamelCase__ :int						=      scope







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :Any						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

					UpperCamelCase__ :Union[str, Any]						=      None
					if self.use_labels:
						UpperCamelCase__ :List[str]						=      ids_tensor([self.batch_size]  ,     self.num_labels     )

					UpperCamelCase__ :Union[str, Any]						=      self.get_config()
					return config, pixel_values, labels







				def 				__a   ( self     :List[str]     ):
					return ConvNextConfig(
					    num_channels=self.num_channels  ,     hidden_sizes=self.hidden_sizes  ,     depths=self.depths  ,     num_stages=self.num_stages  ,     hidden_act=self.hidden_act  ,     is_decoder=lowerCamelCase__  ,     initializer_range=self.initializer_range  ,     out_features=self.out_features  ,     out_indices=self.out_indices  ,     num_labels=self.num_labels  ,     )







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :Union[str, Any]     ):
					UpperCamelCase__ :List[Any]						=      ConvNextModel(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :Union[str, Any]						=      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 				__a   ( self     :Tuple  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]     ):
					UpperCamelCase__ :Union[str, Any]						=      ConvNextForImageClassification(lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :str						=      model(lowerCamelCase__  ,     labels=lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)     )







				def 				__a   ( self     :Optional[int]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :List[str]     ):
					UpperCamelCase__ :List[str]						=      ConvNextBackbone(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :Optional[Any]						=      model(lowerCamelCase__     )

					# verify hidden states
					self.parent.assertEqual(len(result.feature_maps     )  ,     len(config.out_features     )     )
					self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,     [self.batch_size, self.hidden_sizes[1], 4, 4]     )

					# verify channels
					self.parent.assertEqual(len(model.channels     )  ,     len(config.out_features     )     )
					self.parent.assertListEqual(model.channels  ,     config.hidden_sizes[1:]     )

					# verify backbone works with out_features=None
					UpperCamelCase__ :List[Any]						=      None
					UpperCamelCase__ :Optional[Any]						=      ConvNextBackbone(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :int						=      model(lowerCamelCase__     )

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps     )  ,     1     )
					self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,     [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

					# verify channels
					self.parent.assertEqual(len(model.channels     )  ,     1     )
					self.parent.assertListEqual(model.channels  ,     [config.hidden_sizes[-1]]     )







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

@require_torch
class        lowerCAmelCase_				(      lowercase						,      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Tuple        =			(
				    (
				        ConvNextModel,
				        ConvNextForImageClassification,
				        ConvNextBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				_snake_case				:   Dict        =			(
				    {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_snake_case				:   Any        =			True
				_snake_case				:   Dict        =			False
				_snake_case				:   Tuple        =			False
				_snake_case				:   str        =			False
				_snake_case				:   Any        =			False







				def 				__a   ( self     :str     ):
					UpperCamelCase__ :List[str]						=      ConvNextModelTester(self     )
					UpperCamelCase__ :Any						=      ConfigTester(self  ,     config_class=lowerCamelCase__  ,     has_text_modality=lowerCamelCase__  ,     hidden_size=37     )







				def 				__a   ( self     :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 				__a   ( self     :List[str]     ):
					return







				@unittest.skip(reason="""ConvNext does not use inputs_embeds"""     )
				def 				__a   ( self     :Dict     ):
					pass







				@unittest.skip(reason="""ConvNext does not support input and output embeddings"""     )
				def 				__a   ( self     :List[str]     ):
					pass







				@unittest.skip(reason="""ConvNext does not use feedforward chunking"""     )
				def 				__a   ( self     :Dict     ):
					pass







				def 				__a   ( self     :Any     ):
					UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase__ :Tuple						=      ["""pixel_values"""]
						self.assertListEqual(arg_names[:1]  ,     lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :int						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase__     )







				def 				__a   ( self     :Any     ):
					UpperCamelCase__ :Dict						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*lowerCamelCase__     )







				def 				__a   ( self     :int     ):
					def check_hidden_states_output(lowerCamelCase__     :Dict  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Dict     ):
						UpperCamelCase__ :List[Any]						=      model_class(lowerCamelCase__     )
						model.to(lowerCamelCase__     )
						model.eval()

						with torch.no_grad():
							UpperCamelCase__ :int						=      model(**self._prepare_for_class(lowerCamelCase__  ,     lowerCamelCase__     )     )

						UpperCamelCase__ :Optional[Any]						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						UpperCamelCase__ :Union[str, Any]						=      self.model_tester.num_stages
						self.assertEqual(len(lowerCamelCase__     )  ,     expected_num_stages + 1     )

						# ConvNext'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 // 4, self.model_tester.image_size // 4]  ,     )

					UpperCamelCase__				,		UpperCamelCase__ :Union[str, Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						UpperCamelCase__ :int						=      True
						check_hidden_states_output(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase__ :Optional[Any]						=      True

						check_hidden_states_output(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :Union[str, Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__     )







				@slow
				def 				__a   ( self     :Any     ):
					for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase__ :str						=      ConvNextModel.from_pretrained(lowerCamelCase__     )
						self.assertIsNotNone(lowerCamelCase__     )




def 				A		(					)		-> str:
	UpperCamelCase__ :Any						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
	return image

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







				"""simple docstring"""




				@cached_property
				def 				__a   ( self     :Optional[int]     ):
					return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""     ) if is_vision_available() else None







				@slow
				def 				__a   ( self     :Tuple     ):
					UpperCamelCase__ :Tuple						=      ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""     ).to(lowerCamelCase__     )

					UpperCamelCase__ :Optional[Any]						=      self.default_image_processor
					UpperCamelCase__ :str						=      prepare_img()
					UpperCamelCase__ :Dict						=      image_processor(images=lowerCamelCase__  ,     return_tensors="""pt"""     ).to(lowerCamelCase__     )

					# forward pass
					with torch.no_grad():
						UpperCamelCase__ :int						=      model(**lowerCamelCase__     )

					# verify the logits
					UpperCamelCase__ :Any						=      torch.Size((1, 10_00)     )
					self.assertEqual(outputs.logits.shape  ,     lowerCamelCase__     )

					UpperCamelCase__ :List[Any]						=      torch.tensor([-0.0260, -0.4739, 0.1911]     ).to(lowerCamelCase__     )

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

@require_torch
class        lowerCAmelCase_				(      unittest.TestCase						,      lowercase	):







				"""simple docstring"""




				_snake_case				:   List[Any]        =			(ConvNextBackbone,) if is_torch_available() else ()
				_snake_case				:   Dict        =			ConvNextConfig

				_snake_case				:   Any        =			False







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Union[str, Any]						=      ConvNextModelTester(self     )






"""simple docstring"""






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  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	str		 =			["[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
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

							a__  :	int		 =			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(a_    ) + "\n"    )

							a__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			os.path.join(self.tmpdirname	, a_    )
							with open(self.image_processor_file	, "w"	, encoding="utf-8"    ) as fp:
										json.dump(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

							a__  :	List[str]		 =			Image.fromarray(np.moveaxis(a_	, 0	, -1    )    )

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_tokenizer()
							a__  :	Any		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			MgpstrProcessor.from_pretrained(self.tmpdirname	, use_fast=a_    )

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

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_tokenizer()
							a__  :	Tuple		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=a_	, padding_value=1.0    )

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

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.get_image_processor()
							a__  :	Union[str, Any]		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			processor(images=a_	, 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       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_image_processor()
							a__  :	List[Any]		 =			self.get_tokenizer()

							a__  :	int		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			"test"
							a__  :	str		 =			self.prepare_image_inputs()

							a__  :	Any		 =			processor(text=a_	, images=a_    )

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

							# test if it raises when no input is passed
							with pytest.raises(a_    ):
										processor()
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

							a__  :	Dict		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	Optional[Any]		 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a__  :	Any		 =			processor.char_decode(a_    )
							a__  :	str		 =			tokenizer.batch_decode(a_    )
							a__  :	Union[str, Any]		 =			[seq.replace(" "	, ""    ) for seq in decoded_tok]

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.get_image_processor()
							a__  :	Any		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

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






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



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

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