






'''simple docstring'''







import inspect
import unittest

from transformers import ViTMSNConfig
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_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 ViTMSNForImageClassification, ViTMSNModel
			from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase			:




							def __init__(							self:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Dict=13    ,							UpperCamelCase__:       Tuple=30    ,							UpperCamelCase__:       str=2    ,							UpperCamelCase__:       Optional[Any]=3    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Any=32    ,							UpperCamelCase__:       Any=5    ,							UpperCamelCase__:       Any=4    ,							UpperCamelCase__:       Optional[int]=37    ,							UpperCamelCase__:       str="gelu"    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Tuple=0.1    ,							UpperCamelCase__:       Optional[Any]=10    ,							UpperCamelCase__:       List[Any]=0.02    ,							UpperCamelCase__:       Union[str, Any]=None    ,							):
														lowerCamelCase__      :			List[Any] 						= parent
														lowerCamelCase__      :			List[Any] 						= batch_size
														lowerCamelCase__      :			str 						= image_size
														lowerCamelCase__      :			Union[str, Any] 						= patch_size
														lowerCamelCase__      :			int 						= num_channels
														lowerCamelCase__      :			Dict 						= is_training
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			int 						= hidden_size
														lowerCamelCase__      :			Dict 						= num_hidden_layers
														lowerCamelCase__      :			int 						= num_attention_heads
														lowerCamelCase__      :			str 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			List[str] 						= hidden_dropout_prob
														lowerCamelCase__      :			Tuple 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Union[str, Any] 						= type_sequence_label_size
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			Union[str, Any] 						= scope

														# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														lowerCamelCase__      :			List[str] 						= (image_size // patch_size) ** 2
														lowerCamelCase__      :			Union[str, Any] 						= num_patches + 1




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			List[str] 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCamelCase__      :			str 						= None
														if self.use_labels:
																					lowerCamelCase__      :			List[str] 						= ids_tensor([self.batch_size]    ,							self.type_sequence_label_size  )

														lowerCamelCase__      :			int 						= self.get_config()

														return config, pixel_values, labels




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														return ViTMSNConfig(
														    image_size=self.image_size    ,							patch_size=self.patch_size    ,							num_channels=self.num_channels    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							initializer_range=self.initializer_range    ,							)




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]  ):
														lowerCamelCase__      :			List[str] 						= ViTMSNModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Union[str, Any]  ):
														lowerCamelCase__      :			Dict 						= self.type_sequence_label_size
														lowerCamelCase__      :			int 						= ViTMSNForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""  )
														print("""Labels: {labels}"""  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)  )

														# test greyscale images
														lowerCamelCase__      :			str 						= 1
														lowerCamelCase__      :			Optional[Any] 						= ViTMSNForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()

														lowerCamelCase__      :			List[str] 						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
														lowerCamelCase__      :			List[str] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Any 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= config_and_inputs
														lowerCamelCase__      :			int 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							a          = (
							    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			int 						= ViTMSNModelTester(self  )
														lowerCamelCase__      :			str 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			List[Any] 						= model_class(UpperCamelCase__  )
																					self.assertIsInstance(model.get_input_embeddings()    ,							(nn.Module)  )
																					lowerCamelCase__      :			Optional[Any] 						= model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__    ,							nn.Linear  )  )




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()

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

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




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Any 						= ViTMSNModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[Any]:
							lowerCamelCase__      :			List[Any] 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




							@cached_property
							def 	lowerCamelCase_       (							self:       Dict  ):
														return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""  ) if is_vision_available() else None




							@slow
							def 	lowerCamelCase_       (							self:       str  ):
														torch.manual_seed(2  )
														lowerCamelCase__      :			Dict 						= ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""  ).to(UpperCamelCase__  )

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

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

														# verify the logits
														lowerCamelCase__      :			str 						= torch.Size((1, 1_000)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= torch.tensor([-0.0_803, -0.4_454, -0.2_375]  ).to(UpperCamelCase__  )

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









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Any					  =logging.get_logger(__name__)

_A			:    Dict					  ={
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class _lowercase			(       _lowercase	):
							a          = """trocr"""
							a          = ["""past_key_values"""]
							a          = {
							    """num_attention_heads""": """decoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """num_hidden_layers""": """decoder_layers""",
							}




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       int=50_265    ,							UpperCamelCase__:       int=1_024    ,							UpperCamelCase__:       Optional[Any]=12    ,							UpperCamelCase__:       Dict=16    ,							UpperCamelCase__:       int=4_096    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=512    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Tuple=0.0    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Dict=0.02    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       str=True    ,							UpperCamelCase__:       Tuple=False    ,							UpperCamelCase__:       Union[str, Any]=True    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       List[str]=0    ,							UpperCamelCase__:       Union[str, Any]=2    ,							**UpperCamelCase__:       str    ,							):
														lowerCamelCase__      :			Any 						= vocab_size
														lowerCamelCase__      :			Tuple 						= d_model
														lowerCamelCase__      :			Any 						= decoder_layers
														lowerCamelCase__      :			Dict 						= decoder_attention_heads
														lowerCamelCase__      :			str 						= decoder_ffn_dim
														lowerCamelCase__      :			Tuple 						= activation_function
														lowerCamelCase__      :			Dict 						= max_position_embeddings
														lowerCamelCase__      :			int 						= dropout
														lowerCamelCase__      :			int 						= attention_dropout
														lowerCamelCase__      :			List[Any] 						= activation_dropout
														lowerCamelCase__      :			Union[str, Any] 						= init_std
														lowerCamelCase__      :			Optional[int] 						= decoder_layerdrop
														lowerCamelCase__      :			Dict 						= use_cache
														lowerCamelCase__      :			Any 						= scale_embedding
														lowerCamelCase__      :			Optional[int] 						= use_learned_position_embeddings
														lowerCamelCase__      :			List[str] 						= layernorm_embedding

														super().__init__(
														    pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							decoder_start_token_id=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

