





"""simple docstring"""





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _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 (
							    MODEL_MAPPING,
							    BeitForImageClassification,
							    BeitForMaskedImageModeling,
							    BeitForSemanticSegmentation,
							    BeitModel,
							)
							from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							import PIL
							from PIL import Image

							from transformers import BeitImageProcessor
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :List[str]						,    __lowercase  :Any						,    __lowercase  :str=100						,    __lowercase  :Dict=13						,    __lowercase  :Tuple=30						,    __lowercase  :Tuple=2						,    __lowercase  :Dict=3						,    __lowercase  :Optional[Any]=True						,    __lowercase  :int=True						,    __lowercase  :List[Any]=32						,    __lowercase  :str=4						,    __lowercase  :int=4						,    __lowercase  :List[str]=37						,    __lowercase  :Dict="gelu"						,    __lowercase  :List[Any]=0.1						,    __lowercase  :Tuple=0.1						,    __lowercase  :Union[str, Any]=10						,    __lowercase  :Any=0.02						,    __lowercase  :Tuple=3						,    __lowercase  :Any=None						,    __lowercase  :Optional[int]=[0, 1, 2, 3]						,    ):
											__lowerCamelCase      :      int         =parent
											__lowerCamelCase      :      Optional[Any]         =100
											__lowerCamelCase      :      Optional[int]         =batch_size
											__lowerCamelCase      :      Tuple         =image_size
											__lowerCamelCase      :      Tuple         =patch_size
											__lowerCamelCase      :      Optional[Any]         =num_channels
											__lowerCamelCase      :      Union[str, Any]         =is_training
											__lowerCamelCase      :      int         =use_labels
											__lowerCamelCase      :      Optional[int]         =hidden_size
											__lowerCamelCase      :      Optional[Any]         =num_hidden_layers
											__lowerCamelCase      :      List[Any]         =num_attention_heads
											__lowerCamelCase      :      Any         =intermediate_size
											__lowerCamelCase      :      int         =hidden_act
											__lowerCamelCase      :      Tuple         =hidden_dropout_prob
											__lowerCamelCase      :      List[Any]         =attention_probs_dropout_prob
											__lowerCamelCase      :      Union[str, Any]         =type_sequence_label_size
											__lowerCamelCase      :      List[str]         =initializer_range
											__lowerCamelCase      :      Tuple         =scope
											__lowerCamelCase      :      Union[str, Any]         =out_indices
											__lowerCamelCase      :      Tuple         =num_labels

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											__lowerCamelCase      :      Optional[int]         =(image_size // patch_size) ** 2
											__lowerCamelCase      :      Optional[Any]         =num_patches + 1




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Dict         =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

											__lowerCamelCase      :      Any         =None
											__lowerCamelCase      :      str         =None
											if self.use_labels:
																		__lowerCamelCase      :      Dict         =ids_tensor([self.batch_size]						,    self.type_sequence_label_size			)
																		__lowerCamelCase      :      List[Any]         =ids_tensor([self.batch_size, self.image_size, self.image_size]						,    self.num_labels			)

											__lowerCamelCase      :      List[str]         =self.get_config()

											return config, pixel_values, labels, pixel_labels




				def 					__lowercase  (     self  :Tuple			):
											return BeitConfig(
											    vocab_size=self.vocab_size						,    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						,    is_decoder=__lowercase						,    initializer_range=self.initializer_range						,    out_indices=self.out_indices						,    )




				def 					__lowercase  (     self  :Any						,    __lowercase  :List[Any]						,    __lowercase  :str						,    __lowercase  :Union[str, Any]						,    __lowercase  :Dict			):
											__lowerCamelCase      :      Tuple         =BeitModel(config=__lowercase			)
											model.to(__lowercase			)
											model.eval()
											__lowerCamelCase      :      int         =model(__lowercase			)
											self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Dict						,    __lowercase  :Tuple						,    __lowercase  :Optional[Any]						,    __lowercase  :Any			):
											__lowerCamelCase      :      List[str]         =BeitForMaskedImageModeling(config=__lowercase			)
											model.to(__lowercase			)
											model.eval()
											__lowerCamelCase      :      int         =model(__lowercase			)
											self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length - 1, self.vocab_size)			)




				def 					__lowercase  (     self  :Dict						,    __lowercase  :str						,    __lowercase  :Tuple						,    __lowercase  :int						,    __lowercase  :str			):
											__lowerCamelCase      :      int         =self.type_sequence_label_size
											__lowerCamelCase      :      Optional[Any]         =BeitForImageClassification(__lowercase			)
											model.to(__lowercase			)
											model.eval()
											__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    labels=__lowercase			)
											self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.type_sequence_label_size)			)

											# test greyscale images
											__lowerCamelCase      :      int         =1
											__lowerCamelCase      :      str         =BeitForImageClassification(__lowercase			)
											model.to(__lowercase			)
											model.eval()

											__lowerCamelCase      :      Tuple         =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											__lowerCamelCase      :      str         =model(__lowercase						,    labels=__lowercase			)
											self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.type_sequence_label_size)			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :Dict						,    __lowercase  :List[str]						,    __lowercase  :int						,    __lowercase  :str			):
											__lowerCamelCase      :      Union[str, Any]         =self.num_labels
											__lowerCamelCase      :      Any         =BeitForSemanticSegmentation(__lowercase			)
											model.to(__lowercase			)
											model.eval()
											__lowerCamelCase      :      Any         =model(__lowercase			)
											self.parent.assertEqual(
											    result.logits.shape						,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
											__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    labels=__lowercase			)
											self.parent.assertEqual(
											    result.logits.shape						,    (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Optional[int]         =self.prepare_config_and_inputs()
											__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase      :      str         =config_and_inputs
											__lowerCamelCase      :      List[Any]         ={'''pixel_values''': pixel_values}
											return config, inputs_dict

@require_torch
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       List[Any]	 =		(
				    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				__snake_case   :       Any	 =		(
				    {
				        """feature-extraction""": BeitModel,
				        """image-classification""": BeitForImageClassification,
				        """image-segmentation""": BeitForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__snake_case   :       Union[str, Any]	 =		False
				__snake_case   :       Optional[Any]	 =		False
				__snake_case   :       str	 =		False




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Optional[Any]         =BeitModelTester(self			)
											__lowerCamelCase      :      Optional[int]         =ConfigTester(self						,    config_class=__lowercase						,    has_text_modality=__lowercase						,    hidden_size=37			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											self.config_tester.run_common_tests()




				@unittest.skip(reason='''BEiT does not use inputs_embeds'''			)
				def 					__lowercase  (     self  :List[str]			):
											pass




				@require_torch_multi_gpu
				@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											pass




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		__lowerCamelCase      :      Optional[int]         =model_class(__lowercase			)
																		self.assertIsInstance(model.get_input_embeddings()						,    (nn.Module)			)
																		__lowerCamelCase      :      Union[str, Any]         =model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(__lowercase						,    nn.Linear			)			)




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase    ,						__lowerCamelCase      :      List[Any]         =self.model_tester.prepare_config_and_inputs_for_common()

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

																		__lowerCamelCase      :      List[Any]         =['''pixel_values''']
																		self.assertListEqual(arg_names[:1]						,    __lowercase			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      Optional[Any]         =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      int         =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__lowercase			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      str         =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Any         =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase			)




				def 					__lowercase  (     self  :str			):
											if not self.model_tester.is_training:
																		return

											__lowerCamelCase    ,						__lowerCamelCase      :      Any         =self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase      :      Optional[Any]         =True

											for model_class in self.all_model_classes:
																		# we don't test BeitForMaskedImageModeling
																		if model_class in [*get_values(__lowercase			), BeitForMaskedImageModeling]:
																									continue

																		__lowerCamelCase      :      Any         =model_class(__lowercase			)
																		model.to(__lowercase			)
																		model.train()
																		__lowerCamelCase      :      Dict         =self._prepare_for_class(__lowercase						,    __lowercase						,    return_labels=__lowercase			)
																		__lowerCamelCase      :      List[Any]         =model(**__lowercase			).loss
																		loss.backward()




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																		return

											__lowerCamelCase      :      str         =False
											__lowerCamelCase      :      int         =True

											for model_class in self.all_model_classes:
																		# we don't test BeitForMaskedImageModeling
																		if (
																		    model_class in [*get_values(__lowercase			), BeitForMaskedImageModeling]
																		    or not model_class.supports_gradient_checkpointing
																		):
																									continue

																		__lowerCamelCase      :      Any         =model_class(__lowercase			)
																		model.gradient_checkpointing_enable()
																		model.to(__lowercase			)
																		model.train()
																		__lowerCamelCase      :      Any         =self._prepare_for_class(__lowercase						,    __lowercase						,    return_labels=__lowercase			)
																		__lowerCamelCase      :      Union[str, Any]         =model(**__lowercase			).loss
																		loss.backward()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase    ,						__lowerCamelCase      :      str         =self.model_tester.prepare_config_and_inputs_for_common()

											__lowerCamelCase      :      Tuple         =_config_zero_init(__lowercase			)
											for model_class in self.all_model_classes:
																		__lowerCamelCase      :      List[Any]         =model_class(config=__lowercase			)
																		for name, param in model.named_parameters():
																									# we skip lambda parameters as these require special initial values
																									# determined by config.layer_scale_init_value
																									if "lambda" in name:
																																continue
																									if 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'						,    )




				@slow
				def 					__lowercase  (     self  :int			):
											for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase      :      str         =BeitModel.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
							return image
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				@cached_property
				def 					__lowercase  (     self  :Optional[Any]			):
											return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''			) if is_vision_available() else None




				@slow
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Union[str, Any]         =BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''			).to(__lowercase			)

											__lowerCamelCase      :      Union[str, Any]         =self.default_image_processor
											__lowerCamelCase      :      List[str]         =prepare_img()
											__lowerCamelCase      :      Tuple         =image_processor(images=__lowercase						,    return_tensors='''pt'''			).pixel_values.to(__lowercase			)

											# prepare bool_masked_pos
											__lowerCamelCase      :      Dict         =torch.ones((1, 196)						,    dtype=torch.bool			).to(__lowercase			)

											# forward pass
											with torch.no_grad():
																		__lowerCamelCase      :      List[Any]         =model(pixel_values=__lowercase						,    bool_masked_pos=__lowercase			)
											__lowerCamelCase      :      List[Any]         =outputs.logits

											# verify the logits
											__lowerCamelCase      :      str         =torch.Size((1, 196, 8192)			)
											self.assertEqual(logits.shape						,    __lowercase			)

											__lowerCamelCase      :      Tuple         =torch.tensor(
											    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]			).to(__lowercase			)

											self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]						,    __lowercase						,    atol=1e-2			)			)




				@slow
				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Optional[int]         =BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''			).to(__lowercase			)

											__lowerCamelCase      :      str         =self.default_image_processor
											__lowerCamelCase      :      List[Any]         =prepare_img()
											__lowerCamelCase      :      List[Any]         =image_processor(images=__lowercase						,    return_tensors='''pt'''			).to(__lowercase			)

											# forward pass
											with torch.no_grad():
																		__lowerCamelCase      :      int         =model(**__lowercase			)
											__lowerCamelCase      :      List[Any]         =outputs.logits

											# verify the logits
											__lowerCamelCase      :      Union[str, Any]         =torch.Size((1, 1000)			)
											self.assertEqual(logits.shape						,    __lowercase			)

											__lowerCamelCase      :      Optional[Any]         =torch.tensor([-1.2385, -1.0987, -1.0108]			).to(__lowercase			)

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

											__lowerCamelCase      :      str         =281
											self.assertEqual(logits.argmax(-1			).item()						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[int]         =BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''			).to(
											    __lowercase			)

											__lowerCamelCase      :      str         =self.default_image_processor
											__lowerCamelCase      :      str         =prepare_img()
											__lowerCamelCase      :      int         =image_processor(images=__lowercase						,    return_tensors='''pt'''			).to(__lowercase			)

											# forward pass
											with torch.no_grad():
																		__lowerCamelCase      :      str         =model(**__lowercase			)
											__lowerCamelCase      :      int         =outputs.logits

											# verify the logits
											__lowerCamelCase      :      Dict         =torch.Size((1, 2_1841)			)
											self.assertEqual(logits.shape						,    __lowercase			)

											__lowerCamelCase      :      List[Any]         =torch.tensor([1.6881, -0.2787, 0.5901]			).to(__lowercase			)

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

											__lowerCamelCase      :      List[Any]         =2396
											self.assertEqual(logits.argmax(-1			).item()						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Tuple         =BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''			)
											__lowerCamelCase      :      int         =model.to(__lowercase			)

											__lowerCamelCase      :      Tuple         =BeitImageProcessor(do_resize=__lowercase						,    size=640						,    do_center_crop=__lowercase			)

											__lowerCamelCase      :      Optional[Any]         =load_dataset('''hf-internal-testing/fixtures_ade20k'''						,    split='''test'''			)
											__lowerCamelCase      :      Optional[int]         =Image.open(ds[0]['''file''']			)
											__lowerCamelCase      :      Union[str, Any]         =image_processor(images=__lowercase						,    return_tensors='''pt'''			).to(__lowercase			)

											# forward pass
											with torch.no_grad():
																		__lowerCamelCase      :      List[Any]         =model(**__lowercase			)
											__lowerCamelCase      :      Tuple         =outputs.logits

											# verify the logits
											__lowerCamelCase      :      Optional[Any]         =torch.Size((1, 150, 160, 160)			)
											self.assertEqual(logits.shape						,    __lowercase			)

											__lowerCamelCase      :      List[Any]         =version.parse(PIL.__version__			) < version.parse('''9.0.0'''			)

											if is_pillow_less_than_a:
																		__lowerCamelCase      :      Any         =torch.tensor(
																		    [
																		        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
																		        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
																		        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
																		    ]						,    device=__lowercase						,    )
											else:
																		__lowerCamelCase      :      int         =torch.tensor(
																		    [
																		        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
																		        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
																		        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
																		    ]						,    device=__lowercase						,    )

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




				@slow
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Any         =BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''			)
											__lowerCamelCase      :      Union[str, Any]         =model.to(__lowercase			)

											__lowerCamelCase      :      List[str]         =BeitImageProcessor(do_resize=__lowercase						,    size=640						,    do_center_crop=__lowercase			)

											__lowerCamelCase      :      List[str]         =load_dataset('''hf-internal-testing/fixtures_ade20k'''						,    split='''test'''			)
											__lowerCamelCase      :      Optional[Any]         =Image.open(ds[0]['''file''']			)
											__lowerCamelCase      :      List[Any]         =image_processor(images=__lowercase						,    return_tensors='''pt'''			).to(__lowercase			)

											# forward pass
											with torch.no_grad():
																		__lowerCamelCase      :      Tuple         =model(**__lowercase			)

											__lowerCamelCase      :      str         =outputs.logits.detach().cpu()

											__lowerCamelCase      :      Union[str, Any]         =image_processor.post_process_semantic_segmentation(outputs=__lowercase						,    target_sizes=[(500, 300)]			)
											__lowerCamelCase      :      Union[str, Any]         =torch.Size((500, 300)			)
											self.assertEqual(segmentation[0].shape						,    __lowercase			)

											__lowerCamelCase      :      Optional[Any]         =image_processor.post_process_semantic_segmentation(outputs=__lowercase			)
											__lowerCamelCase      :      List[str]         =torch.Size((160, 160)			)
											self.assertEqual(segmentation[0].shape						,    __lowercase			)











"""simple docstring"""





import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							List[str]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =[]
							for i in range(encoder_config.num_hidden_layers   ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight')   )
														rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias')   )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight')   )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias')   )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight')   )
														rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias')   )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight')   )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias')   )
														rename_keys.append(
														    (F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight')   )
														rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias')   )

							# cls token, position embeddings and patch embeddings of encoder
							rename_keys.extend(
							    [
							        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
							        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
							        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
							        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
							        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
							        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
							    ]   )

							return rename_keys



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							for i in range(encoder_config.num_hidden_layers   ):
														# queries, keys and values (only weights, no biases)
														__lowerCamelCase      :      str         =state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight'   )

														__lowerCamelCase      :      Dict         =in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														__lowerCamelCase      :      Any         =in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														__lowerCamelCase      :      Dict         =in_proj_weight[
														    -encoder_config.hidden_size :, :
														]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =dct.pop(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =val



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							if "handwritten" in checkpoint_url:
														__lowerCamelCase      :      List[Any]         ='''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
														# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
							elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
														__lowerCamelCase      :      List[Any]         ='''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
							__lowerCamelCase      :      int         =Image.open(requests.get(SCREAMING_SNAKE_CASE    ,						stream=SCREAMING_SNAKE_CASE   ).raw   ).convert('''RGB'''   )
							return im



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =ViTConfig(image_size=384    ,						qkv_bias=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														__lowerCamelCase      :      Any         =768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														__lowerCamelCase      :      Optional[int]         =1024
														__lowerCamelCase      :      List[Any]         =4096
														__lowerCamelCase      :      Dict         =24
														__lowerCamelCase      :      Optional[Any]         =16
														__lowerCamelCase      :      Union[str, Any]         =1024
							else:
														raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''   )

							# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
							if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
														__lowerCamelCase      :      Optional[int]         =False
														__lowerCamelCase      :      int         ='''relu'''
														__lowerCamelCase      :      Any         =1024
														__lowerCamelCase      :      str         =True
														__lowerCamelCase      :      List[Any]         =False
														__lowerCamelCase      :      Optional[int]         =False

							# load HuggingFace model
							__lowerCamelCase      :      Dict         =ViTModel(SCREAMING_SNAKE_CASE    ,						add_pooling_layer=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Any         =TrOCRForCausalLM(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      List[Any]         =VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE    ,						decoder=SCREAMING_SNAKE_CASE   )
							model.eval()

							# load state_dict of original model, rename some keys
							__lowerCamelCase      :      Optional[Any]         =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE    ,						map_location='''cpu'''    ,						check_hash=SCREAMING_SNAKE_CASE   )['''model''']

							__lowerCamelCase      :      Optional[int]         =create_rename_keys(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							read_in_q_k_v(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# remove parameters we don't need
							del state_dict["encoder.deit.head.weight"]
							del state_dict["encoder.deit.head.bias"]
							del state_dict["decoder.version"]

							# add prefix to decoder keys
							for key, val in state_dict.copy().items():
														__lowerCamelCase      :      List[Any]         =state_dict.pop(SCREAMING_SNAKE_CASE   )
														if key.startswith('''decoder'''   ) and "output_projection" not in key:
																					__lowerCamelCase      :      List[Any]         =val
														else:
																					__lowerCamelCase      :      Optional[Any]         =val

    # load state dict
							model.load_state_dict(SCREAMING_SNAKE_CASE   )

							# Check outputs on an image
							__lowerCamelCase      :      int         =ViTImageProcessor(size=encoder_config.image_size   )
							__lowerCamelCase      :      Dict         =RobertaTokenizer.from_pretrained('''roberta-large'''   )
							__lowerCamelCase      :      str         =TrOCRProcessor(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      str         =processor(images=prepare_img(SCREAMING_SNAKE_CASE   )    ,						return_tensors='''pt'''   ).pixel_values

							# verify logits
							__lowerCamelCase      :      List[Any]         =torch.tensor([[model.config.decoder.decoder_start_token_id]]   )
							__lowerCamelCase      :      Optional[int]         =model(pixel_values=SCREAMING_SNAKE_CASE    ,						decoder_input_ids=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =outputs.logits

							__lowerCamelCase      :      Optional[Any]         =torch.Size([1, 1, 50265]   )
							if "trocr-base-handwritten" in checkpoint_url:
														__lowerCamelCase      :      List[str]         =torch.tensor(
														    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]   )
							elif "trocr-large-handwritten" in checkpoint_url:
														__lowerCamelCase      :      Any         =torch.tensor(
														    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]   )
							elif "trocr-base-printed" in checkpoint_url:
														__lowerCamelCase      :      Optional[Any]         =torch.tensor(
														    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]   )
							elif "trocr-large-printed" in checkpoint_url:
														__lowerCamelCase      :      Optional[Any]         =torch.tensor(
														    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]   )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10]    ,						SCREAMING_SNAKE_CASE    ,						atol=1E-3   ), "First elements of logits not as expected"

							Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
							print(F'Saving model to {pytorch_dump_folder_path}'   )
							model.save_pretrained(SCREAMING_SNAKE_CASE   )
							print(F'Saving processor to {pytorch_dump_folder_path}'   )
							processor.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							_UpperCamelCase							=      argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_url',
							    default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
							    type=str,
							    help='URL to the original PyTorch checkpoint (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
							)
							_UpperCamelCase							=      parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




