'''simple docstring'''


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

     if is_vision_available():
          from transformers import MaskaFormerImageProcessor

if is_vision_available():
     from PIL import Image






class 						A   :





       def __init__(      self				:							Dict     ,		__a				:							Optional[int]     ,		__a				:							List[Any]=2     ,		__a				:							Any=True     ,		__a				:							str=False     ,		__a				:							Tuple=1_0     ,		__a				:							str=3     ,		__a				:							Optional[int]=3_2 * 8     ,		__a				:							List[str]=3_2 * 8     ,		__a				:							Union[str, Any]=4     ,		__a				:							Dict=6_4     ,		)							->	Optional[int]:
        __UpperCAmelCase							=			parent
        __UpperCAmelCase							=			batch_size
        __UpperCAmelCase							=			is_training
        __UpperCAmelCase							=			use_auxiliary_loss
        __UpperCAmelCase							=			num_queries
        __UpperCAmelCase							=			num_channels
        __UpperCAmelCase							=			min_size
        __UpperCAmelCase							=			max_size
        __UpperCAmelCase							=			num_labels
        __UpperCAmelCase							=			hidden_dim
        __UpperCAmelCase							=			hidden_dim





       def       snake_case__						(      self				:							Optional[Any]	)							->	List[Any]:
        __UpperCAmelCase							=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
            __a	)

        __UpperCAmelCase							=			torch.ones([self.batch_size, self.min_size, self.max_size]     ,		device=__a	)

        __UpperCAmelCase							=			(
            torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,		device=__a	) > 0.5
        ).float()
        __UpperCAmelCase							=			(torch.rand((self.batch_size, self.num_labels)     ,		device=__a	) > 0.5).long()

        __UpperCAmelCase							=			self.get_config()
        return config, pixel_values, pixel_mask, mask_labels, class_labels





       def       snake_case__						(      self				:							List[str]	)							->	Optional[Any]:
        __UpperCAmelCase							=			MaskaFormerConfig(
            hidden_size=self.hidden_dim     ,		)
        __UpperCAmelCase							=			self.num_queries
        __UpperCAmelCase							=			self.num_labels

        __UpperCAmelCase							=			[1, 1, 1, 1]
        __UpperCAmelCase							=			self.num_channels

        __UpperCAmelCase							=			6_4
        __UpperCAmelCase							=			1_2_8
        __UpperCAmelCase							=			self.hidden_dim
        __UpperCAmelCase							=			self.hidden_dim
        __UpperCAmelCase							=			self.hidden_dim
        return config





       def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
        __UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			self.prepare_config_and_inputs()
        __UpperCAmelCase							=			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
        return config, inputs_dict





       def       snake_case__						(      self				:							Tuple     ,		__a				:							Optional[int]     ,		__a				:							Dict	)							->	Union[str, Any]:
        __UpperCAmelCase							=			output.encoder_hidden_states
        __UpperCAmelCase							=			output.pixel_decoder_hidden_states
        __UpperCAmelCase							=			output.transformer_decoder_hidden_states

        self.parent.assertTrue(len(__a	)     ,		len(config.backbone_config.depths	)	)
        self.parent.assertTrue(len(__a	)     ,		len(config.backbone_config.depths	)	)
        self.parent.assertTrue(len(__a	)     ,		config.decoder_layers	)





       def       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							Optional[Any]     ,		__a				:							Dict     ,		__a				:							Dict=False	)							->	str:
        with torch.no_grad():
         __UpperCAmelCase							=			MaskaFormerModel(config=__a	)
         model.to(__a	)
         model.eval()

         __UpperCAmelCase							=			model(pixel_values=__a     ,		pixel_mask=__a	)
         __UpperCAmelCase							=			model(__a     ,		output_hidden_states=__a	)

        self.parent.assertEqual(
            output.transformer_decoder_last_hidden_state.shape     ,		(self.batch_size, self.num_queries, self.hidden_dim)     ,		)
        # let's ensure the other two hidden state exists
        self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None	)
        self.parent.assertTrue(output.encoder_last_hidden_state is not None	)

        if output_hidden_states:
         self.check_output_hidden_state(__a     ,		__a	)






       def       snake_case__						(      self				:							Optional[int]     ,		__a				:							str     ,		__a				:							List[Any]     ,		__a				:							Dict     ,		__a				:							Any     ,		__a				:							Optional[int]	)							->	List[Any]:
        __UpperCAmelCase							=			MaskaFormerForUniversalSegmentation(config=__a	)
        model.to(__a	)
        model.eval()

        def comm_check_on_output(__a				:							List[str]	):
         # let's still check that all the required stuff is there
         self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None	)
         self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None	)
         self.parent.assertTrue(result.encoder_last_hidden_state is not None	)
         # okay, now we need to check the logits shape
         # due to the encoder compression, masks have a //4 spatial size
         self.parent.assertEqual(
             result.masks_queries_logits.shape     ,		(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,		)
         # + 1 for null class
         self.parent.assertEqual(
             result.class_queries_logits.shape     ,		(self.batch_size, self.num_queries, self.num_labels + 1)	)

        with torch.no_grad():
         __UpperCAmelCase							=			model(pixel_values=__a     ,		pixel_mask=__a	)
         __UpperCAmelCase							=			model(__a	)

         comm_check_on_output(__a	)

         __UpperCAmelCase							=			model(
             pixel_values=__a     ,		pixel_mask=__a     ,		mask_labels=__a     ,		class_labels=__a	)

        comm_check_on_output(__a	)

        self.parent.assertTrue(result.loss is not None	)
        self.parent.assertEqual(result.loss.shape     ,		torch.Size([1]	)	)






@require_torch
class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
       a_        =      (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
       a_        =      {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

       a_        =      False
       a_        =      False
       a_        =      False
       a_        =      False





       def       snake_case__						(      self				:							str	)							->	List[str]:
        __UpperCAmelCase							=			MaskaFormerModelTester(self	)
        __UpperCAmelCase							=			ConfigTester(self     ,		config_class=__a     ,		has_text_modality=__a	)





       def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
        self.config_tester.run_common_tests()





       def       snake_case__						(      self				:							Optional[int]	)							->	Tuple:
        __UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__a     ,		**__a     ,		output_hidden_states=__a	)





       def       snake_case__						(      self				:							Optional[Any]	)							->	Any:
        __UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__a	)





       @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''	)
       def       snake_case__						(      self				:							List[Any]	)							->	Dict:
        pass





       @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''	)
       def       snake_case__						(      self				:							str	)							->	Any:
        pass





       @unittest.skip(reason='''Mask2Former is not a generative model'''	)
       def       snake_case__						(      self				:							Optional[int]	)							->	Tuple:
        pass





       @unittest.skip(reason='''Mask2Former does not use token embeddings'''	)
       def       snake_case__						(      self				:							List[str]	)							->	Any:
        pass





       @require_torch_multi_gpu
       @unittest.skip(
           reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
       def       snake_case__						(      self				:							List[str]	)							->	Tuple:
        pass





       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
       def       snake_case__						(      self				:							Optional[int]	)							->	Any:
        pass





       def       snake_case__						(      self				:							Dict	)							->	int:
        __UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

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

         __UpperCAmelCase							=			['''pixel_values''']
         self.assertListEqual(arg_names[:1]     ,		__a	)





       @slow
       def       snake_case__						(      self				:							Tuple	)							->	Any:
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
         __UpperCAmelCase							=			MaskaFormerModel.from_pretrained(__a	)
         self.assertIsNotNone(__a	)





       def       snake_case__						(      self				:							Union[str, Any]	)							->	int:
        __UpperCAmelCase							=			(self.model_tester.min_size,) * 2
        __UpperCAmelCase							=			{
            '''pixel_values''': torch.randn((2, 3, *size)     ,		device=__a	),
            '''mask_labels''': torch.randn((2, 1_0, *size)     ,		device=__a	),
            '''class_labels''': torch.zeros(2     ,		1_0     ,		device=__a	).long(),
        }
        __UpperCAmelCase							=			self.model_tester.get_config()

        __UpperCAmelCase							=			MaskaFormerForUniversalSegmentation(__a	).to(__a	)
        __UpperCAmelCase							=			model(**__a	)
        self.assertTrue(outputs.loss is not None	)





       def       snake_case__						(      self				:							Optional[int]	)							->	int:
        __UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.create_and_check_maskaformer_model(__a     ,		**__a     ,		output_hidden_states=__a	)





       def       snake_case__						(      self				:							Optional[int]	)							->	Tuple:
        __UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         __UpperCAmelCase							=			model_class(__a	).to(__a	)
         __UpperCAmelCase							=			model(**__a     ,		output_attentions=__a	)
         self.assertTrue(outputs.attentions is not None	)





       def       snake_case__						(      self				:							List[Any]	)							->	Union[str, Any]:
        if not self.model_tester.is_training:
         return

        __UpperCAmelCase							=			self.all_model_classes[1]
        __UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()

        __UpperCAmelCase							=			model_class(__a	)
        model.to(__a	)
        model.train()

        __UpperCAmelCase							=			model(__a     ,		mask_labels=__a     ,		class_labels=__a	).loss
        loss.backward()






       def       snake_case__						(      self				:							str	)							->	Dict:
        __UpperCAmelCase							=			self.all_model_classes[1]
        __UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
        __UpperCAmelCase							=			True
        __UpperCAmelCase							=			True

        __UpperCAmelCase							=			model_class(__a	).to(__a	)
        model.train()

        __UpperCAmelCase							=			model(__a     ,		mask_labels=__a     ,		class_labels=__a	)

        __UpperCAmelCase							=			outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        __UpperCAmelCase							=			outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

        __UpperCAmelCase							=			outputs.transformer_decoder_hidden_states[0]
        transformer_decoder_hidden_states.retain_grad()

        __UpperCAmelCase							=			outputs.attentions[0]
        attentions.retain_grad()

        outputs.loss.backward(retain_graph=__a	)

        self.assertIsNotNone(encoder_hidden_states.grad	)
        self.assertIsNotNone(pixel_decoder_hidden_states.grad	)
        self.assertIsNotNone(transformer_decoder_hidden_states.grad	)
        self.assertIsNotNone(attentions.grad	)

__lowerCAmelCase	:       Dict						       =	1e-4





def       lowerCAmelCase					(		):


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






@require_vision
@slow
class 						A   (   unittest.TestCase   ):





       @cached_property
       def       snake_case__						(      self				:							str	)							->	Optional[int]:
        return "facebook/mask2former-swin-small-coco-instance"





       @cached_property
       def       snake_case__						(      self				:							Optional[int]	)							->	Optional[Any]:
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints	) if is_vision_available() else None





       def       snake_case__						(      self				:							Any	)							->	Optional[Any]:
        __UpperCAmelCase							=			MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(__a	)
        __UpperCAmelCase							=			self.default_image_processor
        __UpperCAmelCase							=			prepare_img()
        __UpperCAmelCase							=			image_processor(__a     ,		return_tensors='''pt'''	).to(__a	)
        __UpperCAmelCase							=			inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
        # check size
        self.assertEqual(__a     ,		(1, 3, 3_8_4, 3_8_4)	)

        with torch.no_grad():
         __UpperCAmelCase							=			model(**__a	)

        __UpperCAmelCase							=			torch.tensor(
            [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]	).to(__a	)
        self.assertTrue(
            torch.allclose(
                outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,		__a     ,		atol=__a	)	)

        __UpperCAmelCase							=			torch.tensor(
            [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]	).to(__a	)
        self.assertTrue(
            torch.allclose(
                outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,		__a     ,		atol=__a	)	)

        __UpperCAmelCase							=			torch.tensor(
            [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]	).to(__a	)
        self.assertTrue(
            torch.allclose(
                outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,		__a     ,		atol=__a	)	)





       def       snake_case__						(      self				:							List[str]	)							->	Any:
        __UpperCAmelCase							=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(__a	).eval()
        __UpperCAmelCase							=			self.default_image_processor
        __UpperCAmelCase							=			prepare_img()
        __UpperCAmelCase							=			image_processor(__a     ,		return_tensors='''pt'''	).to(__a	)
        __UpperCAmelCase							=			inputs['''pixel_values'''].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0	)
        # check size
        self.assertEqual(__a     ,		(1, 3, 3_8_4, 3_8_4)	)

        with torch.no_grad():
         __UpperCAmelCase							=			model(**__a	)
        # masks_queries_logits
        __UpperCAmelCase							=			outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape     ,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
        __UpperCAmelCase							=			[
            [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
            [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
            [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
        ]
        __UpperCAmelCase							=			torch.tensor(__a	).to(__a	)
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,		__a     ,		atol=__a	)	)
        # class_queries_logits
        __UpperCAmelCase							=			outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape     ,		(1, model.config.num_queries, model.config.num_labels + 1)	)
        __UpperCAmelCase							=			torch.tensor(
            [
                [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
                [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
                [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
            ]	).to(__a	)
        self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,		__a     ,		atol=__a	)	)






       def       snake_case__						(      self				:							Optional[int]	)							->	str:
        __UpperCAmelCase							=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(__a	).eval()
        __UpperCAmelCase							=			self.default_image_processor

        __UpperCAmelCase							=			image_processor(
            [np.zeros((3, 8_0_0, 1_3_3_3)	), np.zeros((3, 8_0_0, 1_3_3_3)	)]     ,		segmentation_maps=[np.zeros((3_8_4, 3_8_4)	).astype(np.floataa	), np.zeros((3_8_4, 3_8_4)	).astype(np.floataa	)]     ,		return_tensors='''pt'''     ,		)

        __UpperCAmelCase							=			inputs['''pixel_values'''].to(__a	)
        __UpperCAmelCase							=			[el.to(__a	) for el in inputs['''mask_labels''']]
        __UpperCAmelCase							=			[el.to(__a	) for el in inputs['''class_labels''']]

        with torch.no_grad():
         __UpperCAmelCase							=			model(**__a	)

        self.assertTrue(outputs.loss is not None	)

'''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()
__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	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					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	for i in range(encoder_config.num_hidden_layers       ):
		# queries, keys and values (only weights, no biases)
		__UpperCAmelCase							=			state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""       )

		__UpperCAmelCase							=			in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__UpperCAmelCase							=			in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__UpperCAmelCase							=			in_proj_weight[
		    -encoder_config.hidden_size :, :
		]





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			dct.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	if "handwritten" in checkpoint_url:
		__UpperCAmelCase							=			'''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:
		__UpperCAmelCase							=			'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
	__UpperCAmelCase							=			Image.open(requests.get(UpperCamelCase__    ,					stream=UpperCamelCase__       ).raw       ).convert('''RGB'''       )
	return im





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ViTConfig(image_size=3_8_4    ,					qkv_bias=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__UpperCAmelCase							=			7_6_8
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			4_0_9_6
		__UpperCAmelCase							=			2_4
		__UpperCAmelCase							=			1_6
		__UpperCAmelCase							=			1_0_2_4
	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:
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			'''relu'''
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			True
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			False

	# load HuggingFace model
	__UpperCAmelCase							=			ViTModel(UpperCamelCase__    ,					add_pooling_layer=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRForCausalLM(UpperCamelCase__       )
	__UpperCAmelCase							=			VisionEncoderDecoderModel(encoder=UpperCamelCase__    ,					decoder=UpperCamelCase__       )
	model.eval()

	# load state_dict of original model, rename some keys
	__UpperCAmelCase							=			torch.hub.load_state_dict_from_url(UpperCamelCase__    ,					map_location='''cpu'''    ,					check_hash=UpperCamelCase__       )['''model''']

	__UpperCAmelCase							=			create_rename_keys(UpperCamelCase__    ,					UpperCamelCase__       )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	read_in_q_k_v(UpperCamelCase__    ,					UpperCamelCase__       )

	# 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():
		__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
		if key.startswith('''decoder'''       ) and "output_projection" not in key:
			__UpperCAmelCase							=			val
		else:
			__UpperCAmelCase							=			val

    # load state dict
	model.load_state_dict(UpperCamelCase__       )

	# Check outputs on an image
	__UpperCAmelCase							=			ViTImageProcessor(size=encoder_config.image_size       )
	__UpperCAmelCase							=			RobertaTokenizer.from_pretrained('''roberta-large'''       )
	__UpperCAmelCase							=			TrOCRProcessor(UpperCamelCase__    ,					UpperCamelCase__       )

	__UpperCAmelCase							=			processor(images=prepare_img(UpperCamelCase__       )    ,					return_tensors='''pt'''       ).pixel_values

	# verify logits
	__UpperCAmelCase							=			torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
	__UpperCAmelCase							=			model(pixel_values=UpperCamelCase__    ,					decoder_input_ids=UpperCamelCase__       )
	__UpperCAmelCase							=			outputs.logits

	__UpperCAmelCase							=			torch.Size([1, 1, 5_0_2_6_5]       )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]       )
	elif "trocr-large-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]       )
	elif "trocr-base-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]       )
	elif "trocr-large-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]       )

	if "stage1" not in checkpoint_url:
		assert logits.shape == expected_shape, "Shape of logits not as expected"
		assert torch.allclose(logits[0, 0, :1_0]    ,					UpperCamelCase__    ,					atol=1E-3       ), "First elements of logits not as expected"

	Path(UpperCamelCase__       ).mkdir(exist_ok=UpperCamelCase__       )
	print(f"""Saving model to {pytorch_dump_folder_path}"""       )
	model.save_pretrained(UpperCamelCase__       )
	print(f"""Saving processor to {pytorch_dump_folder_path}"""       )
	processor.save_pretrained(UpperCamelCase__       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Any						       =	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."
					)
					__lowerCAmelCase	:       Optional[int]						       =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
