





'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
				import jax

				from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor

class     A					(	unittest.TestCase					):







							def __init__(						self :						Optional[int]							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Optional[Any]=1_00							,    lowerCAmelCase_ :						List[str]=13							,    lowerCAmelCase_ :						List[Any]=30							,    lowerCAmelCase_ :						List[Any]=2							,    lowerCAmelCase_ :						Any=3							,    lowerCAmelCase_ :						Any=True							,    lowerCAmelCase_ :						Tuple=True							,    lowerCAmelCase_ :						Optional[Any]=32							,    lowerCAmelCase_ :						List[str]=5							,    lowerCAmelCase_ :						Any=4							,    lowerCAmelCase_ :						Dict=37							,    lowerCAmelCase_ :						str="gelu"							,    lowerCAmelCase_ :						Tuple=0.1							,    lowerCAmelCase_ :						int=0.1							,    lowerCAmelCase_ :						Tuple=10							,    lowerCAmelCase_ :						str=0.0_2							,    lowerCAmelCase_ :						Optional[int]=3							,    )    ->							Tuple:


														"""simple docstring"""


														_a			  =       parent
														_a			  =       vocab_size
														_a			  =       batch_size
														_a			  =       image_size
														_a			  =       patch_size
														_a			  =       num_channels
														_a			  =       is_training
														_a			  =       use_labels
														_a			  =       hidden_size
														_a			  =       num_hidden_layers
														_a			  =       num_attention_heads
														_a			  =       intermediate_size
														_a			  =       hidden_act
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       type_sequence_label_size
														_a			  =       initializer_range

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







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							str:


														"""simple docstring"""


														_a			  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

														_a			  =       None
														if self.use_labels:
																					_a			  =       ids_tensor([self.batch_size]							,    self.type_sequence_label_size      )

														_a			  =       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=lowerCAmelCase_							,    initializer_range=self.initializer_range							,    )

														return config, pixel_values, labels







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Optional[Any]							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Tuple      )    ->							int:


														"""simple docstring"""


														_a			  =       FlaxBeitModel(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						Optional[int]							,    lowerCAmelCase_ :						List[str]      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       FlaxBeitForMaskedImageModeling(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length - 1, self.vocab_size)      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						List[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.type_sequence_label_size
														_a			  =       FlaxBeitForImageClassification(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.type_sequence_label_size)      )

														# test greyscale images
														_a			  =       1
														_a			  =       FlaxBeitForImageClassification(lowerCAmelCase_      )

														_a			  =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
														_a			  =       model(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


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



@require_flax
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       (
							    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
							)







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							None:


														"""simple docstring"""


														_a			  =       FlaxBeitModelTester(self      )
														_a			  =       ConfigTester(self							,    config_class=lowerCAmelCase_							,    has_text_modality=lowerCAmelCase_							,    hidden_size=37      )







							def      __lowerCAmelCase    (						self :						int      )    ->							str:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					_a			  =       ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__      ):
																												_a			  =       self._prepare_for_class(lowerCAmelCase_							,    lowerCAmelCase_      )
																												_a			  =       model_class(lowerCAmelCase_      )

																												@jax.jit
																												def model_jitted(lowerCAmelCase_ :						int							,    **lowerCAmelCase_ :						Dict      ):
																																			return model(pixel_values=lowerCAmelCase_							,    **lowerCAmelCase_      )

																												with self.subTest('''JIT Enabled'''      ):
																																			_a			  =       model_jitted(**lowerCAmelCase_      ).to_tuple()

																												with self.subTest('''JIT Disabled'''      ):
																																			with jax.disable_jit():
																																										_a			  =       model_jitted(**lowerCAmelCase_      ).to_tuple()

																												self.assertEqual(len(lowerCAmelCase_      )							,    len(lowerCAmelCase_      )      )
																												for jitted_output, output in zip(lowerCAmelCase_							,    lowerCAmelCase_      ):
																																			self.assertEqual(jitted_output.shape							,    output.shape      )







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


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_      )







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


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_      )







							@slow
							def      __lowerCAmelCase    (						self :						Dict      )    ->							List[str]:


														"""simple docstring"""


														for model_class_name in self.all_model_classes:
																					_a			  =       model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''      )
																					_a			  =       model(np.ones((1, 3, 2_24, 2_24)      )      )
																					self.assertIsNotNone(lowerCAmelCase_      )



def 	snake_case_					():





							'''simple docstring'''


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

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







							@cached_property
							def      __lowerCAmelCase    (						self :						int      )    ->							Optional[int]:


														"""simple docstring"""


														return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None







							@slow
							def      __lowerCAmelCase    (						self :						Any      )    ->							Dict:


														"""simple docstring"""


														_a			  =       FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''np'''      ).pixel_values

														# prepare bool_masked_pos
														_a			  =       np.ones((1, 1_96)							,    dtype=lowerCAmelCase_      )

														# forward pass
														_a			  =       model(pixel_values=lowerCAmelCase_							,    bool_masked_pos=lowerCAmelCase_      )
														_a			  =       outputs.logits

														# verify the logits
														_a			  =       (1, 1_96, 81_92)
														self.assertEqual(logits.shape							,    lowerCAmelCase_      )

														_a			  =       np.array(
														    [[-3.2_4_3_7, 0.5_0_7_2, -13.91_74], [-3.2_4_5_6, 0.4_9_4_8, -13.94_01], [-3.2_0_3_3, 0.5_1_2_1, -13.85_50]]      )

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







							@slow
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''np'''      )

														# forward pass
														_a			  =       model(**lowerCAmelCase_      )
														_a			  =       outputs.logits

														# verify the logits
														_a			  =       (1, 10_00)
														self.assertEqual(logits.shape							,    lowerCAmelCase_      )

														_a			  =       np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]      )

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

														_a			  =       2_81
														self.assertEqual(logits.argmax(-1      ).item()							,    lowerCAmelCase_      )







							@slow
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''np'''      )

														# forward pass
														_a			  =       model(**lowerCAmelCase_      )
														_a			  =       outputs.logits

														# verify the logits
														_a			  =       (1, 2_18_41)
														self.assertEqual(logits.shape							,    lowerCAmelCase_      )

														_a			  =       np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]      )

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

														_a			  =       23_96
														self.assertEqual(logits.argmax(-1      ).item()							,    lowerCAmelCase_      )







'''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()
_snake_case   :   Any     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						List[str]				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       []
							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 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


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

														_a			  =       in_proj_weight[
														    : encoder_config.hidden_size, :
														]
														_a			  =       in_proj_weight[
														    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
														]
														_a			  =       in_proj_weight[
														    -encoder_config.hidden_size :, :
														]
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       dct.pop(UpperCamelCase   )
							_a			  =       val
def 	snake_case_					(UpperCamelCase :						Union[str, Any]   ):





							'''simple docstring'''


							if "handwritten" in checkpoint_url:
														_a			  =       '''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:
														_a			  =       '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
							_a			  =       Image.open(requests.get(UpperCamelCase				,		stream=UpperCamelCase   ).raw   ).convert('''RGB'''   )
							return im
@torch.no_grad()
def 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						Any   ):





							'''simple docstring'''


							_a			  =       ViTConfig(image_size=384				,		qkv_bias=UpperCamelCase   )
							_a			  =       TrOCRConfig()

							# size of the architecture
							if "base" in checkpoint_url:
														_a			  =       768
							elif "large" in checkpoint_url:
														# use ViT-large encoder
														_a			  =       1024
														_a			  =       4096
														_a			  =       24
														_a			  =       16
														_a			  =       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:
														_a			  =       False
														_a			  =       '''relu'''
														_a			  =       1024
														_a			  =       True
														_a			  =       False
														_a			  =       False

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

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

							_a			  =       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():
														_a			  =       state_dict.pop(UpperCamelCase   )
														if key.startswith('''decoder'''   ) and "output_projection" not in key:
																					_a			  =       val
														else:
																					_a			  =       val

    # load state dict
							model.load_state_dict(UpperCamelCase   )

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

							_a			  =       processor(images=prepare_img(UpperCamelCase   )				,		return_tensors='''pt'''   ).pixel_values

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

							_a			  =       torch.Size([1, 1, 5_0265]   )
							if "trocr-base-handwritten" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]   )
							elif "trocr-large-handwritten" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]   )
							elif "trocr-base-printed" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]   )
							elif "trocr-large-printed" in checkpoint_url:
														_a			  =       torch.tensor(
														    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]   )

							if "stage1" not in checkpoint_url:
														assert logits.shape == expected_shape, "Shape of logits not as expected"
														assert torch.allclose(logits[0, 0, :10]				,		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__":
				_snake_case   :   Tuple     							=	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.'
				)
				_snake_case   :   Dict     							=	parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

