


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   str   				=      (PNDMScheduler,)
					UpperCAmelCase__ :   Tuple   				=      (('''num_inference_steps''', 5_0),)
					def 			lowerCamelCase__(				self       :Optional[int]      ,**__snake_case       :int				) ->   Optional[Any]:
												a__					=					{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.00_01,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												}

												config.update(**__snake_case				)
												return config
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :Optional[Any]=0      ,**__snake_case       :Dict				) ->   Optional[Any]:
												a__					=					dict(self.forward_default_kwargs				)
												a__					=					kwargs.pop('num_inference_steps'      ,__snake_case				)
												a__					=					self.dummy_sample
												a__					=					0.1 * sample
												a__					=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																			a__					=					self.get_scheduler_config(**__snake_case				)
																			a__					=					scheduler_class(**__snake_case				)
																			scheduler.set_timesteps(__snake_case				)
																			# copy over dummy past residuals
																			a__					=					dummy_past_residuals[:]

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										scheduler.save_config(__snake_case				)
																										a__					=					scheduler_class.from_pretrained(__snake_case				)
																										new_scheduler.set_timesteps(__snake_case				)
																										# copy over dummy past residuals
																										a__					=					dummy_past_residuals[:]

																			a__					=					scheduler.step_prk(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample
																			a__					=					new_scheduler.step_prk(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample

																			assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

																			a__					=					scheduler.step_plms(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample
																			a__					=					new_scheduler.step_plms(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample

																			assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
					def 			lowerCamelCase__(				self       :int				) ->   str:
												pass
					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :int=0      ,**__snake_case       :Union[str, Any]				) ->   List[Any]:
												a__					=					dict(self.forward_default_kwargs				)
												a__					=					kwargs.pop('num_inference_steps'      ,__snake_case				)
												a__					=					self.dummy_sample
												a__					=					0.1 * sample
												a__					=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																			a__					=					self.get_scheduler_config()
																			a__					=					scheduler_class(**__snake_case				)
																			scheduler.set_timesteps(__snake_case				)

																			# copy over dummy past residuals (must be after setting timesteps)
																			a__					=					dummy_past_residuals[:]

																			with tempfile.TemporaryDirectory() as tmpdirname:
																										scheduler.save_config(__snake_case				)
																										a__					=					scheduler_class.from_pretrained(__snake_case				)
																										# copy over dummy past residuals
																										new_scheduler.set_timesteps(__snake_case				)

																										# copy over dummy past residual (must be after setting timesteps)
																										a__					=					dummy_past_residuals[:]

																			a__					=					scheduler.step_prk(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample
																			a__					=					new_scheduler.step_prk(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample

																			assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

																			a__					=					scheduler.step_plms(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample
																			a__					=					new_scheduler.step_plms(__snake_case      ,__snake_case      ,__snake_case      ,**__snake_case				).prev_sample

																			assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
					def 			lowerCamelCase__(				self       :int      ,**__snake_case       :Tuple				) ->   List[Any]:
												a__					=					self.scheduler_classes[0]
												a__					=					self.get_scheduler_config(**__snake_case				)
												a__					=					scheduler_class(**__snake_case				)

												a__					=					10
												a__					=					self.dummy_model()
												a__					=					self.dummy_sample_deter
												scheduler.set_timesteps(__snake_case				)

												for i, t in enumerate(scheduler.prk_timesteps				):
																			a__					=					model(__snake_case      ,__snake_case				)
																			a__					=					scheduler.step_prk(__snake_case      ,__snake_case      ,__snake_case				).prev_sample

												for i, t in enumerate(scheduler.plms_timesteps				):
																			a__					=					model(__snake_case      ,__snake_case				)
																			a__					=					scheduler.step_plms(__snake_case      ,__snake_case      ,__snake_case				).prev_sample

												return sample
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Dict:
												a__					=					dict(self.forward_default_kwargs				)

												a__					=					kwargs.pop('num_inference_steps'      ,__snake_case				)

												for scheduler_class in self.scheduler_classes:
																			a__					=					self.get_scheduler_config()
																			a__					=					scheduler_class(**__snake_case				)

																			a__					=					self.dummy_sample
																			a__					=					0.1 * sample

																			if num_inference_steps is not None and hasattr(__snake_case      ,'set_timesteps'				):
																										scheduler.set_timesteps(__snake_case				)
																			elif num_inference_steps is not None and not hasattr(__snake_case      ,'set_timesteps'				):
																										a__					=					num_inference_steps

																			# copy over dummy past residuals (must be done after set_timesteps)
																			a__					=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																			a__					=					dummy_past_residuals[:]

																			a__					=					scheduler.step_prk(__snake_case      ,0      ,__snake_case      ,**__snake_case				).prev_sample
																			a__					=					scheduler.step_prk(__snake_case      ,1      ,__snake_case      ,**__snake_case				).prev_sample

																			self.assertEqual(output_a.shape      ,sample.shape				)
																			self.assertEqual(output_a.shape      ,output_a.shape				)

																			a__					=					scheduler.step_plms(__snake_case      ,0      ,__snake_case      ,**__snake_case				).prev_sample
																			a__					=					scheduler.step_plms(__snake_case      ,1      ,__snake_case      ,**__snake_case				).prev_sample

																			self.assertEqual(output_a.shape      ,sample.shape				)
																			self.assertEqual(output_a.shape      ,output_a.shape				)
					def 			lowerCamelCase__(				self       :Any				) ->   Tuple:
												for timesteps in [1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   Dict:
												for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=__snake_case				)

												a__					=					self.scheduler_classes[0]
												a__					=					self.get_scheduler_config(steps_offset=1				)
												a__					=					scheduler_class(**__snake_case				)
												scheduler.set_timesteps(10				)
												assert torch.equal(
												    scheduler.timesteps      ,torch.LongTensor(
												        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]				)      ,)
					def 			lowerCamelCase__(				self       :List[str]				) ->   List[str]:
												for beta_start, beta_end in zip([0.00_01, 0.0_01]      ,[0.0_02, 0.02]				):
																			self.check_over_configs(beta_start=__snake_case      ,beta_end=__snake_case				)
					def 			lowerCamelCase__(				self       :Any				) ->   List[Any]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   List[Any]:
												for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=__snake_case				)
					def 			lowerCamelCase__(				self       :Any				) ->   Union[str, Any]:
												for t in [1, 5, 10]:
																			self.check_over_forward(time_step=__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Dict:
												for t, num_inference_steps in zip([1, 5, 10]      ,[10, 50, 1_00]				):
																			self.check_over_forward(num_inference_steps=__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
												a__					=					27

												for scheduler_class in self.scheduler_classes:
																			a__					=					self.dummy_sample
																			a__					=					0.1 * sample

																			a__					=					self.get_scheduler_config()
																			a__					=					scheduler_class(**__snake_case				)

																			scheduler.set_timesteps(__snake_case				)

																			# before power of 3 fix, would error on first step, so we only need to do two
																			for i, t in enumerate(scheduler.prk_timesteps[:2]				):
																										a__					=					scheduler.step_prk(__snake_case      ,__snake_case      ,__snake_case				).prev_sample
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												with self.assertRaises(__snake_case				):
																			a__					=					self.scheduler_classes[0]
																			a__					=					self.get_scheduler_config()
																			a__					=					scheduler_class(**__snake_case				)

																			scheduler.step_plms(self.dummy_sample      ,1      ,self.dummy_sample				).prev_sample
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												a__					=					self.full_loop()
												a__					=					torch.sum(torch.abs(__snake_case				)				)
												a__					=					torch.mean(torch.abs(__snake_case				)				)

												assert abs(result_sum.item() - 1_98.13_18				) < 1E-2
												assert abs(result_mean.item() - 0.25_80				) < 1E-3
					def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												a__					=					self.full_loop(prediction_type='v_prediction'				)
												a__					=					torch.sum(torch.abs(__snake_case				)				)
												a__					=					torch.mean(torch.abs(__snake_case				)				)

												assert abs(result_sum.item() - 67.39_86				) < 1E-2
												assert abs(result_mean.item() - 0.08_78				) < 1E-3
					def 			lowerCamelCase__(				self       :int				) ->   Any:
												# We specify different beta, so that the first alpha is 0.99
												a__					=					self.full_loop(set_alpha_to_one=__snake_case      ,beta_start=0.01				)
												a__					=					torch.sum(torch.abs(__snake_case				)				)
												a__					=					torch.mean(torch.abs(__snake_case				)				)

												assert abs(result_sum.item() - 2_30.03_99				) < 1E-2
												assert abs(result_mean.item() - 0.29_95				) < 1E-3



					def 			lowerCamelCase__(				self       :Tuple				) ->   int:
												# We specify different beta, so that the first alpha is 0.99
												a__					=					self.full_loop(set_alpha_to_one=__snake_case      ,beta_start=0.01				)
												a__					=					torch.sum(torch.abs(__snake_case				)				)
												a__					=					torch.mean(torch.abs(__snake_case				)				)

												assert abs(result_sum.item() - 1_86.94_82				) < 1E-2
												assert abs(result_mean.item() - 0.24_34				) < 1E-3








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							:	int					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict       ):
							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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							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(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       ).convert('RGB'       )
							return im






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    int       ):
							a__					=					ViTConfig(image_size=3_8_4 ,   qkv_bias=__lowerCAmelCase       )
							a__					=					TrOCRConfig()

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

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

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

							a__					=					create_rename_keys(__lowerCAmelCase ,   __lowerCAmelCase       )
							for src, dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
							read_in_q_k_v(__lowerCAmelCase ,   __lowerCAmelCase       )

							# 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(__lowerCAmelCase       )
														if key.startswith('decoder'       ) and "output_projection" not in key:
																					a__					=					val
														else:
																					a__					=					val

    # load state dict
							model.load_state_dict(__lowerCAmelCase       )

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

							a__					=					processor(images=prepare_img(__lowerCAmelCase       ) ,   return_tensors='pt'       ).pixel_values

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

							a__					=					torch.Size([1, 1, 5_0_2_6_5]       )
							if "trocr-base-handwritten" in checkpoint_url:
														a__					=					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:
														a__					=					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:
														a__					=					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:
														a__					=					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, :1_0] ,   __lowerCAmelCase ,   atol=1E-3       ), "First elements of logits not as expected"

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


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




