



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 	a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    Union[str, Any]	    =     UnCLIPImageVariationPipeline
	__magic_name__		:    Optional[int]	    =     IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
	__magic_name__		:    List[str]	    =     IMAGE_VARIATION_BATCH_PARAMS

	__magic_name__		:    Optional[int]	    =     [
	    "generator",
	    "return_dict",
	    "decoder_num_inference_steps",
	    "super_res_num_inference_steps",
	]
	__magic_name__		:    Optional[int]	    =     False


	@property
	def 				lowercase__  (self : List[Any]      )     ->			List[Any]:

					"""simple docstring"""

					return 32


	@property
	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					return 32


	@property
	def 				lowercase__  (self : List[Any]      )     ->			List[Any]:

					"""simple docstring"""

					return self.time_input_dim


	@property
	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					return self.time_input_dim * 4


	@property
	def 				lowercase__  (self : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

					return 100


	@property
	def 				lowercase__  (self : List[Any]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
					return tokenizer


	@property
	def 				lowercase__  (self : Optional[int]      )     ->			Union[str, Any]:

					"""simple docstring"""

					torch.manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= CLIPTextConfig(
					    bos_token_id=0,					eos_token_id=2,					hidden_size=self.text_embedder_hidden_size,					projection_dim=self.text_embedder_hidden_size,					intermediate_size=37,					layer_norm_eps=1e-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1000,					)
					return CLIPTextModelWithProjection(__UpperCAmelCase      )


	@property
	def 				lowercase__  (self : List[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					torch.manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Any      					= CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size,					projection_dim=self.text_embedder_hidden_size,					num_hidden_layers=5,					num_attention_heads=4,					image_size=32,					intermediate_size=37,					patch_size=1,					)
					return CLIPVisionModelWithProjection(__UpperCAmelCase      )


	@property
	def 				lowercase__  (self : Dict      )     ->			Any:

					"""simple docstring"""

					torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						int      					= {
					    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
					    '''time_embed_dim''': self.time_embed_dim,
					    '''cross_attention_dim''': self.cross_attention_dim,
					}

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= UnCLIPTextProjModel(**__UpperCAmelCase      )
					return model


	@property
	def 				lowercase__  (self : Dict      )     ->			List[str]:

					"""simple docstring"""

					torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						Dict      					= {
					    '''sample_size''': 32,
					    # RGB in channels
					    '''in_channels''': 3,
					    # Out channels is double in channels because predicts mean and variance
					    '''out_channels''': 6,
					    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
					    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
					    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
					    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    '''layers_per_block''': 1,
					    '''cross_attention_dim''': self.cross_attention_dim,
					    '''attention_head_dim''': 4,
					    '''resnet_time_scale_shift''': '''scale_shift''',
					    '''class_embed_type''': '''identity''',
					}

					SCREAMING_SNAKE_CASE  :						int      					= UNetaDConditionModel(**__UpperCAmelCase      )
					return model


	@property
	def 				lowercase__  (self : Tuple      )     ->			Optional[int]:

					"""simple docstring"""

					return {
					    "sample_size": 64,
					    "layers_per_block": 1,
					    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
					    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
					    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    "in_channels": 6,
					    "out_channels": 3,
					}


	@property
	def 				lowercase__  (self : Any      )     ->			List[str]:

					"""simple docstring"""

					torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= UNetaDModel(**self.dummy_super_res_kwargs      )
					return model


	@property
	def 				lowercase__  (self : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					torch.manual_seed(1      )

					SCREAMING_SNAKE_CASE  :						Dict      					= UNetaDModel(**self.dummy_super_res_kwargs      )
					return model


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.dummy_decoder
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.dummy_text_proj
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.dummy_text_encoder
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.dummy_tokenizer
					SCREAMING_SNAKE_CASE  :						int      					= self.dummy_super_res_first
					SCREAMING_SNAKE_CASE  :						Dict      					= self.dummy_super_res_last

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= UnCLIPScheduler(
					    variance_type='''learned_range''',					prediction_type='''epsilon''',					num_train_timesteps=1000,					)

					SCREAMING_SNAKE_CASE  :						Any      					= UnCLIPScheduler(
					    variance_type='''fixed_small_log''',					prediction_type='''epsilon''',					num_train_timesteps=1000,					)

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= CLIPImageProcessor(crop_size=32,					size=32      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.dummy_image_encoder

					return {
					    "decoder": decoder,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "text_proj": text_proj,
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder,
					    "super_res_first": super_res_first,
					    "super_res_last": super_res_last,
					    "decoder_scheduler": decoder_scheduler,
					    "super_res_scheduler": super_res_scheduler,
					}


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[Any]=0,					__UpperCAmelCase : int=True      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= floats_tensor((1, 3, 32, 32),					rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
					if str(__UpperCAmelCase      ).startswith('''mps'''      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.manual_seed(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Tuple      					= torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )

					if pil_image:
									SCREAMING_SNAKE_CASE  :						List[str]      					= input_image * 0.5 + 0.5
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= input_image.clamp(0,					1      )
									SCREAMING_SNAKE_CASE  :						Any      					= input_image.cpu().permute(0,					2,					3,					1      ).float().numpy()
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= DiffusionPipeline.numpy_to_pil(__UpperCAmelCase      )[0]

					return {
					    "image": input_image,
					    "generator": generator,
					    "decoder_num_inference_steps": 2,
					    "super_res_num_inference_steps": 2,
					    "output_type": "np",
					}


	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''cpu'''

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_dummy_components()

					SCREAMING_SNAKE_CASE  :						str      					= self.pipeline_class(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pipe.to(__UpperCAmelCase      )

					pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pipe(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= output.images

					SCREAMING_SNAKE_CASE  :						int      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= pipe(
					    **__UpperCAmelCase,					return_dict=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						str      					= image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE  :						int      					= image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					SCREAMING_SNAKE_CASE  :						int      					= np.array(
					    [
					        0.9997,
					        0.0002,
					        0.9997,
					        0.9997,
					        0.9969,
					        0.0023,
					        0.9997,
					        0.9969,
					        0.9970,
					    ]      )

					assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2


	def 				lowercase__  (self : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''cpu'''

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_dummy_components()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pipeline_class(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= pipe.to(__UpperCAmelCase      )

					pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= pipe(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= output.images

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pipe(
					    **__UpperCAmelCase,					return_dict=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						int      					= image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE  :						int      					= image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)

					SCREAMING_SNAKE_CASE  :						List[str]      					= np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]      )

					assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''cpu'''

					SCREAMING_SNAKE_CASE  :						int      					= self.get_dummy_components()

					SCREAMING_SNAKE_CASE  :						Dict      					= self.pipeline_class(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= pipe.to(__UpperCAmelCase      )

					pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= [
					    pipeline_inputs['''image'''],
					    pipeline_inputs['''image'''],
					]

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pipe(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= output.images

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
					    tuple_pipeline_inputs['''image'''],
					    tuple_pipeline_inputs['''image'''],
					]

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= pipe(
					    **__UpperCAmelCase,					return_dict=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						int      					= image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)

					SCREAMING_SNAKE_CASE  :						int      					= np.array(
					    [
					        0.9997,
					        0.9989,
					        0.0008,
					        0.0021,
					        0.9960,
					        0.0018,
					        0.0014,
					        0.0002,
					        0.9933,
					    ]      )

					assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2


	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.device('''cpu'''      )


					class 	a__     :
						__magic_name__		:    int	    =     1



					SCREAMING_SNAKE_CASE  :						Any      					= self.get_dummy_components()

					SCREAMING_SNAKE_CASE  :						Any      					= self.pipeline_class(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= pipe.to(__UpperCAmelCase      )

					pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= torch.Generator(device=__UpperCAmelCase      ).manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						str      					= pipe.decoder.dtype
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 1

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= (
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pipe.prepare_latents(
					    __UpperCAmelCase,					dtype=__UpperCAmelCase,					device=__UpperCAmelCase,					generator=__UpperCAmelCase,					latents=__UpperCAmelCase,					scheduler=DummyScheduler()      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= (
					    batch_size,
					    pipe.super_res_first.config.in_channels // 2,
					    pipe.super_res_first.config.sample_size,
					    pipe.super_res_first.config.sample_size,
					)
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pipe.prepare_latents(
					    __UpperCAmelCase,					dtype=__UpperCAmelCase,					device=__UpperCAmelCase,					generator=__UpperCAmelCase,					latents=__UpperCAmelCase,					scheduler=DummyScheduler()      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= pipe(
					    **__UpperCAmelCase,					decoder_latents=__UpperCAmelCase,					super_res_latents=__UpperCAmelCase      ).images

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_dummy_inputs(__UpperCAmelCase,					pil_image=__UpperCAmelCase      )
					# Don't pass image, instead pass embedding
					SCREAMING_SNAKE_CASE  :						Dict      					= pipeline_inputs.pop('''image'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= pipe.image_encoder(__UpperCAmelCase      ).image_embeds

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= pipe(
					    **__UpperCAmelCase,					decoder_latents=__UpperCAmelCase,					super_res_latents=__UpperCAmelCase,					image_embeddings=__UpperCAmelCase,					).images

					# make sure passing text embeddings manually is identical
					assert np.abs(img_out_a - img_out_a      ).max() < 1e-4


	@skip_mps
	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch_device == '''cpu'''

					# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
					SCREAMING_SNAKE_CASE  :						Dict      					= 1e-2

					self._test_attention_slicing_forward_pass(
					    test_max_difference=__UpperCAmelCase,					expected_max_diff=__UpperCAmelCase      )


	@skip_mps
	def 				lowercase__  (self : List[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= torch_device == '''cpu'''
					SCREAMING_SNAKE_CASE  :						Any      					= True
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					self._test_inference_batch_single_identical(
					    test_max_difference=__UpperCAmelCase,					relax_max_difference=__UpperCAmelCase,					additional_params_copy_to_batched_inputs=__UpperCAmelCase,					)


	def 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= [
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]

					if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=__UpperCAmelCase,					additional_params_copy_to_batched_inputs=__UpperCAmelCase,					)
					else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=__UpperCAmelCase      )


	@skip_mps
	def 				lowercase__  (self : Tuple      )     ->			int:

					"""simple docstring"""

					return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					return super().test_save_load_local()






	@skip_mps
	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					return super().test_save_load_optional_components()



@slow
@require_torch_gpu
class 	a__     (				unittest.TestCase	):


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

					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def 				lowercase__  (self : str      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= UnCLIPImageVariationPipeline.from_pretrained(
					    '''kakaobrain/karlo-v1-alpha-image-variations''',					torch_dtype=torch.floataa      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= pipeline.to(__UpperCAmelCase      )
					pipeline.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.Generator(device='''cpu'''      ).manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Dict      					= pipeline(
					    __UpperCAmelCase,					generator=__UpperCAmelCase,					output_type='''np''',					)

					SCREAMING_SNAKE_CASE  :						Any      					= output.images[0]

					assert image.shape == (256, 256, 3)

					assert_mean_pixel_difference(__UpperCAmelCase,					__UpperCAmelCase,					15      )









'''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_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= []
				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  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				for i in range(encoder_config.num_hidden_layers       ):
								# queries, keys and values (only weights, no biases)
								SCREAMING_SNAKE_CASE  :						str      					= state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''       )

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= dct.pop(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				if "handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						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:
								SCREAMING_SNAKE_CASE  :						Union[str, 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'''
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Image.open(requests.get(_SCREAMING_SNAKE_CASE    ,	stream=_SCREAMING_SNAKE_CASE       ).raw       ).convert('''RGB'''       )
				return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ViTConfig(image_size=3_84    ,	qkv_bias=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= 7_68
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 10_24
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 40_96
								SCREAMING_SNAKE_CASE  :						List[str]      					= 24
								SCREAMING_SNAKE_CASE  :						Any      					= 16
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 10_24
				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:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= False
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''relu'''
								SCREAMING_SNAKE_CASE  :						Tuple      					= 10_24
								SCREAMING_SNAKE_CASE  :						int      					= True
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
								SCREAMING_SNAKE_CASE  :						Any      					= False

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

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

				SCREAMING_SNAKE_CASE  :						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():
								SCREAMING_SNAKE_CASE  :						List[Any]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								if key.startswith('''decoder'''       ) and "output_projection" not in key:
												SCREAMING_SNAKE_CASE  :						Tuple      					= val
								else:
												SCREAMING_SNAKE_CASE  :						int      					= val

    # load state dict
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

				# Check outputs on an image
				SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTImageProcessor(size=encoder_config.image_size       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= RobertaTokenizer.from_pretrained('''roberta-large'''       )
				SCREAMING_SNAKE_CASE  :						Dict      					= TrOCRProcessor(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Tuple      					= processor(images=prepare_img(_SCREAMING_SNAKE_CASE       )    ,	return_tensors='''pt'''       ).pixel_values

				# verify logits
				SCREAMING_SNAKE_CASE  :						str      					= torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
				SCREAMING_SNAKE_CASE  :						Dict      					= model(pixel_values=_SCREAMING_SNAKE_CASE    ,	decoder_input_ids=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= outputs.logits

				SCREAMING_SNAKE_CASE  :						Any      					= torch.Size([1, 1, 5_02_65]       )
				if "trocr-base-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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:
								SCREAMING_SNAKE_CASE  :						Any      					= 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:
								SCREAMING_SNAKE_CASE  :						str      					= 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:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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]    ,	_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__":
	snake_case_			=     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_			=     parser.parse_args()
	convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




