'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
       a_        =      StableDiffusionInstructPixaPixPipeline
       a_        =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
       a_        =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       a_        =      IMAGE_TO_IMAGE_IMAGE_PARAMS
       a_        =      IMAGE_TO_IMAGE_IMAGE_PARAMS





       def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
        torch.manual_seed(0	)
        __UpperCAmelCase							=			UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=8     ,		out_channels=4     ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,		cross_attention_dim=3_2     ,		)
        __UpperCAmelCase							=			PNDMScheduler(skip_prk_steps=__a	)
        torch.manual_seed(0	)
        __UpperCAmelCase							=			AutoencoderKL(
            block_out_channels=[3_2, 6_4]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
        torch.manual_seed(0	)
        __UpperCAmelCase							=			CLIPTextConfig(
            bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
        __UpperCAmelCase							=			CLIPTextModel(__a	)
        __UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

        __UpperCAmelCase							=			{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components





       def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							Optional[Any]=0	)							->	int:
        __UpperCAmelCase							=			floats_tensor((1, 3, 3_2, 3_2)     ,		rng=random.Random(__a	)	).to(__a	)
        __UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	)[0]
        __UpperCAmelCase							=			Image.fromarray(np.uinta(__a	)	).convert('''RGB'''	)
        if str(__a	).startswith('''mps'''	):
         __UpperCAmelCase							=			torch.manual_seed(__a	)
        else:
         __UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
        __UpperCAmelCase							=			{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''image_guidance_scale''': 1,
            '''output_type''': '''numpy''',
        }
        return inputs





       def       snake_case__						(      self				:							List[str]	)							->	Optional[Any]:
        __UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCAmelCase							=			self.get_dummy_components()
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
        __UpperCAmelCase							=			sd_pipe.to(__a	)
        sd_pipe.set_progress_bar_config(disable=__a	)

        __UpperCAmelCase							=			self.get_dummy_inputs(__a	)
        __UpperCAmelCase							=			sd_pipe(**__a	).images
        __UpperCAmelCase							=			image[0, -3:, -3:, -1]
        assert image.shape == (1, 3_2, 3_2, 3)
        __UpperCAmelCase							=			np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def       snake_case__						(      self				:							List[str]	)							->	Optional[Any]:
        __UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCAmelCase							=			self.get_dummy_components()
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
        __UpperCAmelCase							=			sd_pipe.to(__a	)
        sd_pipe.set_progress_bar_config(disable=__a	)

        __UpperCAmelCase							=			self.get_dummy_inputs(__a	)
        __UpperCAmelCase							=			'''french fries'''
        __UpperCAmelCase							=			sd_pipe(**__a     ,		negative_prompt=__a	)
        __UpperCAmelCase							=			output.images
        __UpperCAmelCase							=			image[0, -3:, -3:, -1]

        assert image.shape == (1, 3_2, 3_2, 3)
        __UpperCAmelCase							=			np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def       snake_case__						(      self				:							Any	)							->	str:
        __UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCAmelCase							=			self.get_dummy_components()
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
        __UpperCAmelCase							=			sd_pipe.to(__a	)
        sd_pipe.set_progress_bar_config(disable=__a	)

        __UpperCAmelCase							=			self.get_dummy_inputs(__a	)
        __UpperCAmelCase							=			[inputs['''prompt''']] * 2

        __UpperCAmelCase							=			np.array(inputs['''image''']	).astype(np.floataa	) / 2_5_5.0
        __UpperCAmelCase							=			torch.from_numpy(__a	).unsqueeze(0	).to(__a	)
        __UpperCAmelCase							=			image / 2 + 0.5
        __UpperCAmelCase							=			image.permute(0     ,		3     ,		1     ,		2	)
        __UpperCAmelCase							=			image.repeat(2     ,		1     ,		1     ,		1	)

        __UpperCAmelCase							=			sd_pipe(**__a	).images
        __UpperCAmelCase							=			image[-1, -3:, -3:, -1]

        assert image.shape == (2, 3_2, 3_2, 3)
        __UpperCAmelCase							=			np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def       snake_case__						(      self				:							Optional[int]	)							->	Optional[Any]:
        __UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCAmelCase							=			self.get_dummy_components()
        __UpperCAmelCase							=			EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5     ,		beta_end=0.0_1_2     ,		beta_schedule='''scaled_linear'''	)
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
        __UpperCAmelCase							=			sd_pipe.to(__a	)
        sd_pipe.set_progress_bar_config(disable=__a	)

        __UpperCAmelCase							=			self.get_dummy_inputs(__a	)
        __UpperCAmelCase							=			sd_pipe(**__a	).images
        __UpperCAmelCase							=			image[0, -3:, -3:, -1]

        __UpperCAmelCase							=			[round(__a     ,		4	) for x in image_slice.flatten().tolist()]
        print(''','''.join([str(__a	) for x in slice]	)	)

        assert image.shape == (1, 3_2, 3_2, 3)
        __UpperCAmelCase							=			np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3





       def       snake_case__						(      self				:							str	)							->	Optional[int]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






       def       snake_case__						(      self				:							List[Any]	)							->	Tuple:
        __UpperCAmelCase							=			self.get_dummy_components()
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline(**__a	)
        __UpperCAmelCase							=			VaeImageProcessor(do_resize=__a     ,		do_normalize=__a	)
        __UpperCAmelCase							=			pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)

        __UpperCAmelCase							=			pipe(**self.get_dummy_inputs_by_type(__a     ,		input_image_type='''pt'''	)	)[0]

        __UpperCAmelCase							=			components['''vae''']
        __UpperCAmelCase							=			self.get_dummy_inputs_by_type(__a     ,		input_image_type='''pt'''	)

        for image_param in self.image_latents_params:
         if image_param in inputs.keys():
          __UpperCAmelCase							=			vae.encode(inputs[image_param]	).latent_dist.mode()

        __UpperCAmelCase							=			pipe(**__a	)[0]

        __UpperCAmelCase							=			np.abs(out - out_latents_inputs	).max()
        self.assertLess(__a     ,		1e-4     ,		'''passing latents as image input generate different result from passing image'''	)






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





       def       snake_case__						(      self				:							Dict	)							->	Dict:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





       def       snake_case__						(      self				:							List[str]     ,		__a				:							Dict=0	)							->	List[str]:
        __UpperCAmelCase							=			torch.manual_seed(__a	)
        __UpperCAmelCase							=			load_image(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''	)
        __UpperCAmelCase							=			{
            '''prompt''': '''turn him into a cyborg''',
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 3,
            '''guidance_scale''': 7.5,
            '''image_guidance_scale''': 1.0,
            '''output_type''': '''numpy''',
        }
        return inputs





       def       snake_case__						(      self				:							Optional[Any]	)							->	Union[str, Any]:
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
            '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a	)
        pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing()

        __UpperCAmelCase							=			self.get_inputs()
        __UpperCAmelCase							=			pipe(**__a	).images
        __UpperCAmelCase							=			image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        __UpperCAmelCase							=			np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-3





       def       snake_case__						(      self				:							Any	)							->	List[str]:
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
            '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a	)
        __UpperCAmelCase							=			LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
        pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing()

        __UpperCAmelCase							=			self.get_inputs()
        __UpperCAmelCase							=			pipe(**__a	).images
        __UpperCAmelCase							=			image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        __UpperCAmelCase							=			np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-3





       def       snake_case__						(      self				:							Tuple	)							->	Tuple:
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
            '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a	)
        __UpperCAmelCase							=			DDIMScheduler.from_config(pipe.scheduler.config	)
        pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing()

        __UpperCAmelCase							=			self.get_inputs()
        __UpperCAmelCase							=			pipe(**__a	).images
        __UpperCAmelCase							=			image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        __UpperCAmelCase							=			np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]	)

        assert np.abs(expected_slice - image_slice	).max() < 1e-3





       def       snake_case__						(      self				:							Optional[int]	)							->	Optional[Any]:
        __UpperCAmelCase							=			0

        def callback_fn(__a				:							int     ,		__a				:							int     ,		__a				:							torch.FloatTensor	) -> None:
         __UpperCAmelCase							=			True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
          __UpperCAmelCase							=			latents.detach().cpu().numpy()
          assert latents.shape == (1, 4, 6_4, 6_4)
          __UpperCAmelCase							=			latents[0, -3:, -3:, -1]
          __UpperCAmelCase							=			np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]	)

          assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
         elif step == 2:
          __UpperCAmelCase							=			latents.detach().cpu().numpy()
          assert latents.shape == (1, 4, 6_4, 6_4)
          __UpperCAmelCase							=			latents[0, -3:, -3:, -1]
          __UpperCAmelCase							=			np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]	)

          assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

        __UpperCAmelCase							=			False

        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
            '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a     ,		torch_dtype=torch.floataa	)
        __UpperCAmelCase							=			pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing()

        __UpperCAmelCase							=			self.get_inputs()
        pipe(**__a     ,		callback=__a     ,		callback_steps=1	)
        assert callback_fn.has_been_called
        assert number_of_steps == 3





       def       snake_case__						(      self				:							Any	)							->	List[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
            '''timbrooks/instruct-pix2pix'''     ,		safety_checker=__a     ,		torch_dtype=torch.floataa	)
        __UpperCAmelCase							=			pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing(1	)
        pipe.enable_sequential_cpu_offload()

        __UpperCAmelCase							=			self.get_inputs()
        __UpperCAmelCase							=			pipe(**__a	)

        __UpperCAmelCase							=			torch.cuda.max_memory_allocated()
        # make sure that less than 2.2 GB is allocated
        assert mem_bytes < 2.2 * 1_0**9






       def       snake_case__						(      self				:							Optional[int]	)							->	Any:
        __UpperCAmelCase							=			self.get_inputs()
        # resize to resolution that is divisible by 8 but not 16 or 32
        __UpperCAmelCase							=			inputs['''image'''].resize((5_0_4, 5_0_4)	)

        __UpperCAmelCase							=			'''timbrooks/instruct-pix2pix'''
        __UpperCAmelCase							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
            __a     ,		safety_checker=__a     ,		)
        pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing()

        __UpperCAmelCase							=			pipe(**__a	)
        __UpperCAmelCase							=			output.images[0]

        __UpperCAmelCase							=			image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

        assert image.shape == (5_0_4, 5_0_4, 3)
        __UpperCAmelCase							=			np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3

'''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)
