from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return [
        int(1_000 * (box[0] / width)	),
        int(1_000 * (box[1] / height)	),
        int(1_000 * (box[2] / width)	),
        int(1_000 * (box[3] / height)	),
    ]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		to_pil_image(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		pil_image.size
    __UpperCamelCase     :str			  =		pytesseract.image_to_data(SCREAMING_SNAKE_CASE  ,			lang=SCREAMING_SNAKE_CASE  ,			output_type='''dict'''  ,			config=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

    # filter empty words and corresponding coordinates
    __UpperCamelCase     :Dict			  =		[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE	) if not word.strip()]
    __UpperCamelCase     :Union[str, Any]			  =		[word for idx, word in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :List[Any]			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :Dict			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :List[str]			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :str			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    __UpperCamelCase     :int			  =		[]
    for x, y, w, h in zip(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :int			  =		[x, y, x + w, y + h]
        actual_boxes.append(SCREAMING_SNAKE_CASE	)

    # finally, normalize the bounding boxes
    __UpperCamelCase     :Optional[int]			  =		[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)

    assert len(SCREAMING_SNAKE_CASE	) == len(SCREAMING_SNAKE_CASE	), "Not as many words as there are bounding boxes"

    return words, normalized_boxes


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = None , __lowercase = PILImageResampling.BILINEAR , __lowercase = True , __lowercase = 1 / 255 , __lowercase = True , __lowercase = None , __lowercase = None , __lowercase = True , __lowercase = None , __lowercase = "" , **__lowercase , )       ->	None:
        super().__init__(**__lowercase)
        __UpperCamelCase     :Any			  =		size if size is not None else {'''height''': 224, '''width''': 224}
        __UpperCamelCase     :str			  =		get_size_dict(__lowercase)

        __UpperCamelCase     :Tuple			  =		do_resize
        __UpperCamelCase     :Tuple			  =		size
        __UpperCamelCase     :Union[str, Any]			  =		resample
        __UpperCamelCase     :str			  =		do_rescale
        __UpperCamelCase     :Optional[int]			  =		rescale_value
        __UpperCamelCase     :List[str]			  =		do_normalize
        __UpperCamelCase     :Optional[Any]			  =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCamelCase     :str			  =		image_std if image_std is not None else IMAGENET_STANDARD_STD
        __UpperCamelCase     :str			  =		apply_ocr
        __UpperCamelCase     :Optional[Any]			  =		ocr_lang
        __UpperCamelCase     :Optional[Any]			  =		tesseract_config







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = PILImageResampling.BILINEAR , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        __UpperCamelCase     :List[str]			  =		get_size_dict(__lowercase)
        if "height" not in size or "width" not in size:
            raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""")
        __UpperCamelCase     :Any			  =		(size['''height'''], size['''width'''])
        return resize(__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	PIL.Image.Image:
        __UpperCamelCase     :Optional[Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Dict			  =		size if size is not None else self.size
        __UpperCamelCase     :Union[str, Any]			  =		get_size_dict(__lowercase)
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample
        __UpperCamelCase     :Optional[int]			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[Any]			  =		rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase     :str			  =		do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase     :Any			  =		image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase     :List[str]			  =		image_std if image_std is not None else self.image_std
        __UpperCamelCase     :List[Any]			  =		apply_ocr if apply_ocr is not None else self.apply_ocr
        __UpperCamelCase     :int			  =		ocr_lang if ocr_lang is not None else self.ocr_lang
        __UpperCamelCase     :Dict			  =		tesseract_config if tesseract_config is not None else self.tesseract_config

        __UpperCamelCase     :Optional[int]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError(
                '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                '''torch.Tensor, tf.Tensor or jax.ndarray.''')

        if do_resize and size is None:
            raise ValueError('''Size must be specified if do_resize is True.''')

        if do_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Any			  =		[to_numpy_array(__lowercase) for image in images]

        # Tesseract OCR to get words + normalized bounding boxes
        if apply_ocr:
            requires_backends(self , '''pytesseract''')
            __UpperCamelCase     :str			  =		[]
            __UpperCamelCase     :Optional[Any]			  =		[]
            for image in images:
                __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		apply_tesseract(__lowercase , __lowercase , __lowercase)
                words_batch.append(__lowercase)
                boxes_batch.append(__lowercase)

        if do_resize:
            __UpperCamelCase     :Dict			  =		[self.resize(image=__lowercase , size=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Tuple			  =		[self.rescale(image=__lowercase , scale=__lowercase) for image in images]

        if do_normalize:
            __UpperCamelCase     :List[str]			  =		[self.normalize(image=__lowercase , mean=__lowercase , std=__lowercase) for image in images]

        __UpperCamelCase     :Dict			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :Any			  =		BatchFeature(data={'''pixel_values''': images} , tensor_type=__lowercase)

        if apply_ocr:
            __UpperCamelCase     :List[Any]			  =		words_batch
            __UpperCamelCase     :int			  =		boxes_batch
        return data











import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					if hor == 1_28:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 64, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					__lowerCAmelCase				   =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""     )
					__lowerCAmelCase				   =       model.state_dict()
					__lowerCAmelCase				   =       {
					    "down_block_types": down_block_types,
					    "block_out_channels": block_out_channels,
					    "up_block_types": up_block_types,
					    "layers_per_block": 1,
					    "use_timestep_embedding": True,
					    "out_block_type": "OutConv1DBlock",
					    "norm_num_groups": 8,
					    "downsample_each_block": False,
					    "in_channels": 14,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "sample_size": 6_55_36,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )
					__lowerCAmelCase				   =       dict(zip(model.state_dict().keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					torch.save(hf_value_function.state_dict()      ,      F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""     )
					with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,      "w"     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       {
					    "in_channels": 14,
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": (),
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": (32, 64, 1_28, 2_56),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 6_55_36,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "norm_num_groups": 8,
					    "act_fn": "mish",
					}

					__lowerCAmelCase				   =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"     )
					__lowerCAmelCase				   =       model
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )

					__lowerCAmelCase				   =       dict(zip(state_dict.keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )

					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					torch.save(hf_value_function.state_dict()      ,      "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"     )
					with open("hub/hopper-medium-v2/value_function/config.json"      ,      "w"     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
					unet(32)
					# unet(128)
					value_function()




