




import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__lowerCAmelCase		:				Tuple       			=		{
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			a    =			list(s_dict.keys()   )
			for key in keys:
						a    =			R".*/layers_(\d+)"
						a    =			key
						if re.match(A,  A   ):
									a    =			re.sub(R"layers_(\d+)",  R"block/\1/layer",  A   )

						a    =			R"(encoder|decoder)\/"

						if re.match(A,  A   ):
									a    =			re.match(A,  A   ).groups()
									if groups[0] == "encoder":
												a    =			re.sub(R"/mlp/",  R"/1/mlp/",  A   )
												a    =			re.sub(R"/pre_mlp_layer_norm/",  R"/1/layer_norm/",  A   )

									elif groups[0] == "decoder":
												a    =			re.sub(R"/mlp/",  R"/2/mlp/",  A   )
												a    =			re.sub(R"/pre_mlp_layer_norm/",  R"/2/layer_norm/",  A   )

        # 2. Convert other classic mappings
						for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
									if old_key in new_key:
												a    =			new_key.replace(A,  A   )

						print(F"""{key} -> {new_key}"""   )
						a    =			s_dict.pop(A   )

			if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						a    =			s_dict[
						    "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
						].T
			if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						a    =			s_dict[
						    "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
						].T

			# 3. Take extra care of the EXPERTS layer
			for key in list(s_dict.keys()   ):
						if "expert" in key:
									a    =			s_dict[key].shape[0]
									a    =			s_dict[key]
									for idx in range(A   ):
												a    =			expert_weihts[idx]
												print(F"""{key} -> {key.replace("expert/",  "nested fstring"   )}"""   )

									s_dict.pop(A   )

			return s_dict


__lowerCAmelCase		:				Optional[int]       			=		{
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}




def 			__magic_name__				( A		:					Dict,  A		:					Optional[Any]   ):






			'''simple docstring'''







			import regex as re

			with open(A,  "r"   ) as f:
						a    =			f.read()

			a    =			re.findall(R"(.*) = ([0-9.]*)",  A   )
			a    =			{}
			for param, value in regex_match:
						if param in GIN_TO_CONFIG_MAPPING and value != "":
									a    =			float(A   ) if "." in value else int(A   )

			a    =			re.findall(R"(.*activations) = \(\'(.*)\',\)",  A   )[0]
			a    =			str(activation[1]   )

			a    =			num_experts
			a    =			SwitchTransformersConfig(**A   )
			return config




def 			__magic_name__				( A		:					Union[str, Any],  A		:					Union[str, Any],  A		:					List[str]=None,  A		:					List[Any]="./",  A		:					str=8   ):






			'''simple docstring'''







			print(F"""Loading flax weights from : {flax_checkpoint_path}"""   )
			a    =			checkpoints.load_tax_checkpoint(A   )

			if gin_file is not None:
						a    =			convert_gin_to_config(A,  A   )
			else:
						a    =			SwitchTransformersConfig.from_pretrained(A   )

			a    =			SwitchTransformersForConditionalGeneration(A   )

			a    =			flax_params["target"]
			a    =			flatten_dict(A,  sep="/"   )
			a    =			rename_keys(A   )
			a    =			unflatten_dict(A,  sep="/"   )

			# Load the flax params in the PT model
			load_flax_weights_in_pytorch_model(A,  A   )

			print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
			pt_model.save_pretrained(A   )


if __name__ == "__main__":
		__lowerCAmelCase		:				int       			=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--switch_t5x_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
		        ' model architecture. If not provided, a `gin_file` has to be provided.'
		    ),
		)
		parser.add_argument(
		    '--gin_file',
		    default=None,
		    type=str,
		    required=False,
		    help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
		)
		parser.add_argument(
		    '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
		)
		parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
		__lowerCAmelCase		:				Tuple       			=		parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)












from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

__lowerCAmelCase		:				List[str]       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Dict,  A		:					int,  A		:					Optional[int]   ):






			'''simple docstring'''







			return [
			    int(1000 * (box[0] / width)   ),
			    int(1000 * (box[1] / height)   ),
			    int(1000 * (box[2] / width)   ),
			    int(1000 * (box[3] / height)   ),
			]




def 			__magic_name__				( A		:					np.ndarray,  A		:					Optional[str],  A		:					Optional[str] = None   ):






			'''simple docstring'''







			a    =			tesseract_config if tesseract_config is not None else ""

			# apply OCR
			a    =			to_pil_image(A   )
			a    ,		a    =			pil_image.size
			a    =			pytesseract.image_to_data(A,  lang=A,  output_type="dict",  config=A   )
			a    ,		a    ,		a    ,		a    ,		a    =			data["text"], data["left"], data["top"], data["width"], data["height"]

			# filter empty words and corresponding coordinates
			a    =			[idx for idx, word in enumerate(A   ) if not word.strip()]
			a    =			[word for idx, word in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]

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

			# finally, normalize the bounding boxes
			a    =			[]
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(A,  A,  A   )   )

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

			return words, normalized_boxes






class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					["""pixel_values"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = ""					,      **__lowerCamelCase				:     Tuple					,      )					->     None:
							super().__init__(**__lowerCamelCase							)
							a    =			size if size is not None else {"height": 2_24, "width": 2_24}
							a    =			get_size_dict(__lowerCamelCase							)

							a    =			do_resize
							a    =			size
							a    =			resample
							a    =			apply_ocr
							a    =			ocr_lang
							a    =			tesseract_config





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     np.ndarray					,      __lowerCamelCase				:     Dict[str, int]					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     np.ndarray:
							a    =			get_size_dict(__lowerCamelCase							)
							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()}"""							)
							a    =			(size["height"], size["width"])
							return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     ImageInput					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = None					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase				:     ChannelDimension = ChannelDimension.FIRST					,      **__lowerCamelCase				:     Optional[Any]					,      )					->     PIL.Image.Image:
							a    =			do_resize if do_resize is not None else self.do_resize
							a    =			size if size is not None else self.size
							a    =			get_size_dict(__lowerCamelCase							)
							a    =			resample if resample is not None else self.resample
							a    =			apply_ocr if apply_ocr is not None else self.apply_ocr
							a    =			ocr_lang if ocr_lang is not None else self.ocr_lang
							a    =			tesseract_config if tesseract_config is not None else self.tesseract_config

							a    =			make_list_of_images(__lowerCamelCase							)

							if not valid_images(__lowerCamelCase							):
										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."							)

							# All transformations expect numpy arrays.
							a    =			[to_numpy_array(__lowerCamelCase							) for image in images]

							if apply_ocr:
										requires_backends(self					,      "pytesseract"							)
										a    =			[]
										a    =			[]
										for image in images:
													a    ,		a    =			apply_tesseract(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
													words_batch.append(__lowerCamelCase							)
													boxes_batch.append(__lowerCamelCase							)

							if do_resize:
										a    =			[self.resize(image=__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase							) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							a    =			[flip_channel_order(__lowerCamelCase							) for image in images]
							a    =			[to_channel_dimension_format(__lowerCamelCase					,      __lowerCamelCase							) for image in images]

							a    =			BatchFeature(data={"pixel_values": images}					,      tensor_type=__lowerCamelCase							)

							if apply_ocr:
										a    =			words_batch
										a    =			boxes_batch
							return data






