


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None					):


		"""simple docstring"""




		assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
		lowercase__						=				nn.Parameter(SCREAMING_SNAKE_CASE					)
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
				lowercase__						=				nn.Parameter(SCREAMING_SNAKE_CASE					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				np.asarray(weights[0]					)
		lowercase__						=				np.asarray(weights[1]					)
		lowercase__						=				np.asarray(weights[2]					)

		set_param(
		    torch_layer.self_attention.query_key						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(1						,    2					).contiguous().view(-1						,    SCREAMING_SNAKE_CASE					)						,    )
		set_param(
		    torch_layer.self_attention.value						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(1						,    2					).contiguous().view(-1						,    SCREAMING_SNAKE_CASE					)						,    )
		set_param(
		    torch_layer.output.dense						,    torch.tensor(SCREAMING_SNAKE_CASE					).view(-1						,    SCREAMING_SNAKE_CASE					).contiguous().transpose(0						,    1					)						,    )
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				np.asarray(weights[0]					)
		lowercase__						=				np.asarray(weights[1]					)
		lowercase__						=				np.asarray(weights[2]					)
		lowercase__						=				np.asarray(weights[3]					)

		set_param(
		    torch_layer.self_attention.query						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(1						,    2					).contiguous().view(-1						,    SCREAMING_SNAKE_CASE					)						,    )
		set_param(
		    torch_layer.self_attention.key						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(1						,    2					).contiguous().view(-1						,    SCREAMING_SNAKE_CASE					)						,    )
		set_param(
		    torch_layer.self_attention.value						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(1						,    2					).contiguous().view(-1						,    SCREAMING_SNAKE_CASE					)						,    )
		set_param(
		    torch_layer.output.dense						,    torch.tensor(SCREAMING_SNAKE_CASE					).view(-1						,    SCREAMING_SNAKE_CASE					).contiguous().transpose(0						,    1					)						,    )
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				weights[0][0][0]
		lowercase__						=				np.asarray(layer_norm_a[0]					)
		lowercase__						=				np.asarray(layer_norm_a[1]					)
		set_param(
		    torch_block.attention.layer_norm						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )

		# lsh weights + output
		lowercase__						=				weights[0][1]
		if len(SCREAMING_SNAKE_CASE					) < 4:
				set_layer_weights_in_torch_lsh(SCREAMING_SNAKE_CASE						,    torch_block.attention						,    SCREAMING_SNAKE_CASE					)
		else:
				set_layer_weights_in_torch_local(SCREAMING_SNAKE_CASE						,    torch_block.attention						,    SCREAMING_SNAKE_CASE					)

		# intermediate weighs
		lowercase__						=				weights[2][0][1][2]

		# Chunked Feed Forward
		if len(SCREAMING_SNAKE_CASE					) == 4:
				lowercase__						=				intermediate_weights[2]

		# layernorm 2
		lowercase__						=				np.asarray(intermediate_weights[0][0]					)
		lowercase__						=				np.asarray(intermediate_weights[0][1]					)
		set_param(
		    torch_block.feed_forward.layer_norm						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )

		# intermediate dense
		lowercase__						=				np.asarray(intermediate_weights[1][0]					)
		lowercase__						=				np.asarray(intermediate_weights[1][1]					)
		set_param(
		    torch_block.feed_forward.dense.dense						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(0						,    1					).contiguous()						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )

		# intermediate out
		lowercase__						=				np.asarray(intermediate_weights[4][0]					)
		lowercase__						=				np.asarray(intermediate_weights[4][1]					)
		set_param(
		    torch_block.feed_forward.output.dense						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(0						,    1					).contiguous()						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				torch_model.reformer

		# word embeds
		lowercase__						=				np.asarray(weights[1]					)
		set_param(
		    torch_model_reformer.embeddings.word_embeddings						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )

		if isinstance(weights[3]						,    SCREAMING_SNAKE_CASE					):
				lowercase__						=				torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights					)					):
						lowercase__						=				np.asarray(weights[3][emb_idx][0]					)
						assert (
						    position_embeddings.weights[emb_idx].shape == emb_weights.shape
						), f'{position_embeddings[emb_idx]} emb does not match'
						lowercase__						=				nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE					)					)

		lowercase__						=				weights[5]
		assert len(torch_model_reformer.encoder.layers					) * 4 == len(
		    SCREAMING_SNAKE_CASE					), "HF and trax model do not have the same number of layers"
		for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers					):
				lowercase__						=				trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		# output layer norm
		lowercase__						=				np.asarray(weights[7][0]					)
		lowercase__						=				np.asarray(weights[7][1]					)
		set_param(
		    torch_model_reformer.encoder.layer_norm						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )

		# output embeddings
		lowercase__						=				np.asarray(weights[9][0]					)
		lowercase__						=				np.asarray(weights[9][1]					)
		set_param(
		    torch_model.lm_head.decoder						,    torch.tensor(SCREAMING_SNAKE_CASE					).transpose(0						,    1					).contiguous()						,    torch.tensor(SCREAMING_SNAKE_CASE					)						,    )
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE					)
		print(f'Building PyTorch model from configuration: {config}'					)
		lowercase__						=				ReformerModelWithLMHead(SCREAMING_SNAKE_CASE					)

		with open(SCREAMING_SNAKE_CASE						,    '''rb'''					) as f:
				lowercase__						=				pickle.load(SCREAMING_SNAKE_CASE					)['''weights''']

		set_model_weights_in_torch(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    config.hidden_size					)

		# Save pytorch-model
		print(f'Save PyTorch model to {pytorch_dump_path}'					)
		torch.save(model.state_dict()						,    SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
			)
			parser.add_argument(
			    '--config_file',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        'The config json file corresponding to the pre-trained Reformer model. \n'
			        'This specifies the model architecture.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			lowerCAmelCase	=							parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)
		lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE					)
		return flax_params
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				{}

		lowercase__						=				{
		    '''token_embedder''': '''embeddings''',
		    '''encoder_norm''': '''layernorm''',
		    '''kernel''': '''weight''',
		    '''.out''': '''.output''',
		    '''scale''': '''weight''',
		    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
		    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
		}

		lowercase__						=				{
		    '''query''': '''attention.query''',
		    '''key''': '''attention.key''',
		    '''value''': '''attention.value''',
		    '''output.dense''': '''output''',
		    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
		    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
		    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
		    '''mlp.''': '''mlp.DenseReluDense.''',
		    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
		    '''self_attention.o''': '''self_attention.attention.o''',
		    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
		    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
		    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
		    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
		}

		for key in flax_dict.keys():
				if "target" in key:
						# remove the first prefix from the key
						lowercase__						=				'''.'''.join(key[1:]					)

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								lowercase__						=				re.sub(R'''layers_(\d+)'''						,    R'''layer.\1'''						,    SCREAMING_SNAKE_CASE					)
								lowercase__						=				new_key.replace('''encoder'''						,    '''encoder.encoder'''					)

						elif "layers" in new_key and "decoder" in new_key:
								# use regex to replace the layer number
								lowercase__						=				re.sub(R'''layers_(\d+)'''						,    R'''layer.\1'''						,    SCREAMING_SNAKE_CASE					)

						lowercase__						=				flax_dict[key]

		lowercase__						=				{}
		# convert converted_dict into torch format
		for key in converted_dict.keys():
				if ("embed_tokens" not in key) and ("embedder" not in key):
						lowercase__						=				torch.from_numpy(converted_dict[key].T					)
				else:
						lowercase__						=				torch.from_numpy(converted_dict[key]					)

		return converted_torch_dict
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				get_flax_param(SCREAMING_SNAKE_CASE					)

		if not use_large:
				lowercase__						=				PixaStructVisionConfig()
				lowercase__						=				PixaStructTextConfig()
		else:
				lowercase__						=				PixaStructVisionConfig(
				    hidden_size=15_36						,    d_ff=39_68						,    num_attention_heads=24						,    num_hidden_layers=18					)
				lowercase__						=				PixaStructTextConfig(hidden_size=15_36						,    d_ff=39_68						,    num_heads=24						,    num_layers=18					)
		lowercase__						=				PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,    text_config=decoder_config.to_dict()						,    is_vqa=SCREAMING_SNAKE_CASE					)

		lowercase__						=				PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE					)

		lowercase__						=				rename_and_convert_flax_params(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

		lowercase__						=				AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
		lowercase__						=				PixaStructImageProcessor()
		lowercase__						=				PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE						,    tokenizer=SCREAMING_SNAKE_CASE					)

		if use_large:
				lowercase__						=				40_96

		lowercase__						=				True

		# mkdir if needed
		os.makedirs(SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)

		model.save_pretrained(SCREAMING_SNAKE_CASE					)
		processor.save_pretrained(SCREAMING_SNAKE_CASE					)

		print('''Model saved in {}'''.format(SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
			parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
			parser.add_argument('--use_large', action='store_true', help='Use large model.')
			parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
			lowerCAmelCase	=							parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)


