


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if isinstance(SCREAMING_SNAKE_CASE						,    np.ndarray					):
				return list(tensor.shape					)

		lowercase__						=				tf.shape(SCREAMING_SNAKE_CASE					)

		if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE					):
				return dynamic

		lowercase__						=				tensor.shape.as_list()

		return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE					)]
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = None						,    SCREAMING_SNAKE_CASE = None					):


		"""simple docstring"""




		return tf.nn.softmax(logits=logits + 1E-9						,    axis=SCREAMING_SNAKE_CASE						,    name=SCREAMING_SNAKE_CASE					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=1E-5						,    SCREAMING_SNAKE_CASE=-1					):


		"""simple docstring"""




		if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):
				raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''					)

		# Get mean and variance on the axis to be normalized
		lowercase__					,		lowercase__						=				tf.nn.moments(SCREAMING_SNAKE_CASE						,    axes=[axis]						,    keepdims=SCREAMING_SNAKE_CASE					)

		if axis != -1:
				# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
				# on every dimension except axis
				lowercase__						=				[1] * inputs.shape.rank
				lowercase__						=				shape_list(SCREAMING_SNAKE_CASE					)[axis]
				lowercase__						=				tf.reshape(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
				lowercase__						=				tf.reshape(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		# Compute layer normalization using the batch_normalization
		# function.
		lowercase__						=				tf.nn.batch_normalization(
		    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    offset=SCREAMING_SNAKE_CASE						,    scale=SCREAMING_SNAKE_CASE						,    variance_epsilon=SCREAMING_SNAKE_CASE						,    )
		return outputs
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0						,    SCREAMING_SNAKE_CASE=-1					):


		"""simple docstring"""




		if end_dim < 0:
				end_dim += input.shape.rank
		if start_dim < 0:
				start_dim += input.shape.rank

		if start_dim == end_dim:
				return input

		lowercase__						=				tf.shape(SCREAMING_SNAKE_CASE					)
		lowercase__						=				tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]					)
		lowercase__						=				tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,    axis=0					)
		return tf.reshape(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if not isinstance(SCREAMING_SNAKE_CASE						,    tf.Tensor					):
				lowercase__						=				tf.convert_to_tensor(SCREAMING_SNAKE_CASE					)  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				lowercase__						=				encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				lowercase__						=				encoder_attention_mask[:, None, None, :]
		# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
		# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
		# /transformer/transformer_layers.py#L270
		# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
		# encoder_extended_attention_mask.transpose(-1, -2))
		lowercase__						=				(
		    tf.cast(1						,    encoder_attention_mask.dtype					) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = "input_ids"					):


		"""simple docstring"""




		tf.debugging.assert_less(
		    SCREAMING_SNAKE_CASE						,    tf.cast(SCREAMING_SNAKE_CASE						,    dtype=tensor.dtype					)						,    message=(
		        f'The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE					)}) must be smaller than the embedding '
		        f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
		    )						,    )
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				6_45_12
		# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
		# because in that case even chunking the array would not make the saving
		# possible.
		lowercase__						=				[x for x in data if len(SCREAMING_SNAKE_CASE					) > HDF5_OBJECT_HEADER_LIMIT]

		# Expecting this to never be true.
		if bad_attributes:
				raise RuntimeError(
				    '''The following attributes cannot be saved to HDF5 file because '''
				    f'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
				    f'bytes: {bad_attributes}'					)

		lowercase__						=				np.asarray(SCREAMING_SNAKE_CASE					)

		lowercase__						=				1
		lowercase__						=				np.array_split(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		# This will never loop forever thanks to the test above.
		while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data					):
				num_chunks += 1
				lowercase__						=				np.array_split(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE					):
						lowercase__						=				chunk_data
		else:
				lowercase__						=				data
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if name in group.attrs:
				lowercase__						=				[n.decode('''utf8'''					) if hasattr(SCREAMING_SNAKE_CASE						,    '''decode'''					) else n for n in group.attrs[name]]
		else:
				lowercase__						=				[]
				lowercase__						=				0
				while "%s%d" % (name, chunk_id) in group.attrs:
						data.extend(
						    [n.decode('''utf8'''					) if hasattr(SCREAMING_SNAKE_CASE						,    '''decode'''					) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]					)
						chunk_id += 1
		return data
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE					):
				if isinstance(SCREAMING_SNAKE_CASE						,    tf.Tensor					) and t.shape.rank == 1:
						return tf.expand_dims(SCREAMING_SNAKE_CASE						,    axis=-1					)
				return t

		return tf.nest.map_structure(_expand_single_ad_tensor						,    SCREAMING_SNAKE_CASE					)






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
			)


