




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCAmelCase__				     = "bert-base-cased"
UpperCAmelCase__				     = "google/pegasus-xsum"
UpperCAmelCase__				     = [" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCAmelCase__				     = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCAmelCase__				     = "patrickvonplaten/t5-tiny-random"
UpperCAmelCase__				     = "sshleifer/bart-tiny-random"
UpperCAmelCase__				     = "sshleifer/tiny-mbart"
UpperCAmelCase__				     = "sshleifer/tiny-marian-en-de"







def 		_A(       UpperCamelCase__				,       UpperCamelCase__				)    -> str:
							'''simple docstring'''


							__lowercase      =    "\n".join(__SCREAMING_SNAKE_CASE				)
							Path(__SCREAMING_SNAKE_CASE				).open('''w'''				).writelines(__SCREAMING_SNAKE_CASE				)







def 		_A(       UpperCamelCase__				)    -> Optional[int]:
							'''simple docstring'''


							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE				,       F'{split}.source'				)				,       __SCREAMING_SNAKE_CASE				)
														_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE				,       F'{split}.target'				)				,       __SCREAMING_SNAKE_CASE				)
							return tmp_dir







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

						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]				,							)
						@slow
						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     List[str]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    AutoTokenizer.from_pretrained(lowercase_				)
													__lowercase      =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
													__lowercase      =    max(len(tokenizer.encode(lowercase_				)				) for a in ARTICLES				)
													__lowercase      =    max(len(tokenizer.encode(lowercase_				)				) for a in SUMMARIES				)
													__lowercase      =    4
													__lowercase      =    8
													assert max_len_target > max_src_len  # Will be truncated
													assert max_len_source > max_src_len  # Will be truncated
													__lowercase      =    "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
													__lowercase      =    SeqaSeqDataset(
													    lowercase_				,							data_dir=lowercase_				,							type_path='''train'''				,							max_source_length=lowercase_				,							max_target_length=lowercase_				,							src_lang=lowercase_				,							tgt_lang=lowercase_				,							)
													__lowercase      =    DataLoader(lowercase_				,							batch_size=2				,							collate_fn=train_dataset.collate_fn				)
													for batch in dataloader:
																				assert isinstance(lowercase_				,							lowercase_				)
																				assert batch["attention_mask"].shape == batch["input_ids"].shape
																				# show that articles were trimmed.
																				assert batch["input_ids"].shape[1] == max_src_len
																				# show that targets are the same len
																				assert batch["labels"].shape[1] == max_tgt_len
																				if tok_name != MBART_TINY:
																											continue
																				# check language codes in correct place
																				__lowercase      =    shift_tokens_right(batch['''labels''']				,							tokenizer.pad_token_id				)
																				assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
																				assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
																				assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
																				assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

																				break  # No need to test every batch






						@parameterized.expand([BART_TINY, BERT_BASE_CASED]				)
						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     Optional[int]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    AutoTokenizer.from_pretrained(lowercase_				)
													__lowercase      =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
													__lowercase      =    max(len(tokenizer.encode(lowercase_				)				) for a in ARTICLES				)
													__lowercase      =    max(len(tokenizer.encode(lowercase_				)				) for a in SUMMARIES				)
													__lowercase      =    4
													__lowercase      =    LegacySeqaSeqDataset(
													    lowercase_				,							data_dir=lowercase_				,							type_path='''train'''				,							max_source_length=20				,							max_target_length=lowercase_				,							)
													__lowercase      =    DataLoader(lowercase_				,							batch_size=2				,							collate_fn=train_dataset.collate_fn				)
													for batch in dataloader:
																				assert batch["attention_mask"].shape == batch["input_ids"].shape
																				# show that articles were trimmed.
																				assert batch["input_ids"].shape[1] == max_len_source
																				assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
																				# show that targets were truncated
																				assert batch["labels"].shape[1] == trunc_target  # Truncated
																				assert max_len_target > trunc_target  # Truncated
																				break  # No need to test every batch






						def     UpperCAmelCase_      ( self :     str				)    ->						str:
													"""simple docstring"""


													__lowercase      =    AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

													__lowercase      =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
													__lowercase      =    tmp_dir.joinpath('''train.source'''				).open().readlines()
													__lowercase      =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
													pack_data_dir(lowercase_				,							lowercase_				,							128				,							lowercase_				)
													__lowercase      =    {x.name for x in tmp_dir.iterdir()}
													__lowercase      =    {x.name for x in save_dir.iterdir()}
													__lowercase      =    save_dir.joinpath('''train.source'''				).open().readlines()
													# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
													# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
													assert len(lowercase_				) < len(lowercase_				)
													assert len(lowercase_				) == 1
													assert len(packed_examples[0]				) == sum(len(lowercase_				) for x in orig_examples				)
													assert orig_paths == new_paths






						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,							reason='''This test requires fairseq'''				)
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						int:
													"""simple docstring"""


													if not FAIRSEQ_AVAILABLE:
																				return
													__lowercase      =    self._get_dataset(max_len=64				)
													__lowercase      =    64
													__lowercase      =    ds.make_dynamic_sampler(lowercase_				,							required_batch_size_multiple=lowercase_				)
													__lowercase      =    [len(lowercase_				) for x in batch_sampler]
													assert len(set(lowercase_				)				) > 1  # it's not dynamic batch size if every batch is the same length
													assert sum(lowercase_				) == len(lowercase_				)  # no dropped or added examples
													__lowercase      =    DataLoader(lowercase_				,							batch_sampler=lowercase_				,							collate_fn=ds.collate_fn				,							num_workers=2				)
													__lowercase      =    []
													__lowercase      =    []
													for batch in data_loader:
																				__lowercase      =    batch["input_ids"].shape
																				__lowercase      =    src_shape[0]
																				assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																				__lowercase      =    np.product(batch['''input_ids'''].shape				)
																				num_src_per_batch.append(lowercase_				)
																				if num_src_tokens > (max_tokens * 1.1):
																											failures.append(lowercase_				)
													assert num_src_per_batch[0] == max(lowercase_				)
													if failures:
																				raise AssertionError(f'too many tokens in {len(lowercase_				)} batches'				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self._get_dataset(max_len=512				)
													__lowercase      =    2
													__lowercase      =    ds.make_sortish_sampler(lowercase_				,							shuffle=lowercase_				)

													__lowercase      =    DataLoader(lowercase_				,							batch_size=lowercase_				,							collate_fn=ds.collate_fn				,							num_workers=2				)
													__lowercase      =    DataLoader(lowercase_				,							batch_size=lowercase_				,							collate_fn=ds.collate_fn				,							num_workers=2				,							sampler=lowercase_				)

													__lowercase      =    tokenizer.pad_token_id

													def count_pad_tokens(lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tuple="input_ids"				):
																				return [batch[k].eq(lowercase_				).sum().item() for batch in data_loader]

													assert sum(count_pad_tokens(lowercase_				,							k='''labels'''				)				) < sum(count_pad_tokens(lowercase_				,							k='''labels'''				)				)
													assert sum(count_pad_tokens(lowercase_				)				) < sum(count_pad_tokens(lowercase_				)				)
													assert len(lowercase_				) == len(lowercase_				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     List[Any]=1_000				,							lowerCamelCase__ :     List[Any]=128				)    ->						int:
													"""simple docstring"""


													if os.getenv('''USE_REAL_DATA'''				,							lowercase_				):
																				__lowercase      =    "examples/seq2seq/wmt_en_ro"
																				__lowercase      =    max_len * 2 * 64
																				if not Path(lowercase_				).joinpath('''train.len'''				).exists():
																											save_len_file(lowercase_				,							lowercase_				)
													else:
																				__lowercase      =    "examples/seq2seq/test_data/wmt_en_ro"
																				__lowercase      =    max_len * 4
																				save_len_file(lowercase_				,							lowercase_				)

													__lowercase      =    AutoTokenizer.from_pretrained(lowercase_				)
													__lowercase      =    SeqaSeqDataset(
													    lowercase_				,							data_dir=lowercase_				,							type_path='''train'''				,							max_source_length=lowercase_				,							max_target_length=lowercase_				,							n_obs=lowercase_				,							)
													return ds, max_tokens, tokenizer






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    self._get_dataset()
													__lowercase      =    set(DistributedSortishSampler(lowercase_				,							256				,							num_replicas=2				,							rank=0				,							add_extra_examples=lowercase_				)				)
													__lowercase      =    set(DistributedSortishSampler(lowercase_				,							256				,							num_replicas=2				,							rank=1				,							add_extra_examples=lowercase_				)				)
													assert idsa.intersection(lowercase_				) == set()






						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]				,							)
						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     int				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    AutoTokenizer.from_pretrained(lowercase_				,							use_fast=lowercase_				)
													if tok_name == MBART_TINY:
																				__lowercase      =    SeqaSeqDataset(
																				    lowercase_				,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				,							type_path='''train'''				,							max_source_length=4				,							max_target_length=8				,							src_lang='''EN'''				,							tgt_lang='''FR'''				,							)
																				__lowercase      =    train_dataset.dataset_kwargs
																				assert "src_lang" in kwargs and "tgt_lang" in kwargs
													else:
																				__lowercase      =    SeqaSeqDataset(
																				    lowercase_				,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				,							type_path='''train'''				,							max_source_length=4				,							max_target_length=8				,							)
																				__lowercase      =    train_dataset.dataset_kwargs
																				assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																				assert len(lowercase_				) == 1 if tok_name == BART_TINY else len(lowercase_				) == 0












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(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''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(UpperCamelCase__				,       UpperCamelCase__				)

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

																					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'''				,       UpperCamelCase__				)
																												__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'''				,       UpperCamelCase__				)

																					__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(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

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

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

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

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

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


if __name__ == "__main__":
			UpperCAmelCase__				     = 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.")
			UpperCAmelCase__				     = parser.parse_args()

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






