
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A							=logging.get_logger(__name__)

__A							={
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'git_vision_model'



	def __init__(       self		,    lowercase=768		,    lowercase=3072		,    lowercase=12		,    lowercase=12		,    lowercase=3		,    lowercase=224		,    lowercase=16		,    lowercase="quick_gelu"		,    lowercase=1e-5		,    lowercase=0.0		,    lowercase=0.0_2		,    **lowercase		,    )							->			Union[str, Any]:
					super().__init__(**lowercase					)

					lowerCamelCase_         =     hidden_size
					lowerCamelCase_         =     intermediate_size
					lowerCamelCase_         =     num_hidden_layers
					lowerCamelCase_         =     num_attention_heads
					lowerCamelCase_         =     num_channels
					lowerCamelCase_         =     patch_size
					lowerCamelCase_         =     image_size
					lowerCamelCase_         =     initializer_range
					lowerCamelCase_         =     attention_dropout
					lowerCamelCase_         =     layer_norm_eps
					lowerCamelCase_         =     hidden_act



	@classmethod
	def        SCREAMING_SNAKE_CASE_(       cls		,    lowercase		,    **lowercase					)							->			"PretrainedConfig":
					cls._set_token_in_kwargs(lowercase					)

					lowerCamelCase_      ,       lowerCamelCase_         =     cls.get_config_dict(lowercase		,    **lowercase					)

					# get the vision config dict if we are loading from GITConfig
					if config_dict.get("model_type"					) == "git":
									lowerCamelCase_         =     config_dict["vision_config"]

					if "model_type" in config_dict and hasattr(cls		,    "model_type"					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

					return cls.from_dict(lowercase		,    **lowercase					)




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'git'



	def __init__(       self		,    lowercase=None		,    lowercase=30522		,    lowercase=768		,    lowercase=6		,    lowercase=12		,    lowercase=3072		,    lowercase="gelu"		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=1024		,    lowercase=0.0_2		,    lowercase=1e-12		,    lowercase=0		,    lowercase="absolute"		,    lowercase=True		,    lowercase=False		,    lowercase=101		,    lowercase=102		,    lowercase=None		,    **lowercase		,    )							->			int:
					super().__init__(bos_token_id=lowercase		,    eos_token_id=lowercase		,    pad_token_id=lowercase		,    **lowercase					)

					if vision_config is None:
									lowerCamelCase_         =     {}
									logger.info("vision_config is None. initializing the GitVisionConfig with default values."					)

					lowerCamelCase_         =     GitVisionConfig(**lowercase					)
					lowerCamelCase_         =     vocab_size
					lowerCamelCase_         =     hidden_size
					lowerCamelCase_         =     num_hidden_layers
					lowerCamelCase_         =     num_attention_heads
					lowerCamelCase_         =     hidden_act
					lowerCamelCase_         =     intermediate_size
					lowerCamelCase_         =     hidden_dropout_prob
					lowerCamelCase_         =     attention_probs_dropout_prob
					lowerCamelCase_         =     max_position_embeddings
					lowerCamelCase_         =     initializer_range
					lowerCamelCase_         =     layer_norm_eps
					lowerCamelCase_         =     position_embedding_type
					lowerCamelCase_         =     use_cache
					lowerCamelCase_         =     tie_word_embeddings
					lowerCamelCase_         =     num_image_with_embedding

					lowerCamelCase_         =     bos_token_id
					lowerCamelCase_         =     eos_token_id



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     copy.deepcopy(self.__dict__					)
					lowerCamelCase_         =     self.vision_config.to_dict()
					lowerCamelCase_         =     self.__class__.model_type
					return output




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__A							=logging.getLogger(__name__)





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     argparse.ArgumentParser(
				    description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."       )
				parser.add_argument(
				    "--dataset_name"      ,					type=lowerCamelCase__      ,					default="wikitext"      ,					help="Name of the training. Explore datasets at: hf.co/datasets."      ,					)
				parser.add_argument(
				    "--dataset_config"      ,					type=lowerCamelCase__      ,					default="wikitext-103-raw-v1"      ,					help="Configuration name of the dataset."       )
				parser.add_argument(
				    "--tokenizer_name_or_path"      ,					type=lowerCamelCase__      ,					default="sayakpaul/unigram-tokenizer-wikitext"      ,					help="Tokenizer identifier. Can be a local filepath or a Hub identifier."      ,					)
				parser.add_argument(
				    "--shard_size"      ,					type=lowerCamelCase__      ,					default=1_0_0_0      ,					help="Number of entries to go in a single shard."      ,					)
				parser.add_argument("--split"      ,					type=lowerCamelCase__      ,					default="train"      ,					choices=["train", "test", "validation"]       )
				parser.add_argument(
				    "--limit"      ,					default=lowerCamelCase__      ,					type=lowerCamelCase__      ,					help="Limit the number of shards (used for debugging)."      ,					)
				parser.add_argument(
				    "--max_length"      ,					type=lowerCamelCase__      ,					default=5_1_2      ,					help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
				    " sequence length that is a multiple of 8."      ,					)
				parser.add_argument(
				    "--output_dir"      ,					default="tf-tpu"      ,					type=lowerCamelCase__      ,					help="Output directory where the TFRecord shards will be saved. If the"
				    " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
				    " shards will be directly saved to a Google Cloud Storage bucket."      ,					)

				lowerCamelCase_         =     parser.parse_args()
				return args





def      lowerCamelCase_					(							lowerCamelCase__       ):
				def fn(lowerCamelCase__       ):
								return tokenizer(examples["text"]       )

				return fn





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				for i in range(len(tokenized_data["input_ids"]       )       ):
								lowerCamelCase_         =     {
								    "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]       )       ),
								    "attention_mask": tf.train.Feature(
								        intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]       )       ),
								}
								lowerCamelCase_         =     tf.train.Features(feature=lowerCamelCase__       )
								lowerCamelCase_         =     tf.train.Example(features=lowerCamelCase__       )
								lowerCamelCase_         =     example.SerializeToString()
								records.append(lowerCamelCase__       )
				return records





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     datasets.load_dataset(args.dataset_name      ,					args.dataset_config      ,					split=args.split       )

				if args.limit is not None:
								lowerCamelCase_         =     min(len(lowerCamelCase__       )      ,					args.limit       )
								lowerCamelCase_         =     dataset.select(range(lowerCamelCase__       )       )
								print(F'Limiting the dataset to {args.limit} entries.'       )

				lowerCamelCase_         =     AutoTokenizer.from_pretrained(args.tokenizer_name_or_path       )

				# Handle output directory creation.
				# For serializing into a Google Cloud Storage Bucket, one needs to first
				# create a bucket.
				if "gs" not in args.output_dir:
								if not os.path.exists(args.output_dir       ):
												os.makedirs(args.output_dir       )
								lowerCamelCase_         =     os.path.join(args.output_dir      ,					args.split       )
								if not os.path.exists(lowerCamelCase__       ):
												os.makedirs(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     os.path.join(args.output_dir      ,					args.split       )

				# Tokenize the whole dataset at once.
				lowerCamelCase_         =     tokenize_function(lowerCamelCase__       )
				lowerCamelCase_         =     dataset.map(lowerCamelCase__      ,					batched=lowerCamelCase__      ,					num_proc=4      ,					remove_columns=["text"]       )

				# We need to concatenate all our texts together, and then split the result
				# into chunks of a fixed size, which we will call block_size. To do this, we
				# will use the map method again, with the option batched=True. When we use batched=True,
				# the function we pass to map() will be passed multiple inputs at once, allowing us
				# to group them into more or fewer examples than we had in the input.
				# This allows us to create our new fixed-length samples. The advantage of this
				# method is that we don't lose a whole lot of content from the dataset compared to the
				# case where we simply tokenize with a pre-defined max_length.

				def group_texts(lowerCamelCase__       ):
								# Concatenate all texts.
								lowerCamelCase_         =     {k: sum(examples[k]      ,					[]       ) for k in examples.keys()}
								lowerCamelCase_         =     len(concatenated_examples[list(examples.keys()       )[0]]       )
								# We drop the small remainder, though you could add padding instead if the model supports it
								# In this, as in all things, we advise you to follow your heart 🫀
								lowerCamelCase_         =     (total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								lowerCamelCase_         =     {
								    k: [t[i : i + args.max_length] for i in range(0      ,					lowerCamelCase__      ,					args.max_length       )]
								    for k, t in concatenated_examples.items()
								}
								return result

				lowerCamelCase_         =     dataset_tokenized.map(lowerCamelCase__      ,					batched=lowerCamelCase__      ,					batch_size=1_0_0_0      ,					num_proc=4       )

				lowerCamelCase_         =     0
				lowerCamelCase_         =     0
				for shard in range(0      ,					len(lowerCamelCase__       )      ,					args.shard_size       ):
								lowerCamelCase_         =     grouped_dataset[shard : shard + args.shard_size]
								lowerCamelCase_         =     len(dataset_snapshot["input_ids"]       )
								lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					F'dataset-{shard_count}-{records_containing}.tfrecord'       )
								lowerCamelCase_         =     get_serialized_examples(lowerCamelCase__       )

								with tf.io.TFRecordWriter(lowerCamelCase__       ) as out_file:
												for i in range(len(lowerCamelCase__       )       ):
																lowerCamelCase_         =     serialized_examples[i]
																out_file.write(lowerCamelCase__       )
												print("Wrote file {} containing {} records".format(lowerCamelCase__      ,					lowerCamelCase__       )       )

								shard_count += 1
								total_records += records_containing

				with open(F'split-{args.split}-records-count.txt'      ,					"w"       ) as f:
								print(F'Total {args.split} records: {total_records}'      ,					file=lowerCamelCase__       )


if __name__ == "__main__":
			__A							=parse_args()
			main(args)


