"""simple docstring"""




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
     from PIL import Image
else:
     class 						__A							:






          @staticmethod
          def     __A  (		*a__				,     **a__  ):
            pass





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Image	)       ->      str:
  _lowerCAmelCase						:       str  			=     hashlib.mda(image.tobytes()	)
  return m.hexdigest()[:10]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Image	)       ->      Dict:
  _lowerCAmelCase						:       Dict  			=     np.array(_lowerCamelCase	)
  _lowerCAmelCase						:       Union[str, Any]  			=     npimg.shape
  return {"hash": hashimage(_lowerCamelCase	), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 						__A							( unittest.TestCase		):
     _UpperCamelCase							:			Optional[Any]							=		dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
     _UpperCamelCase							:			Union[str, Any]							=		dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     MaskGenerationPipeline(model=a__				,     image_processor=a__  )
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]






     def     __A  (		self				,     a__				,     a__  ):
       pass






     @require_tf
     @unittest.skip("""Image segmentation not implemented in TF"""  )
     def     __A  (		self  ):
       pass






     @slow
     @require_torch
     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     pipeline("""mask-generation"""				,     model="""facebook/sam-vit-huge"""  )

       _lowerCAmelCase						:       Optional[int]  			=     image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""				,     points_per_batch=256  )

       # Shortening by hashing
       _lowerCAmelCase						:       Dict  			=     []
       for i, o in enumerate(outputs["""masks"""]  ):
         new_outupt += [{"mask": mask_to_test_readable(a__  ), "scores": outputs["scores"][i]}]

       # fmt: off
       self.assertEqual(
           nested_simplify(a__				,     decimals=4  )				,     [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_4_4_4},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9_9_6_7},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.9_9_3},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9_9_0_9},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9_8_7_9},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9_8_3_4},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9_7_1_6},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9_6_1_2},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9_5_9_9},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9_5_5_2},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9_5_3_2},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9_5_1_6},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9_4_9_9},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9_4_8_3},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9_4_6_4},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.9_4_3},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9_4_0_8},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9_3_3_5},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9_3_2_6},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9_2_6_2},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8_9_9_9},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8_9_8_6},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8_9_8_4},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8_8_7_3},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8_8_7_1}
           ]				,     )
       # fmt: on






     @require_torch
     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """facebook/sam-vit-huge"""
       _lowerCAmelCase						:       Any  			=     pipeline("""mask-generation"""				,     model=a__  )

       _lowerCAmelCase						:       Optional[int]  			=     image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""				,     pred_iou_thresh=1				,     points_per_batch=256  )

       # Shortening by hashing
       _lowerCAmelCase						:       Tuple  			=     []
       for i, o in enumerate(outputs["""masks"""]  ):
         new_outupt += [{"mask": mask_to_test_readable(a__  ), "scores": outputs["scores"][i]}]

       self.assertEqual(
           nested_simplify(a__				,     decimals=4  )				,     [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0_4_4_4},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0_2_1_0},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0_1_6_7},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0_1_3_2},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0_0_5_3},
           ]				,     )











'''simple docstring'''







import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowerCAmelCase_					:    List[str]      	=			logging.getLogger(__name__)

lowerCAmelCase_					:    List[Any]      	=			tf.data.AUTOTUNE






def  _lowerCamelCase				( )	->	Optional[int]:
				_a								= argparse.ArgumentParser(description="Train a masked language model on TPU."						)
				parser.add_argument(
				    "--pretrained_model_config"    ,							type=lowercase    ,							default="roberta-base"    ,							help="The model config to use. Note that we don't copy the model's weights, only the config!"    ,							)
				parser.add_argument(
				    "--tokenizer"    ,							type=lowercase    ,							default="unigram-tokenizer-wikitext"    ,							help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."    ,							)

				parser.add_argument(
				    "--per_replica_batch_size"    ,							type=lowercase    ,							default=8    ,							help="Batch size per TPU core."    ,							)

				parser.add_argument(
				    "--no_tpu"    ,							action="store_true"    ,							help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."    ,							)

				parser.add_argument(
				    "--tpu_name"    ,							type=lowercase    ,							help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."    ,							default="local"    ,							)

				parser.add_argument(
				    "--tpu_zone"    ,							type=lowercase    ,							help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."    ,							)

				parser.add_argument(
				    "--gcp_project"    ,							type=lowercase    ,							help="Google cloud project name. Only used for non-Colab TPU nodes."						)

				parser.add_argument(
				    "--bfloat16"    ,							action="store_true"    ,							help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."    ,							)

				parser.add_argument(
				    "--train_dataset"    ,							type=lowercase    ,							help="Path to training dataset to load. If the path begins with `gs://`"
				    " then the dataset will be loaded from a Google Cloud Storage bucket."    ,							)

				parser.add_argument(
				    "--shuffle_buffer_size"    ,							type=lowercase    ,							default=2**18    ,							help="Size of the shuffle buffer (in samples)"    ,							)

				parser.add_argument(
				    "--eval_dataset"    ,							type=lowercase    ,							help="Path to evaluation dataset to load. If the path begins with `gs://`"
				    " then the dataset will be loaded from a Google Cloud Storage bucket."    ,							)

				parser.add_argument(
				    "--num_epochs"    ,							type=lowercase    ,							default=1    ,							help="Number of epochs to train for."    ,							)

				parser.add_argument(
				    "--learning_rate"    ,							type=lowercase    ,							default=1E-4    ,							help="Learning rate to use for training."    ,							)

				parser.add_argument(
				    "--weight_decay_rate"    ,							type=lowercase    ,							default=1E-3    ,							help="Weight decay rate to use for training."    ,							)

				parser.add_argument(
				    "--max_length"    ,							type=lowercase    ,							default=512    ,							help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"    ,							)

				parser.add_argument(
				    "--mlm_probability"    ,							type=lowercase    ,							default=0.15    ,							help="Fraction of tokens to mask during training."    ,							)

				parser.add_argument("--output_dir"    ,							type=lowercase    ,							required=lowercase    ,							help="Path to save model checkpoints to."						)
				parser.add_argument("--hub_model_id"    ,							type=lowercase    ,							help="Model ID to upload to on the Hugging Face Hub."						)

				_a								= parser.parse_args()
				return args






def  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	Optional[int]:
				try:
								if args.tpu_name:
												_a								= tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name    ,							zone=args.tpu_zone    ,							project=args.gcp_project						)
								else:
												_a								= tf.distribute.cluster_resolver.TPUClusterResolver()
				except ValueError:
								raise RuntimeError(
								    "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
								    "--gcp_project. When running on a TPU VM, use --tpu_name local."						)

				tf.config.experimental_connect_to_cluster(lowercase						)
				tf.tpu.experimental.initialize_tpu_system(lowercase						)

				return tpu






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	Any:
				_a								= 0
				for file in file_list:
								_a								= file.split("/"						)[-1]
								_a								= re.search(r"-\d+-(\d+)\.tfrecord"    ,							lowercase						).group(1						)
								_a								= int(lowercase						)
								num_samples += sample_count

				return num_samples






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      Tuple    ,							lowercase						:      List[str]    ,							lowercase						:      Any    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]=None						)	->	int:
				_a								= count_samples(lowercase						)
				_a								= tf.data.Dataset.from_tensor_slices(lowercase						)
				if shuffle:
								_a								= dataset.shuffle(len(lowercase						)						)
				_a								= tf.data.TFRecordDataset(lowercase    ,							num_parallel_reads=lowercase						)
				# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
				_a								= dataset.apply(tf.data.experimental.assert_cardinality(lowercase						)						)
				_a								= dataset.map(lowercase    ,							num_parallel_calls=lowercase						)
				if shuffle:
								assert shuffle_buffer_size is not None
								_a								= dataset.shuffle(args.shuffle_buffer_size						)
				_a								= dataset.batch(lowercase    ,							drop_remainder=lowercase						)
				_a								= dataset.map(lowercase    ,							num_parallel_calls=lowercase						)
				_a								= dataset.prefetch(lowercase						)
				return dataset






def  _lowerCamelCase				( lowercase						:      Union[str, Any]						)	->	Dict:
				if not args.no_tpu:
								_a								= initialize_tpu(lowercase						)
								_a								= tf.distribute.TPUStrategy(lowercase						)
				else:
								_a								= tf.distribute.OneDeviceStrategy(device="/gpu:0"						)

				if args.bfloataa:
								tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"						)

				_a								= AutoTokenizer.from_pretrained(args.tokenizer						)
				_a								= AutoConfig.from_pretrained(args.pretrained_model_config						)
				_a								= tokenizer.vocab_size

				_a								= tf.io.gfile.glob(os.path.join(args.train_dataset    ,							"*.tfrecord"						)						)
				if not training_records:
								raise ValueError(F'No .tfrecord files found in {args.train_dataset}.'						)
				_a								= tf.io.gfile.glob(os.path.join(args.eval_dataset    ,							"*.tfrecord"						)						)
				if not eval_records:
								raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.'						)

				_a								= count_samples(lowercase						)

				_a								= num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				_a								= steps_per_epoch * args.num_epochs

				with strategy.scope():
								_a								= TFAutoModelForMaskedLM.from_config(lowercase						)
								model(model.dummy_inputs						)  # Pass some dummy inputs through the model to ensure all the weights are built
								_a	,					_a								= create_optimizer(
								    num_train_steps=lowercase    ,							num_warmup_steps=total_train_steps // 20    ,							init_lr=args.learning_rate    ,							weight_decay_rate=args.weight_decay_rate    ,							)

								# Transformers models compute the right loss for their task by default when labels are passed, and will
								# use this for training unless you specify your own loss function in compile().
								model.compile(optimizer=lowercase    ,							metrics=["accuracy"]						)

				def decode_fn(lowercase						:      int						):
								_a								= {
								    "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa    ,							shape=(args.max_length,)						),
								    "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa    ,							shape=(args.max_length,)						),
								}
								return tf.io.parse_single_example(lowercase    ,							lowercase						)

				# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
				# use their methods in our data pipeline.
				_a								= DataCollatorForLanguageModeling(
				    tokenizer=lowercase    ,							mlm_probability=args.mlm_probability    ,							mlm=lowercase    ,							return_tensors="tf"						)

				def mask_with_collator(lowercase						:      List[Any]						):
								# TF really needs an isin() function
								_a								= (
								    ~tf.cast(batch["attention_mask"]    ,							tf.bool						)
								    | (batch["input_ids"] == tokenizer.cls_token_id)
								    | (batch["input_ids"] == tokenizer.sep_token_id)
								)
								_a	,					_a								= data_collator.tf_mask_tokens(
								    batch["input_ids"]    ,							vocab_size=len(lowercase						)    ,							mask_token_id=tokenizer.mask_token_id    ,							special_tokens_mask=lowercase    ,							)
								return batch

				_a								= args.per_replica_batch_size * strategy.num_replicas_in_sync

				_a								= prepare_dataset(
				    lowercase    ,							decode_fn=lowercase    ,							mask_fn=lowercase    ,							batch_size=lowercase    ,							shuffle=lowercase    ,							shuffle_buffer_size=args.shuffle_buffer_size    ,							)

				_a								= prepare_dataset(
				    lowercase    ,							decode_fn=lowercase    ,							mask_fn=lowercase    ,							batch_size=lowercase    ,							shuffle=lowercase    ,							)

				_a								= []
				if args.hub_model_id:
								callbacks.append(
								    PushToHubCallback(output_dir=args.output_dir    ,							hub_model_id=args.hub_model_id    ,							tokenizer=lowercase						)						)

				model.fit(
				    lowercase    ,							validation_data=lowercase    ,							epochs=args.num_epochs    ,							callbacks=lowercase    ,							)

				model.save_pretrained(args.output_dir						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			parse_args()
					main(args)
