

'''simple docstring'''





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	# Initialise PyTorch model
	UpperCAmelCase__:  Dict		       =   TaConfig.from_json_file(lowercase_			)
	print(f"Building PyTorch model from configuration: {config}"			)
	UpperCAmelCase__:  Optional[Any]		       =   TaForConditionalGeneration(lowercase_			)

	# Load weights from tf checkpoint
	load_tf_weights_in_ta(lowercase_  ,lowercase_  ,lowercase_			)

	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"			)
	model.save_pretrained(lowercase_			)


if __name__ == "__main__":
					_lowerCAmelCase     :			int    =argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_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 T5 model. \nThis specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_lowerCAmelCase     :			List[str]    =parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import FlavaImageProcessor, FlavaProcessor
					from transformers.models.flava.image_processing_flava import (
					    FLAVA_CODEBOOK_MEAN,
					    FLAVA_CODEBOOK_STD,
					    FLAVA_IMAGE_MEAN,
					    FLAVA_IMAGE_STD,
					)

@require_vision
class __UpperCamelCase    (							unittest.TestCase		):






					'''simple docstring'''



					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   tempfile.mkdtemp()

						# fmt: off
						UpperCAmelCase__:  Optional[Any]		       =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
						# fmt: on
						UpperCAmelCase__:  str		       =   os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]	)

						with open(self.vocab_file							,			"w"							,			encoding="utf-8"	) as fp:
							fp.write("".join([x + "\n" for x in vocab_tokens]	)	)

						UpperCAmelCase__:  Tuple		       =   {
						    "image_mean": FLAVA_IMAGE_MEAN,
						    "image_std": FLAVA_IMAGE_STD,
						    "do_normalize": True,
						    "do_resize": True,
						    "size": 2_2_4,
						    "do_center_crop": True,
						    "crop_size": 2_2_4,
						    "input_size_patches": 1_4,
						    "total_mask_patches": 7_5,
						    "mask_group_max_patches": None,
						    "mask_group_min_patches": 1_6,
						    "mask_group_min_aspect_ratio": 0.3,
						    "mask_group_max_aspect_ratio": None,
						    "codebook_do_resize": True,
						    "codebook_size": 1_1_2,
						    "codebook_do_center_crop": True,
						    "codebook_crop_size": 1_1_2,
						    "codebook_do_map_pixels": True,
						    "codebook_do_normalize": True,
						    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
						    "codebook_image_std": FLAVA_CODEBOOK_STD,
						}

						UpperCAmelCase__:  Tuple		       =   os.path.join(self.tmpdirname							,			lowerCamelCase__	)
						with open(self.image_processor_file							,			"w"							,			encoding="utf-8"	) as fp:
							json.dump(lowerCamelCase__							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
						return BertTokenizer.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
						return BertTokenizerFast.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
						return FlavaImageProcessor.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						shutil.rmtree(self.tmpdirname	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Dict		       =   [np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta	)]

						UpperCAmelCase__:  Any		       =   [Image.fromarray(np.moveaxis(lowerCamelCase__							,			0							,			-1	)	) for x in image_inputs]

						return image_inputs





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.get_tokenizer()
						UpperCAmelCase__:  Any		       =   self.get_rust_tokenizer()
						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()

						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
						processor_slow.save_pretrained(self.tmpdirname	)
						UpperCAmelCase__:  Dict		       =   FlavaProcessor.from_pretrained(self.tmpdirname							,			use_fast=lowerCamelCase__	)

						UpperCAmelCase__:  Union[str, Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
						processor_fast.save_pretrained(self.tmpdirname	)
						UpperCAmelCase__:  int		       =   FlavaProcessor.from_pretrained(self.tmpdirname	)

						self.assertEqual(processor_slow.tokenizer.get_vocab()							,			tokenizer_slow.get_vocab()	)
						self.assertEqual(processor_fast.tokenizer.get_vocab()							,			tokenizer_fast.get_vocab()	)
						self.assertEqual(tokenizer_slow.get_vocab()							,			tokenizer_fast.get_vocab()	)
						self.assertIsInstance(processor_slow.tokenizer							,			lowerCamelCase__	)
						self.assertIsInstance(processor_fast.tokenizer							,			lowerCamelCase__	)

						self.assertEqual(processor_slow.image_processor.to_json_string()							,			image_processor.to_json_string()	)
						self.assertEqual(processor_fast.image_processor.to_json_string()							,			image_processor.to_json_string()	)
						self.assertIsInstance(processor_slow.image_processor							,			lowerCamelCase__	)
						self.assertIsInstance(processor_fast.image_processor							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()	)
						processor.save_pretrained(self.tmpdirname	)

						UpperCAmelCase__:  Any		       =   self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"	)
						UpperCAmelCase__:  str		       =   self.get_image_processor(do_normalize=lowerCamelCase__							,			padding_value=1.0	)

						UpperCAmelCase__:  str		       =   FlavaProcessor.from_pretrained(
						    self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=lowerCamelCase__							,			padding_value=1.0	)

						self.assertEqual(processor.tokenizer.get_vocab()							,			tokenizer_add_kwargs.get_vocab()	)
						self.assertIsInstance(processor.tokenizer							,			lowerCamelCase__	)

						self.assertEqual(processor.image_processor.to_json_string()							,			image_processor_add_kwargs.to_json_string()	)
						self.assertIsInstance(processor.image_processor							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.get_image_processor()
						UpperCAmelCase__:  Optional[Any]		       =   self.get_tokenizer()

						UpperCAmelCase__:  Optional[Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Optional[int]		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  Tuple		       =   image_processor(lowerCamelCase__							,			return_tensors="np"	)
						UpperCAmelCase__:  str		       =   processor(images=lowerCamelCase__							,			return_tensors="np"	)

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1e-2	)

						# With rest of the args
						random.seed(1_2_3_4	)
						UpperCAmelCase__:  Optional[int]		       =   image_processor(
						    lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
						random.seed(1_2_3_4	)
						UpperCAmelCase__:  Tuple		       =   processor(
						    images=lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1e-2	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Dict		       =   self.get_image_processor()
						UpperCAmelCase__:  str		       =   self.get_tokenizer()

						UpperCAmelCase__:  List[str]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Union[str, Any]		       =   "lower newer"

						UpperCAmelCase__:  Optional[Any]		       =   processor(text=lowerCamelCase__	)

						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__	)

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
						UpperCAmelCase__:  int		       =   self.get_tokenizer()

						UpperCAmelCase__:  str		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Any		       =   "lower newer"
						UpperCAmelCase__:  Any		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  Optional[int]		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)

						self.assertListEqual(list(inputs.keys()	)							,			["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

						# add extra args
						UpperCAmelCase__:  str		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_image_mask=lowerCamelCase__	)

						self.assertListEqual(
						    list(inputs.keys()	)							,			[
						        "input_ids",
						        "token_type_ids",
						        "attention_mask",
						        "pixel_values",
						        "codebook_pixel_values",
						        "bool_masked_pos",
						    ]							,			)

						# test if it raises when no input is passed
						with pytest.raises(lowerCamelCase__	):
							processor()





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   self.get_image_processor()
						UpperCAmelCase__:  List[Any]		       =   self.get_tokenizer()

						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  List[Any]		       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCAmelCase__:  str		       =   processor.batch_decode(lowerCamelCase__	)
						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__	)

						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   self.get_image_processor()
						UpperCAmelCase__:  Dict		       =   self.get_tokenizer()

						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Optional[Any]		       =   "lower newer"
						UpperCAmelCase__:  Dict		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  int		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)

						self.assertListEqual(list(inputs.keys()	)							,			processor.model_input_names	)