

'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  tempfile.mkdtemp()

								# fmt: off
								snake_case__						:       Any       						=  ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
								# fmt: on
								snake_case__						:       List[str]       						=  dict(zip(snake_case_			,			range(len(snake_case_  )  )  )  )
								snake_case__						:       int       						=  ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
								snake_case__						:       Optional[int]       						=  {'''unk_token''': '''<unk>'''}

								snake_case__						:       Union[str, Any]       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']  )
								snake_case__						:       str       						=  os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8'''  ) as fp:
										fp.write(json.dumps(snake_case_  ) + '''\n'''  )
								with open(self.merges_file			,			'''w'''			,			encoding='''utf-8'''  ) as fp:
										fp.write('''\n'''.join(snake_case_  )  )

								snake_case__						:       str       						=  {
								    '''do_resize''': True,
								    '''size''': 2_0,
								    '''do_center_crop''': True,
								    '''crop_size''': 1_8,
								    '''do_normalize''': True,
								    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
								    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
								}
								snake_case__						:       List[Any]       						=  os.path.join(self.tmpdirname			,			snake_case_  )
								with open(self.image_processor_file			,			'''w'''			,			encoding='''utf-8'''  ) as fp:
										json.dump(snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Dict			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								return CLIPTokenizer.from_pretrained(self.tmpdirname			,			**snake_case_  )







						def       __magic_name__							(							self					:      List[Any]			,			**snake_case_					:      str  ):



								'''simple docstring'''



								return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,			**snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			**snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								return ViTImageProcessor.from_pretrained(self.tmpdirname			,			**snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								shutil.rmtree(self.tmpdirname  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  [np.random.randint(2_5_5			,			size=(3, 3_0, 4_0_0)			,			dtype=np.uinta  )]

								snake_case__						:       Tuple       						=  [Image.fromarray(np.moveaxis(snake_case_			,			0			,			-1  )  ) for x in image_inputs]

								return image_inputs







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.get_tokenizer()
								snake_case__						:       str       						=  self.get_rust_tokenizer()
								snake_case__						:       Tuple       						=  self.get_image_processor()

								snake_case__						:       str       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )
								processor_slow.save_pretrained(self.tmpdirname  )
								snake_case__						:       List[str]       						=  CLIPSegProcessor.from_pretrained(self.tmpdirname			,			use_fast=snake_case_  )

								snake_case__						:       List[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )
								processor_fast.save_pretrained(self.tmpdirname  )
								snake_case__						:       Tuple       						=  CLIPSegProcessor.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			,			snake_case_  )
								self.assertIsInstance(processor_fast.tokenizer			,			snake_case_  )

								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			,			snake_case_  )
								self.assertIsInstance(processor_fast.image_processor			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  CLIPSegProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()  )
								processor.save_pretrained(self.tmpdirname  )

								snake_case__						:       Tuple       						=  self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''  )
								snake_case__						:       Union[str, Any]       						=  self.get_image_processor(do_normalize=snake_case_			,			padding_value=1.0  )

								snake_case__						:       int       						=  CLIPSegProcessor.from_pretrained(
								    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=snake_case_			,			padding_value=1.0  )

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

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







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.get_image_processor()
								snake_case__						:       str       						=  self.get_tokenizer()

								snake_case__						:       Tuple       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

								snake_case__						:       List[Any]       						=  self.prepare_image_inputs()

								snake_case__						:       Any       						=  image_processor(snake_case_			,			return_tensors='''np'''  )
								snake_case__						:       Optional[int]       						=  processor(images=snake_case_			,			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       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.get_image_processor()
								snake_case__						:       Tuple       						=  self.get_tokenizer()

								snake_case__						:       Optional[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

								snake_case__						:       Union[str, Any]       						=  '''lower newer'''

								snake_case__						:       str       						=  processor(text=snake_case_  )

								snake_case__						:       List[str]       						=  tokenizer(snake_case_  )

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







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.get_image_processor()
								snake_case__						:       List[Any]       						=  self.get_tokenizer()

								snake_case__						:       List[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

								snake_case__						:       List[Any]       						=  '''lower newer'''
								snake_case__						:       int       						=  self.prepare_image_inputs()

								snake_case__						:       Tuple       						=  processor(text=snake_case_			,			images=snake_case_  )

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

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







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.get_image_processor()
								snake_case__						:       Any       						=  self.get_tokenizer()

								snake_case__						:       List[Any]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

								snake_case__						:       Optional[Any]       						=  self.prepare_image_inputs()
								snake_case__						:       str       						=  self.prepare_image_inputs()

								snake_case__						:       List[str]       						=  processor(images=snake_case_			,			visual_prompt=snake_case_  )

								self.assertListEqual(list(inputs.keys()  )			,			['''pixel_values''', '''conditional_pixel_values''']  )

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







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.get_image_processor()
								snake_case__						:       List[Any]       						=  self.get_tokenizer()

								snake_case__						:       Optional[int]       						=  CLIPSegProcessor(tokenizer=snake_case_			,			image_processor=snake_case_  )

								snake_case__						:       str       						=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								snake_case__						:       str       						=  processor.batch_decode(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  tokenizer.batch_decode(snake_case_  )

								self.assertListEqual(snake_case_			,			snake_case_  )






'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

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


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						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(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



