



import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  CLIPTokenizer
			UpperCamelCase           =  CLIPTokenizerFast
			UpperCamelCase           =  True
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						int)     ->					List[str]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['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
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))
						_UpperCAmelCase	 =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
						_UpperCAmelCase	 =     {'unk_token': '<unk>'}

						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase	 =     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(A) + '\n')
						with open(self.merges_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write('\n'.join(A))



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						Any)     ->					Dict:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						str				,	**A     :						List[Any])     ->					List[str]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						int)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     'lower newer'
						_UpperCAmelCase	 =     'lower newer'
						return input_text, output_text



			def 					_lowerCamelCase (		self     :						int)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     CLIPTokenizer(self.vocab_file				,	self.merges_file				,	**self.special_tokens_map)
						_UpperCAmelCase	 =     'lower newer'
						_UpperCAmelCase	 =     ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
						_UpperCAmelCase	 =     tokenizer.tokenize(A)
						self.assertListEqual(A				,	A)

						_UpperCAmelCase	 =     tokens + [tokenizer.unk_token]
						_UpperCAmelCase	 =     [10, 2, 16, 9, 3, 2, 16, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)				,	A)



			@require_ftfy
			def 					_lowerCamelCase (		self     :						List[Any])     ->					str:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase	 =     self.tokenizer_class.from_pretrained(A				,	**A)
												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(A				,	**A)

												_UpperCAmelCase	 =     'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
												_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
												_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

												self.assertListEqual(A				,	A)

												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
												# with Tilde) encoded in 2 different ways
												_UpperCAmelCase	 =     'xa\u0303y' + ' ' + 'x\xe3y'
												_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
												_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

												self.assertListEqual(A				,	A)

												# Test that the tokenization is identical on unicode of space type
												_UpperCAmelCase	 =     [
												    '\u0009',  # (horizontal tab, '\t')
												    '\u000B',  # (vertical tab)
												    '\u000C',  # (form feed)
												    '\u0020',  # (space, ' ')
												    '\u200E',  # (left-to-right mark):w
												    '\u200F',  # (right-to-left mark)
												]
												for unicode_seq in spaces_unicodes:
															_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
															_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

															self.assertListEqual(A				,	A)

												# Test that the tokenization is identical on unicode of line break type
												_UpperCAmelCase	 =     [
												    '\u000A',  # (line feed, '\n')
												    '\r\n',  # (carriage return and line feed, '\r\n')
												    '\u000D',  # (carriage return, '\r')
												    '\r',  # (carriage return, '\r')
												    '\u000D',  # (carriage return, '\r')
												    '\u2028',  # (line separator)
												    '\u2029',  # (paragraph separator)
												    # "\u0085", # (next line)
												]

												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
												# space (and thus into an empty list).

												for unicode_seq in line_break_unicodes:
															_UpperCAmelCase	 =     tokenizer_s.tokenize(A)
															_UpperCAmelCase	 =     tokenizer_r.tokenize(A)

															self.assertListEqual(A				,	A)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Union[str, Any]:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase	 =     'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
												_UpperCAmelCase	 =     F"{text_of_1_token} {text_of_1_token}"

												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(
												    A				,	use_fast=A				,	)
												_UpperCAmelCase	 =     tokenizer_r(A				,	return_offsets_mapping=A				,	add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]				,	(0, len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]				,	(len(A) + 1, len(A) + 1 + len(A))				,	)

												_UpperCAmelCase	 =     F" {text}"

												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(
												    A				,	use_fast=A				,	)
												_UpperCAmelCase	 =     tokenizer_r(A				,	return_offsets_mapping=A				,	add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]				,	(1, 1 + len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]				,	(1 + len(A) + 1, 1 + len(A) + 1 + len(A))				,	)



			def 					_lowerCamelCase (		self     :						Any)     ->					Optional[int]:



						"""simple docstring"""






						with self.assertRaises(A) as context:
									self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer')

						self.assertTrue(
						    context.exception.args[0].startswith(
						        'The `backend_tokenizer` provided does not match the expected format.'))



			@require_ftfy
			def 					_lowerCamelCase (		self     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						super().test_tokenization_python_rust_equals()



			def 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



						"""simple docstring"""






						pass





import argparse

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


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

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

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


if __name__ == "__main__":
							UpperCAmelCase__     = 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."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
