





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCamelCase__   : Tuple					  = """src/transformers"""
UpperCamelCase__   : List[Any]					  = """docs/source/en"""
UpperCamelCase__   : Tuple					  = """."""




def    A_(			A				,      A				,      A   ):
	with open(A				,      """r"""				,      encoding="""utf-8"""				,      newline="""\n"""   ) as f:
		UpperCAmelCase_								=     f.readlines()
	# Find the start prompt.
	UpperCAmelCase_								=     0
	while not lines[start_index].startswith(A   ):
		start_index += 1
	start_index += 1

	UpperCAmelCase_								=     start_index
	while not lines[end_index].startswith(A   ):
		end_index += 1
	end_index -= 1

	while len(lines[start_index]   ) <= 1:
		start_index += 1
	while len(lines[end_index]   ) <= 1:
		end_index -= 1
	end_index += 1
	return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCamelCase__   : Optional[Any]					  = """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__   : List[Any]					  = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
UpperCamelCase__   : str					  = re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCamelCase__   : int					  = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase__   : Tuple					  = direct_transformers_import(TRANSFORMERS_PATH)




def    A_(			A   ):
	UpperCAmelCase_								=     re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""				,      A   )
	return [m.group(0   ) for m in matches]




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     2 if text == """✅""" or text == """❌""" else len(A   )
	UpperCAmelCase_								=     (width - text_length) // 2
	UpperCAmelCase_								=     width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent




def    A_(			):
	UpperCAmelCase_								=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	UpperCAmelCase_								=     {
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	UpperCAmelCase_								=     {name: config.replace("""Config"""				,      """"""   ) for name, config in model_name_to_config.items()}

	# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )
	UpperCAmelCase_								=     collections.defaultdict(A   )

	# Let's lookup through all transformers object (once).
	for attr_name in dir(A   ):
		UpperCAmelCase_								=     None
		if attr_name.endswith("""Tokenizer"""   ):
			UpperCAmelCase_								=     slow_tokenizers
			UpperCAmelCase_								=     attr_name[:-9]
		elif attr_name.endswith("""TokenizerFast"""   ):
			UpperCAmelCase_								=     fast_tokenizers
			UpperCAmelCase_								=     attr_name[:-13]
		elif _re_tf_models.match(A   ) is not None:
			UpperCAmelCase_								=     tf_models
			UpperCAmelCase_								=     _re_tf_models.match(A   ).groups()[0]
		elif _re_flax_models.match(A   ) is not None:
			UpperCAmelCase_								=     flax_models
			UpperCAmelCase_								=     _re_flax_models.match(A   ).groups()[0]
		elif _re_pt_models.match(A   ) is not None:
			UpperCAmelCase_								=     pt_models
			UpperCAmelCase_								=     _re_pt_models.match(A   ).groups()[0]

		if lookup_dict is not None:
			while len(A   ) > 0:
				if attr_name in model_name_to_prefix.values():
					UpperCAmelCase_								=     True
					break
				# Try again after removing the last word in the name
				UpperCAmelCase_								=     """""".join(camel_case_split(A   )[:-1]   )

    # Let's build that table!
	UpperCAmelCase_								=     list(model_name_to_config.keys()   )
	model_names.sort(key=str.lower   )
	UpperCAmelCase_								=     ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
	# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
	UpperCAmelCase_								=     [len(A   ) + 2 for c in columns]
	UpperCAmelCase_								=     max([len(A   ) for name in model_names]   ) + 2

	# Build the table per se
	UpperCAmelCase_								=     """|""" + """|""".join([_center_text(A				,      A   ) for c, w in zip(A				,      A   )]   ) + """|\n"""
	# Use ":-----:" format to center-aligned table cell texts
	table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]   ) + "|\n"

	UpperCAmelCase_								=     {True: """✅""", False: """❌"""}
	for name in model_names:
		UpperCAmelCase_								=     model_name_to_prefix[name]
		UpperCAmelCase_								=     [
		    name,
		    check[slow_tokenizers[prefix]],
		    check[fast_tokenizers[prefix]],
		    check[pt_models[prefix]],
		    check[tf_models[prefix]],
		    check[flax_models[prefix]],
		]
		table += "|" + "|".join([_center_text(A				,      A   ) for l, w in zip(A				,      A   )]   ) + "|\n"
	return table




def    A_(			A=False   ):
	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     _find_text_in_file(
	    filename=os.path.join(A				,      """index.md"""   )				,      start_prompt="""<!--This table is updated automatically from the auto modules"""				,      end_prompt="""<!-- End table-->"""				,      )
	UpperCAmelCase_								=     get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(A				,      """index.md"""   )				,      """w"""				,      encoding="""utf-8"""				,      newline="""\n"""   ) as f:
				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
		else:
			raise ValueError(
			    """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""   )


if __name__ == "__main__":
				UpperCamelCase__   : Optional[Any]					  = argparse.ArgumentParser()
				parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
				UpperCamelCase__   : List[Any]					  = parser.parse_args()

				check_model_table(args.fix_and_overwrite)









import json
import os
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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     tempfile.mkdtemp()

						UpperCAmelCase_								=     [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """的""",
						    """价""",
						    """格""",
						    """是""",
						    """15""",
						    """便""",
						    """alex""",
						    """##andra""",
						    """，""",
						    """。""",
						    """-""",
						    """t""",
						    """shirt""",
						]
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]		)
						with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""		) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

						UpperCAmelCase_								=     {
						    """do_resize""": True,
						    """size""": {"""height""": 2_24, """width""": 2_24},
						    """do_center_crop""": True,
						    """crop_size""": {"""height""": 18, """width""": 18},
						    """do_normalize""": True,
						    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
						    """image_std""": [0.26862954, 0.26130258, 0.27577711],
						    """do_convert_rgb""": True,
						}
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					__lowercase		)
						with open(self.image_processor_file ,					"""w""" ,					encoding="""utf-8"""		) as fp:
							json.dump(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizer.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizerFast.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					**__lowercase    :  int		):






						'''simple docstring'''







						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						shutil.rmtree(self.tmpdirname		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     [np.random.randint(2_55 ,					size=(3, 30, 4_00) ,					dtype=np.uinta		)]

						UpperCAmelCase_								=     [Image.fromarray(np.moveaxis(__lowercase ,					0 ,					-1		)		) for x in image_inputs]

						return image_inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_tokenizer()
						UpperCAmelCase_								=     self.get_rust_tokenizer()
						UpperCAmelCase_								=     self.get_image_processor()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_slow.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(self.tmpdirname ,					use_fast=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_fast.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.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 ,					__lowercase		)
						self.assertIsInstance(processor_fast.tokenizer ,					__lowercase		)

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





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						UpperCAmelCase_								=     self.get_tokenizer(cls_token="""(CLS)""" ,					sep_token="""(SEP)"""		)
						UpperCAmelCase_								=     self.get_image_processor(do_normalize=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname ,					cls_token="""(CLS)""" ,					sep_token="""(SEP)""" ,					do_normalize=__lowercase		)

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

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





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     image_processor(__lowercase ,					return_tensors="""np"""		)
						UpperCAmelCase_								=     processor(images=__lowercase ,					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        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""

						UpperCAmelCase_								=     processor(text=__lowercase		)

						UpperCAmelCase_								=     tokenizer(__lowercase		)

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





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

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

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





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCAmelCase_								=     processor.batch_decode(__lowercase		)
						UpperCAmelCase_								=     tokenizer.batch_decode(__lowercase		)

						self.assertListEqual(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

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


