
'''simple docstring'''
import argparse
import collections
import importlib.util
import os
import re


# 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
lowercase__		    =	"src/diffusers"
lowercase__		    =	"docs/source/en"
lowercase__		    =	"."




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Optional[Any]:


		'''simple docstring'''


		with open(SCREAMING_SNAKE_CASE__      ,    '''r'''      ,    encoding='''utf-8'''      ,    newline='''\n'''       ) as f:
				snake_case   :    Tuple			       =		f.readlines()
		# Find the start prompt.
		snake_case   :    List[str]			       =		0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE__       ):
				start_index += 1
		start_index += 1

		snake_case   :    Optional[int]			       =		start_index
		while not lines[end_index].startswith(SCREAMING_SNAKE_CASE__       ):
				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 |
lowercase__		    =	"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
lowercase__		    =	re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowercase__		    =	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.
lowercase__		    =	re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the diffusers module imported is the one in the repo.
lowercase__		    =	importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
lowercase__		    =	spec.loader.load_module()




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Any:


		'''simple docstring'''


		snake_case   :    Union[str, Any]			       =		re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''      ,    SCREAMING_SNAKE_CASE__       )
		return [m.group(0       ) for m in matches]




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Tuple:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE__       )
		snake_case   :    List[str]			       =		(width - text_length) // 2
		snake_case   :    int			       =		width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent




def 					_UpperCamelCase		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Tuple			       =		diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		snake_case   :    str			       =		{
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		snake_case   :    List[str]			       =		{name: config.replace('''ConfigMixin'''      ,    ''''''       ) 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.
		snake_case   :    Tuple			       =		collections.defaultdict(SCREAMING_SNAKE_CASE__       )
		snake_case   :    int			       =		collections.defaultdict(SCREAMING_SNAKE_CASE__       )
		snake_case   :    List[str]			       =		collections.defaultdict(SCREAMING_SNAKE_CASE__       )
		snake_case   :    str			       =		collections.defaultdict(SCREAMING_SNAKE_CASE__       )
		snake_case   :    List[Any]			       =		collections.defaultdict(SCREAMING_SNAKE_CASE__       )

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(SCREAMING_SNAKE_CASE__       ):
				snake_case   :    Optional[int]			       =		None
				if attr_name.endswith('''Tokenizer'''       ):
						snake_case   :    Union[str, Any]			       =		slow_tokenizers
						snake_case   :    List[str]			       =		attr_name[:-9]
				elif attr_name.endswith('''TokenizerFast'''       ):
						snake_case   :    Optional[int]			       =		fast_tokenizers
						snake_case   :    Tuple			       =		attr_name[:-13]
				elif _re_tf_models.match(SCREAMING_SNAKE_CASE__       ) is not None:
						snake_case   :    List[Any]			       =		tf_models
						snake_case   :    int			       =		_re_tf_models.match(SCREAMING_SNAKE_CASE__       ).groups()[0]
				elif _re_flax_models.match(SCREAMING_SNAKE_CASE__       ) is not None:
						snake_case   :    Optional[int]			       =		flax_models
						snake_case   :    List[Any]			       =		_re_flax_models.match(SCREAMING_SNAKE_CASE__       ).groups()[0]
				elif _re_pt_models.match(SCREAMING_SNAKE_CASE__       ) is not None:
						snake_case   :    List[str]			       =		pt_models
						snake_case   :    List[Any]			       =		_re_pt_models.match(SCREAMING_SNAKE_CASE__       ).groups()[0]

				if lookup_dict is not None:
						while len(SCREAMING_SNAKE_CASE__       ) > 0:
								if attr_name in model_name_to_prefix.values():
										snake_case   :    Tuple			       =		True
										break
								# Try again after removing the last word in the name
								snake_case   :    Union[str, Any]			       =		''''''.join(camel_case_split(SCREAMING_SNAKE_CASE__       )[:-1]       )

    # Let's build that table!
		snake_case   :    List[str]			       =		list(model_name_to_config.keys()       )
		model_names.sort(key=str.lower       )
		snake_case   :    int			       =		['''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).
		snake_case   :    List[Any]			       =		[len(SCREAMING_SNAKE_CASE__       ) + 2 for c in columns]
		snake_case   :    List[str]			       =		max([len(SCREAMING_SNAKE_CASE__       ) for name in model_names]       ) + 2

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

		snake_case   :    Tuple			       =		{True: '''✅''', False: '''❌'''}
		for name in model_names:
				snake_case   :    Tuple			       =		model_name_to_prefix[name]
				snake_case   :    Optional[int]			       =		[
				    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(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       ) for l, w in zip(SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )]       ) + "|\n"
		return table




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__=False       )   ->				Union[str, Any]:


		'''simple docstring'''


		snake_case       ,snake_case       ,snake_case       ,snake_case   :    Tuple			       =		_find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE__      ,    '''index.mdx'''       )      ,    start_prompt='''<!--This table is updated automatically from the auto modules'''      ,    end_prompt='''<!-- End table-->'''      ,    )
		snake_case   :    str			       =		get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(SCREAMING_SNAKE_CASE__      ,    '''index.mdx'''       )      ,    '''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.mdx` has not been updated. Run `make fix-copies` to fix this.'''       )


if __name__ == "__main__":
						lowercase__		    =	argparse.ArgumentParser()
						parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
						lowercase__		    =	parser.parse_args()

						check_model_table(args.fix_and_overwrite)




'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor






class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       Optional[Any]=7   ,     UpperCamelCase__							:       Union[str, Any]=3   ,     UpperCamelCase__							:       Tuple=18   ,     UpperCamelCase__							:       Union[str, Any]=30   ,     UpperCamelCase__							:       Dict=400   ,     UpperCamelCase__							:       Tuple=True   ,     UpperCamelCase__							:       Optional[Any]=None   ,     UpperCamelCase__							:       Any=True   ,     UpperCamelCase__							:       Dict=False   ,     UpperCamelCase__							:       Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     UpperCamelCase__							:       int=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     UpperCamelCase__							:       str=True   ,     )		->       Any:






				"""simple docstring"""







				snake_case   :    Dict			       =		size if size is not None else {'''height''': 20, '''width''': 20}
				snake_case   :    Union[str, Any]			       =		parent
				snake_case   :    Optional[int]			       =		batch_size
				snake_case   :    Dict			       =		num_channels
				snake_case   :    List[Any]			       =		image_size
				snake_case   :    List[Any]			       =		min_resolution
				snake_case   :    Dict			       =		max_resolution
				snake_case   :    Dict			       =		do_resize
				snake_case   :    Optional[int]			       =		size
				snake_case   :    Dict			       =		do_normalize
				snake_case   :    Tuple			       =		image_mean
				snake_case   :    Optional[int]			       =		image_std
				snake_case   :    int			       =		do_pad
				snake_case   :    List[Any]			       =		do_convert_rgb



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_convert_rgb": self.do_convert_rgb,
				    "do_pad": self.do_pad,
				}



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]=False   ,     UpperCamelCase__							:       Tuple=False   ,     UpperCamelCase__							:       Union[str, Any]=False	)		->       List[str]:






				"""simple docstring"""







				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				if equal_resolution:
						snake_case   :    Any			       =		[]
						for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        255   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta	)	)
				else:
						snake_case   :    Union[str, Any]			       =		[]
						for i in range(self.batch_size	):
								snake_case       ,snake_case   :    List[Any]			       =		np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution	)   ,     2	)
								image_inputs.append(np.random.randint(255   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta	)	)

				if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						snake_case   :    Optional[int]			       =		[Image.fromarray(np.moveaxis(UpperCamelCase__   ,     0   ,     -1	)	) for x in image_inputs]

				if torchify:
						snake_case   :    Optional[Any]			       =		[torch.from_numpy(UpperCamelCase__	) for x in image_inputs]

				return image_inputs







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		BlipImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Dict	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		BlipImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       Any	)		->       Union[str, Any]:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Dict:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Union[str, Any]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Optional[int]			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    int			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Any			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				snake_case   :    List[str]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     numpify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     np.ndarray	)

				# Test not batched input
				snake_case   :    Dict			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Dict			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )



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






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				snake_case   :    List[Any]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     torchify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     torch.Tensor	)

				# Test not batched input
				snake_case   :    List[str]			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    str			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		BlipImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		BlipImageProcessingTester(self   ,     num_channels=4	)
				snake_case   :    Optional[int]			       =		3



		@property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Tuple:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       List[str]:






				"""simple docstring"""







				pass



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






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Tuple			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    int			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Union[str, Any]			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )


