





'''simple docstring'''






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer







@require_torch
class       lowerCamelCase       (    unittest.TestCase	):


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'							)
											model.to(__lowercase							)

											# encoder-decoder outputs
											_snake_case: str	       =		torch.tensor([[17, 9_62_08, 10_34_71, 2]]							).to(__lowercase							)
											_snake_case: Any	       =		torch.tensor(
											    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]							).to(__lowercase							)
											_snake_case: Tuple	       =		model(
											    input_ids=__lowercase							,  attention_mask=__lowercase							,  encoder_outputs=__lowercase							,  decoder_input_ids=__lowercase							)
											_snake_case: Optional[Any]	       =		output[0]
											_snake_case: Tuple	       =		torch.Size((1, 14, 25_00_12)							)
											self.assertEqual(output_predited_logis.shape							,  __lowercase							)
											_snake_case: str	       =		torch.tensor(
											    [[[-6.3_986, -8.2_391, 12.51_89], [-6.3_289, -8.0_864, 12.62_11], [-6.2_418, -8.0_445, 12.79_68]]]							).to(__lowercase							)
											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]							,  __lowercase							,  atol=1e-4							)							)

											# encoder outputs
											_snake_case: Union[str, Any]	       =		model.prophetnet.encoder(__lowercase							)[0]
											_snake_case: List[str]	       =		torch.tensor(
											    [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]							).to(__lowercase							)
											_snake_case: List[str]	       =		torch.Size((1, 4, 10_24)							)
											self.assertEqual(encoder_outputs.shape							,  __lowercase							)
											self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]							,  __lowercase							,  atol=1e-4							)							)

											# decoder outputs
											_snake_case: Optional[Any]	       =		model.prophetnet.decoder(
											    __lowercase							,  encoder_hidden_states=__lowercase							,  )
											_snake_case: Optional[Any]	       =		decoder_outputs[1].view(1							,  model.config.ngram							,  14							,  -1							)
											_snake_case: Dict	       =		model.lm_head(__lowercase							)
											_snake_case: List[str]	       =		predicting_streams_logits[:, 0]
											self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]							,  __lowercase							,  atol=1e-4							)							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)
											model.to(__lowercase							)

											_snake_case: Any	       =		torch.tensor([[17, 9_62_08, 10_34_71, 2]]							).to(__lowercase							)
											_snake_case: int	       =		torch.tensor(
											    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]							).to(__lowercase							)
											_snake_case: Any	       =		model(
											    input_ids=__lowercase							,  attention_mask=__lowercase							,  encoder_outputs=__lowercase							,  decoder_input_ids=__lowercase							)
											_snake_case: Optional[Any]	       =		output[0]
											_snake_case: List[Any]	       =		torch.Size((1, 14, 25_00_12)							)
											self.assertEqual(output_predited_logis.shape							,  __lowercase							)
											# compare the actual values for a slice.
											_snake_case: str	       =		torch.tensor(
											    [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]							).to(__lowercase							)

											self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]							,  __lowercase							,  atol=1e-4							)							)




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: Tuple	       =		XLMProphetNetForConditionalGeneration.from_pretrained(
											    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)
											model.to(__lowercase							)
											_snake_case: List[Any]	       =		5_12

											_snake_case: List[Any]	       =		XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'							)

											_snake_case: Any	       =		(
											    '''Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'''
											    ''' January 14, 2020, according to the official portal of the organization. From that day, users of this'''
											    ''' system will not be able to receive security updates, which could make their computers vulnerable to'''
											    ''' cyber attacks.'''
											)
											_snake_case: str	       =		(
											    '''орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'''
											    ''' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'''
											    ''' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'''
											    ''' к кибератакам.'''
											)
											_snake_case: Optional[int]	       =		(
											    '''根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'''
											)

											_snake_case: List[Any]	       =		tokenizer(
											    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]							,  padding=__lowercase							,  max_length=2_55							,  return_tensors='pt'							).input_ids
											_snake_case: Union[str, Any]	       =		input_ids.to(__lowercase							)

											_snake_case: Optional[int]	       =		model.generate(
											    __lowercase							,  num_beams=10							,  length_penalty=1.0							,  no_repeat_ngram_size=3							,  early_stopping=__lowercase							)
											_snake_case: List[Any]	       =		[tokenizer.decode(__lowercase							,  skip_special_tokens=__lowercase							) for g in summary_ids]
											_snake_case: Tuple	       =		'''Microsoft to end Windows 7 free support after January 14, 2020'''
											_snake_case: List[Any]	       =		'''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
											_snake_case: List[Any]	       =		'''微软打算终止对Windows 7操作系统的免费支持'''
											self.assertListEqual(
											    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]							,  __lowercase							,  )

											_snake_case: str	       =		model.generate(
											    __lowercase							,  num_beams=1							,  length_penalty=1.0							,  no_repeat_ngram_size=3							,  early_stopping=__lowercase							)
											_snake_case: Union[str, Any]	       =		[
											    tokenizer.convert_ids_to_tokens(__lowercase							,  skip_special_tokens=__lowercase							) for g in summary_ids_beama
											]
											_snake_case: Any	       =		'''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(' '							)
											_snake_case: str	       =		'''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
											    ' '							)
											_snake_case: List[str]	       =		'''微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'''.split(' '							)
											self.assertListEqual(
											    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]							,  __lowercase							,  )












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
snake_case_				=      '''src/diffusers'''
snake_case_				=      '''docs/source/en'''
snake_case_				=      '''.'''


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		with open(SCREAMING_SNAKE_CASE_				,							'''r'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
				lowerCamelCase :			List[str]    		=f.readlines()
		# Find the start prompt.
		lowerCamelCase :			List[Any]    		=0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_		):
				start_index += 1
		start_index += 1

		lowerCamelCase :			Dict    		=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 |
snake_case_				=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case_				=      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case_				=      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.
snake_case_				=      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		lowerCamelCase :			Optional[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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Dict    		=2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Union[str, Any]    		=(width - text_length) // 2
		lowerCamelCase :			List[str]    		=width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def        A__						(							) ->  Union[str, Any]:
		lowerCamelCase :			List[str]    		=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowerCamelCase :			List[Any]    		={
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		lowerCamelCase :			Optional[int]    		={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.
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=None
				if attr_name.endswith('''Tokenizer'''		):
						lowerCamelCase :			List[str]    		=slow_tokenizers
						lowerCamelCase :			List[Any]    		=attr_name[:-9]
				elif attr_name.endswith('''TokenizerFast'''		):
						lowerCamelCase :			Any    		=fast_tokenizers
						lowerCamelCase :			Optional[Any]    		=attr_name[:-1_3]
				elif _re_tf_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			Optional[Any]    		=tf_models
						lowerCamelCase :			Any    		=_re_tf_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_flax_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[str]    		=flax_models
						lowerCamelCase :			int    		=_re_flax_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_pt_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[Any]    		=pt_models
						lowerCamelCase :			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():
										lowerCamelCase :			Optional[Any]    		=True
										break
								# Try again after removing the last word in the name
								lowerCamelCase :			List[str]    		=''''''.join(camel_case_split(SCREAMING_SNAKE_CASE_		)[:-1]		)

    # Let's build that table!
		lowerCamelCase :			Optional[int]    		=list(model_name_to_config.keys()		)
		model_names.sort(key=str.lower		)
		lowerCamelCase :			Union[str, Any]    		=['''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).
		lowerCamelCase :			Tuple    		=[len(SCREAMING_SNAKE_CASE_		) + 2 for c in columns]
		lowerCamelCase :			Dict    		=max([len(SCREAMING_SNAKE_CASE_		) for name in model_names]		) + 2

		# Build the table per se
		lowerCamelCase :			List[Any]    		='''|''' + '''|'''.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"

		lowerCamelCase :			Optional[Any]    		={True: '''✅''', False: '''❌'''}
		for name in model_names:
				lowerCamelCase :			str    		=model_name_to_prefix[name]
				lowerCamelCase :			Dict    		=[
				    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        A__						(							SCREAMING_SNAKE_CASE_=False		) ->  int:
		lowerCamelCase		, lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			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-->'''				,							)
		lowerCamelCase :			List[Any]    		=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__":
				snake_case_				=      argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				snake_case_				=      parser.parse_args()

				check_model_table(args.fix_and_overwrite)

