

"""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 			__UpperCamelCase (   unittest.TestCase						):


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased")
							model.to(__SCREAMING_SNAKE_CASE)

							# encoder-decoder outputs
							A			=					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]).to(__SCREAMING_SNAKE_CASE)
							A			=					torch.tensor(
							    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]).to(__SCREAMING_SNAKE_CASE)
							A			=					model(
							    input_ids=__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE     ,     encoder_outputs=__SCREAMING_SNAKE_CASE     ,     decoder_input_ids=__SCREAMING_SNAKE_CASE)
							A			=					output[0]
							A			=					torch.Size((1, 1_4, 2_5_0_0_1_2))
							self.assertEqual(output_predited_logis.shape     ,     __SCREAMING_SNAKE_CASE)
							A			=					torch.tensor(
							    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]).to(__SCREAMING_SNAKE_CASE)
							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))

							# encoder outputs
							A			=					model.prophetnet.encoder(__SCREAMING_SNAKE_CASE)[0]
							A			=					torch.tensor(
							    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]).to(__SCREAMING_SNAKE_CASE)
							A			=					torch.Size((1, 4, 1_0_2_4))
							self.assertEqual(encoder_outputs.shape     ,     __SCREAMING_SNAKE_CASE)
							self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))

							# decoder outputs
							A			=					model.prophetnet.decoder(
							    __SCREAMING_SNAKE_CASE     ,     encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,     )
							A			=					decoder_outputs[1].view(1     ,     model.config.ngram     ,     1_4     ,     -1)
							A			=					model.lm_head(__SCREAMING_SNAKE_CASE)
							A			=					predicting_streams_logits[:, 0]
							self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]     ,     __SCREAMING_SNAKE_CASE     ,     atol=1E-4))


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					XLMProphetNetForConditionalGeneration.from_pretrained(
							    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
							model.to(__SCREAMING_SNAKE_CASE)

							A			=					torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]).to(__SCREAMING_SNAKE_CASE)
							A			=					torch.tensor(
							    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]).to(__SCREAMING_SNAKE_CASE)
							A			=					model(
							    input_ids=__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE     ,     encoder_outputs=__SCREAMING_SNAKE_CASE     ,     decoder_input_ids=__SCREAMING_SNAKE_CASE)
							A			=					output[0]
							A			=					torch.Size((1, 1_4, 2_5_0_0_1_2))
							self.assertEqual(output_predited_logis.shape     ,     __SCREAMING_SNAKE_CASE)
							# compare the actual values for a slice.
							A			=					torch.tensor(
							    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]).to(__SCREAMING_SNAKE_CASE)

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


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					XLMProphetNetForConditionalGeneration.from_pretrained(
							    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
							model.to(__SCREAMING_SNAKE_CASE)
							A			=					5_1_2

							A			=					XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")

							A			=					(
							    "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."
							)
							A			=					(
							    "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."
							)
							A			=					(
							    "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。"
							)

							A			=					tokenizer(
							    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]     ,     padding=__SCREAMING_SNAKE_CASE     ,     max_length=2_5_5     ,     return_tensors="pt").input_ids
							A			=					input_ids.to(__SCREAMING_SNAKE_CASE)

							A			=					model.generate(
							    __SCREAMING_SNAKE_CASE     ,     num_beams=1_0     ,     length_penalty=1.0     ,     no_repeat_ngram_size=3     ,     early_stopping=__SCREAMING_SNAKE_CASE)
							A			=					[tokenizer.decode(__SCREAMING_SNAKE_CASE     ,     skip_special_tokens=__SCREAMING_SNAKE_CASE) for g in summary_ids]
							A			=					"Microsoft to end Windows 7 free support after January 14, 2020"
							A			=					"Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
							A			=					"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]     ,     __SCREAMING_SNAKE_CASE     ,     )

							A			=					model.generate(
							    __SCREAMING_SNAKE_CASE     ,     num_beams=1     ,     length_penalty=1.0     ,     no_repeat_ngram_size=3     ,     early_stopping=__SCREAMING_SNAKE_CASE)
							A			=					[
							    tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE     ,     skip_special_tokens=__SCREAMING_SNAKE_CASE) for g in summary_ids_beama
							]
							A			=					"▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" ")
							A			=					"▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
							    " ")
							A			=					"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]     ,     __SCREAMING_SNAKE_CASE     ,     )








"""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
__A     :    List[str]    =		'src/diffusers'
__A     :    Optional[int]    =		'docs/source/en'
__A     :    Union[str, Any]    =		'.'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							"r"			,							encoding="utf-8"			,							newline="\n"      ) as f:
										A			=					f.readlines()
					# Find the start prompt.
					A			=					0
					while not lines[start_index].startswith(lowercase__      ):
										start_index += 1
					start_index += 1

					A			=					start_index
					while not lines[end_index].startswith(lowercase__      ):
										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 |
__A     :    Any    =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__A     :    Union[str, Any]    =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A     :    int    =		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.
__A     :    Any    =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
__A     :    Optional[int]    =		importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__A     :    Tuple    =		spec.loader.load_module()
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"			,							lowercase__      )
					return [m.group(0      ) for m in matches]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					2 if text == "✅" or text == "❌" else len(lowercase__      )
					A			=					(width - text_length) // 2
					A			=					width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent
def    __SCREAMING_SNAKE_CASE						(						):
					"""simple docstring"""
					A			=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					A			=					{
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					A			=					{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.
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )
					A			=					collections.defaultdict(lowercase__      )

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

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

    # Let's build that table!
					A			=					list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					A			=					["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).
					A			=					[len(lowercase__      ) + 2 for c in columns]
					A			=					max([len(lowercase__      ) for name in model_names]      ) + 2

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

					A			=					{True: "✅", False: "❌"}
					for name in model_names:
										A			=					model_name_to_prefix[name]
										A			=					[
										    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(lowercase__			,							lowercase__      ) for l, w in zip(lowercase__			,							lowercase__      )]      ) + "|\n"
					return table
def    __SCREAMING_SNAKE_CASE						(						lowercase__=False      ):
					"""simple docstring"""
					A			,    A			,    A			,    A			=					_find_text_in_file(
					    filename=os.path.join(lowercase__			,							"index.mdx"      )			,							start_prompt="<!--This table is updated automatically from the auto modules"			,							end_prompt="<!-- End table-->"			,							)
					A			=					get_model_table_from_auto_modules()

					if current_table != new_table:
										if overwrite:
															with open(os.path.join(lowercase__			,							"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__":
	__A     :    Any    =		argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	__A     :    str    =		parser.parse_args()

	check_model_table(args.fix_and_overwrite)





