




"""simple docstring"""







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
						from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
						__A   :  int					=      get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
						from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__A   :  Union[str, Any]					=      1_2_8_0_2_2
__A   :  List[Any]					=      1_2_8_0_2_8




@require_sentencepiece
class       lowerCAmelCase__       (			__A  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Dict       =      MaMaaaTokenizer
				__UpperCAmelCase :     List[Any]       =      False
				__UpperCAmelCase :     Tuple       =      False
				__UpperCAmelCase :     Union[str, Any]       =      True



				def       snake_case    (     self					:	int						):
						super().setUp()

						__lowercase				:		Dict												=				['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
						__lowercase				:		Union[str, Any]												=				dict(zip(UpperCamelCase__    ,   range(len(UpperCamelCase__						)						)						)						)
						__lowercase				:		List[Any]												=				Path(self.tmpdirname						)
						save_json(UpperCamelCase__    ,   save_dir / VOCAB_FILES_NAMES["vocab_file"]						)
						if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
								copyfile(UpperCamelCase__    ,   save_dir / VOCAB_FILES_NAMES["spm_file"]						)

						__lowercase				:		Any												=				MaMaaaTokenizer.from_pretrained(self.tmpdirname						)
						tokenizer.save_pretrained(self.tmpdirname						)



				def       snake_case    (     self					:	Optional[int]    ,   **lowercase__					:	Union[str, Any]						):
						return MaMaaaTokenizer.from_pretrained(self.tmpdirname    ,   **UpperCamelCase__						)



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	int						):
						return (
						    "This is a test",
						    "This is a test",
						)



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		List[str]												=				'</s>'
						__lowercase				:		Dict												=				0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__						)    ,   UpperCamelCase__						)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__						)    ,   UpperCamelCase__						)



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		List[Any]												=				self.get_tokenizer()
						__lowercase				:		Union[str, Any]												=				list(tokenizer.get_vocab().keys()						)

						self.assertEqual(vocab_keys[0]    ,   "</s>"						)
						self.assertEqual(vocab_keys[1]    ,   "<unk>"						)
						self.assertEqual(vocab_keys[-1]    ,   "<s>"						)
						self.assertEqual(len(UpperCamelCase__						)    ,   tokenizer.vocab_size + len(tokenizer.get_added_vocab()						)						)



				@unittest.skip("Skip this test while all models are still to be uploaded."						)
				def       snake_case    (     self					:	Union[str, Any]						):
						pass



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		int												=				self.get_tokenizer()

						__lowercase				:		Optional[int]												=				tokenizer.tokenize("This is a test"						)
						self.assertListEqual(UpperCamelCase__    ,   ["▁This", "▁is", "▁a", "▁t", "est"]						)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCamelCase__						)    ,   [2, 3, 4, 5, 6]    ,   )

						__lowercase				:		Optional[Any]												=				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]						)
						self.assertListEqual(UpperCamelCase__    ,   ["▁This", "▁is", "▁a", "▁t", "est"]						)

						__lowercase				:		Any												=				tokenizer.convert_tokens_to_string(UpperCamelCase__						)
						self.assertEqual(UpperCamelCase__    ,   "This is a test"						)



				@slow
				def       snake_case    (     self					:	str						):
						# fmt: off
						__lowercase				:		str												=				{'input_ids': [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCamelCase__    ,   model_name="facebook/m2m100_418M"    ,   revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"    ,   )





@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[Any]       =      """facebook/m2m100_418M"""
				__UpperCAmelCase :     Optional[int]       =      [
				    """In my opinion, there are two levels of response from the French government.""",
				    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
				]
				__UpperCAmelCase :     Optional[Any]       =      [
				    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
				    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
				]

				# fmt: off
				__UpperCAmelCase :     Optional[Any]       =      [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]



				@classmethod
				def       snake_case    (     cls					:	Any						):
						__lowercase				:		MaMaaaTokenizer												=				MaMaaaTokenizer.from_pretrained(
						    cls.checkpoint_name    ,   src_lang="en"    ,   tgt_lang="fr"						)
						__lowercase				:		Any												=				1
						return cls



				def       snake_case    (     self					:	Any						):
						self.assertEqual(self.tokenizer.get_lang_id("ar"						)    ,   1_2_8_0_0_6						)
						self.assertEqual(self.tokenizer.get_lang_id("en"						)    ,   1_2_8_0_2_2						)
						self.assertEqual(self.tokenizer.get_lang_id("ro"						)    ,   1_2_8_0_7_6						)
						self.assertEqual(self.tokenizer.get_lang_id("mr"						)    ,   1_2_8_0_6_3						)



				def       snake_case    (     self					:	int						):
						__lowercase				:		Any												=				self.tokenizer.get_vocab()
						self.assertEqual(len(UpperCamelCase__						)    ,   self.tokenizer.vocab_size						)
						self.assertEqual(vocab["<unk>"]    ,   3						)
						self.assertIn(self.tokenizer.get_lang_token("en"						)    ,   UpperCamelCase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		str												=				'en'
						__lowercase				:		str												=				self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
						self.assertListEqual(self.expected_src_tokens    ,   UpperCamelCase__						)



				def       snake_case    (     self					:	Optional[int]						):
						self.assertIn(UpperCamelCase__    ,   self.tokenizer.all_special_ids						)
						# fmt: off
						__lowercase				:		Any												=				[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
						# fmt: on
						__lowercase				:		str												=				self.tokenizer.decode(UpperCamelCase__    ,   skip_special_tokens=UpperCamelCase__						)
						__lowercase				:		Dict												=				self.tokenizer.decode(generated_ids[1:]    ,   skip_special_tokens=UpperCamelCase__						)
						self.assertEqual(UpperCamelCase__    ,   UpperCamelCase__						)
						self.assertNotIn(self.tokenizer.eos_token    ,   UpperCamelCase__						)



				def       snake_case    (     self					:	str						):
						__lowercase				:		Any												=				tempfile.mkdtemp()
						__lowercase				:		List[str]												=				self.tokenizer.lang_token_to_id
						self.tokenizer.save_pretrained(UpperCamelCase__						)
						__lowercase				:		Optional[Any]												=				MaMaaaTokenizer.from_pretrained(UpperCamelCase__						)
						self.assertDictEqual(new_tok.lang_token_to_id    ,   UpperCamelCase__						)



				@require_torch
				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		int												=				'en'
						__lowercase				:		Optional[Any]												=				'fr'

						__lowercase				:		List[Any]												=				self.tokenizer(self.src_text    ,   text_target=self.tgt_text    ,   padding=UpperCamelCase__    ,   return_tensors="pt"						)

						__lowercase				:		int												=				shift_tokens_right(
						    batch["labels"]    ,   self.tokenizer.pad_token_id    ,   self.tokenizer.eos_token_id						)

						for k in batch:
								__lowercase				:		Tuple												=				batch[k].tolist()
						# batch = {k: v.tolist() for k,v in batch.items()}
						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						# batch.decoder_inputs_ids[0][0] ==
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == FR_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]



				@require_torch
				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		str												=				'mr'
						self.assertListEqual(self.tokenizer.prefix_tokens    ,   [self.tokenizer.get_lang_id("mr"						)]						)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,   [self.tokenizer.eos_token_id]						)

						__lowercase				:		str												=				'zh'
						self.assertListEqual(self.tokenizer.prefix_tokens    ,   [self.tokenizer.get_lang_id("zh"						)]						)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,   [self.tokenizer.eos_token_id]						)



				@require_torch
				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Optional[Any]												=				'mr'
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,   [self.tokenizer.get_lang_id("mr"						)]						)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,   [self.tokenizer.eos_token_id]						)
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,   [self.tokenizer.get_lang_id(self.tokenizer.src_lang						)]						)

						__lowercase				:		str												=				'zh'
						self.tokenizer._switch_to_target_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,   [self.tokenizer.get_lang_id("zh"						)]						)
						self.assertListEqual(self.tokenizer.suffix_tokens    ,   [self.tokenizer.eos_token_id]						)
						self.tokenizer._switch_to_input_mode()
						self.assertListEqual(self.tokenizer.prefix_tokens    ,   [self.tokenizer.get_lang_id(self.tokenizer.src_lang						)]						)



				@require_torch
				def       snake_case    (     self					:	Any						):
						__lowercase				:		Optional[Any]												=				self.tokenizer._build_translation_inputs("A test"    ,   return_tensors="pt"    ,   src_lang="en"    ,   tgt_lang="ar"						)

						self.assertEqual(
						    nested_simplify(UpperCamelCase__						)    ,   {
						        # en_XX, A, test, EOS
						        "input_ids": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
						        "attention_mask": [[1, 1, 1, 1]],
						        # ar_AR
						        "forced_bos_token_id": 1_2_8_0_0_6,
						    }    ,   )








"""simple docstring"""







import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


__A   :  List[Any]					=      get_logger(__name__)


__A   :  Tuple					=      Path(__file__).parent / 'model_card_template.md'
__A   :  Any					=      uuida().hex
__A   :  Optional[int]					=      os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__A   :  Tuple					=      os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__A   :  List[Any]					=      HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'

def 						snake_case__       (					_lowerCamelCase = None				)			->str:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				F'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
		if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
				return ua + "; telemetry/off"
		if is_torch_available():
				ua += F'; torch/{_torch_version}'
		if is_flax_available():
				ua += F'; jax/{_jax_version}'
				ua += F'; flax/{_flax_version}'
		if is_onnx_available():
				ua += F'; onnxruntime/{_onnxruntime_version}'
		# CI will set this value to True
		if os.environ.get("DIFFUSERS_IS_CI",  ""				).upper() in ENV_VARS_TRUE_VALUES:
				ua += "; is_ci/true"
		if isinstance(_lowerCamelCase,  _lowerCamelCase				):
				ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				ua += "; " + user_agent
		return ua

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None,  _lowerCamelCase = None				)			->List[Any]:






		"""simple docstring"""


		if token is None:
				__lowercase				:		Any												=				HfFolder.get_token()
		if organization is None:
				__lowercase				:		int												=				whoami(_lowerCamelCase				)["name"]
				return F'{username}/{model_id}'
		else:
				return F'{organization}/{model_id}'

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		if not is_jinja_available():
				raise ValueError(
				    "Modelcard rendering is based on Jinja templates."
				    " Please make sure to have `jinja` installed before using `create_model_card`."
				    " To install it, please run `pip install Jinja2`."				)

		if hasattr(_lowerCamelCase,  "local_rank"				) and args.local_rank not in [-1, 0]:
				return

		__lowercase				:		Tuple												=				args.hub_token if hasattr(_lowerCamelCase,  "hub_token"				) else None
		__lowercase				:		int												=				get_full_repo_name(_lowerCamelCase,  token=_lowerCamelCase				)

		__lowercase				:		Optional[Any]												=				ModelCard.from_template(
		    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
		        language="en",  license="apache-2.0",  library_name="diffusers",  tags=[],  datasets=args.dataset_name,  metrics=[],  ),  template_path=_lowerCamelCase,  model_name=_lowerCamelCase,  repo_name=_lowerCamelCase,  dataset_name=args.dataset_name if hasattr(_lowerCamelCase,  "dataset_name"				) else None,  learning_rate=args.learning_rate,  train_batch_size=args.train_batch_size,  eval_batch_size=args.eval_batch_size,  gradient_accumulation_steps=(
		        args.gradient_accumulation_steps if hasattr(_lowerCamelCase,  "gradient_accumulation_steps"				) else None
		    ),  adam_betaa=args.adam_betaa if hasattr(_lowerCamelCase,  "adam_beta1"				) else None,  adam_betaa=args.adam_betaa if hasattr(_lowerCamelCase,  "adam_beta2"				) else None,  adam_weight_decay=args.adam_weight_decay if hasattr(_lowerCamelCase,  "adam_weight_decay"				) else None,  adam_epsilon=args.adam_epsilon if hasattr(_lowerCamelCase,  "adam_epsilon"				) else None,  lr_scheduler=args.lr_scheduler if hasattr(_lowerCamelCase,  "lr_scheduler"				) else None,  lr_warmup_steps=args.lr_warmup_steps if hasattr(_lowerCamelCase,  "lr_warmup_steps"				) else None,  ema_inv_gamma=args.ema_inv_gamma if hasattr(_lowerCamelCase,  "ema_inv_gamma"				) else None,  ema_power=args.ema_power if hasattr(_lowerCamelCase,  "ema_power"				) else None,  ema_max_decay=args.ema_max_decay if hasattr(_lowerCamelCase,  "ema_max_decay"				) else None,  mixed_precision=args.mixed_precision,  )

		__lowercase				:		Dict												=				os.path.join(args.output_dir,  "README.md"				)
		model_card.save(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->Tuple:






		"""simple docstring"""


		if resolved_file is None or commit_hash is not None:
				return commit_hash
		__lowercase				:		Union[str, Any]												=				str(Path(_lowerCamelCase				).as_posix()				)
		__lowercase				:		int												=				re.search(R"snapshots/([^/]+)/",  _lowerCamelCase				)
		if search is None:
				return None
		__lowercase				:		Union[str, Any]												=				search.groups()[0]
		return commit_hash if REGEX_COMMIT_HASH.match(_lowerCamelCase				) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__A   :  Union[str, Any]					=      os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__A   :  List[str]					=      os.path.join(hf_cache_home, 'diffusers')

def 						snake_case__       (					_lowerCamelCase = None,  _lowerCamelCase = None				)			->None:






		"""simple docstring"""


		if new_cache_dir is None:
				__lowercase				:		int												=				DIFFUSERS_CACHE
		if old_cache_dir is None:
				__lowercase				:		Tuple												=				old_diffusers_cache

		__lowercase				:		str												=				Path(_lowerCamelCase				).expanduser()
		__lowercase				:		Optional[int]												=				Path(_lowerCamelCase				).expanduser()
		for old_blob_path in old_cache_dir.glob("**/blobs/*"				):
				if old_blob_path.is_file() and not old_blob_path.is_symlink():
						__lowercase				:		str												=				new_cache_dir / old_blob_path.relative_to(_lowerCamelCase				)
						new_blob_path.parent.mkdir(parents=_lowerCamelCase,  exist_ok=_lowerCamelCase				)
						os.replace(_lowerCamelCase,  _lowerCamelCase				)
						try:
								os.symlink(_lowerCamelCase,  _lowerCamelCase				)
						except OSError:
								logger.warning(
								    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."				)
		# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


__A   :  int					=      os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
						__A   :  Optional[Any]					=      0
else:
						with open(cache_version_file) as f:
												try:
																		__A   :  Optional[int]					=      int(f.read())
												except ValueError:
																		__A   :  Optional[Any]					=      0

if cache_version < 1:
						__A   :  Optional[Any]					=      os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
						if old_cache_is_not_empty:
												logger.warning(
												    'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
												    'existing cached models. This is a one-time operation, you can interrupt it or run it '
												    'later by calling `diffusers.utils.hub_utils.move_cache()`.'
												)
												try:
																		move_cache()
												except Exception as e:
																		__A   :  Union[str, Any]					=      '\n'.join(traceback.format_tb(e.__traceback__))
																		logger.error(
																		    F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
																		    'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
																		    'message and we will do our best to help.'
																		)

if cache_version < 1:
						try:
												os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
												with open(cache_version_file, 'w') as f:
																		f.write('1')
						except Exception:
												logger.warning(
												    F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
												    'the directory exists and can be written to.'
												)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->str:






		"""simple docstring"""


		if variant is not None:
				__lowercase				:		str												=				weights_name.split("."				)
				__lowercase				:		str												=				splits[:-1] + [variant] + splits[-1:]
				__lowercase				:		Dict												=				".".join(_lowerCamelCase				)

		return weights_name





def 						snake_case__       (					_lowerCamelCase,  *,
    _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=None,  )			->str:






		"""simple docstring"""


		__lowercase				:		Dict												=				str(_lowerCamelCase				)
		if os.path.isfile(_lowerCamelCase				):
				return pretrained_model_name_or_path
		elif os.path.isdir(_lowerCamelCase				):
				if os.path.isfile(os.path.join(_lowerCamelCase,  _lowerCamelCase				)				):
						# Load from a PyTorch checkpoint
						__lowercase				:		str												=				os.path.join(_lowerCamelCase,  _lowerCamelCase				)
						return model_file
				elif subfolder is not None and os.path.isfile(
				    os.path.join(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)				):
						__lowercase				:		Dict												=				os.path.join(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
						return model_file
				else:
						raise EnvironmentError(
						    F'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'				)
		else:
				# 1. First check if deprecated way of loading from branches is used
				if (
				    revision in DEPRECATED_REVISION_ARGS
				    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
				    and version.parse(version.parse(_lowerCamelCase				).base_version				) >= version.parse("0.20.0"				)
				):
						try:
								__lowercase				:		Any												=				hf_hub_download(
								    _lowerCamelCase,  filename=_add_variant(_lowerCamelCase,  _lowerCamelCase				),  cache_dir=_lowerCamelCase,  force_download=_lowerCamelCase,  proxies=_lowerCamelCase,  resume_download=_lowerCamelCase,  local_files_only=_lowerCamelCase,  use_auth_token=_lowerCamelCase,  user_agent=_lowerCamelCase,  subfolder=_lowerCamelCase,  revision=revision or commit_hash,  )
								warnings.warn(
								    F'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.',  _lowerCamelCase,  )
								return model_file
						except:  # noqa: E722
								warnings.warn(
								    F'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(_lowerCamelCase,  _lowerCamelCase				)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(_lowerCamelCase,  _lowerCamelCase				)}\' so that the correct variant file can be added.',  _lowerCamelCase,  )
				try:
						# 2. Load model file as usual
						__lowercase				:		str												=				hf_hub_download(
						    _lowerCamelCase,  filename=_lowerCamelCase,  cache_dir=_lowerCamelCase,  force_download=_lowerCamelCase,  proxies=_lowerCamelCase,  resume_download=_lowerCamelCase,  local_files_only=_lowerCamelCase,  use_auth_token=_lowerCamelCase,  user_agent=_lowerCamelCase,  subfolder=_lowerCamelCase,  revision=revision or commit_hash,  )
						return model_file

				except RepositoryNotFoundError:
						raise EnvironmentError(
						    F'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
						    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
						    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
						    "login`."				)
				except RevisionNotFoundError:
						raise EnvironmentError(
						    F'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
						    "this model name. Check the model page at "
						    F'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'				)
				except EntryNotFoundError:
						raise EnvironmentError(
						    F'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'				)
				except HTTPError as err:
						raise EnvironmentError(
						    F'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'				)
				except ValueError:
						raise EnvironmentError(
						    F'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
						    F' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
						    F' directory containing a file named {weights_name} or'
						    " \nCheckout your internet connection or see how to run the library in"
						    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."				)
				except EnvironmentError:
						raise EnvironmentError(
						    F'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
						    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
						    F'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
						    F'containing a file named {weights_name}'				)
