
"""simple docstring"""






import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    RemBertConfig,
					    RemBertForCausalLM,
					    RemBertForMaskedLM,
					    RemBertForMultipleChoice,
					    RemBertForQuestionAnswering,
					    RemBertForSequenceClassification,
					    RemBertForTokenClassification,
					    RemBertModel,
					)
					from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=13     ,						_lowerCamelCase=7     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=99     ,						_lowerCamelCase=32     ,						_lowerCamelCase=18     ,						_lowerCamelCase=43     ,						_lowerCamelCase=5     ,						_lowerCamelCase=4     ,						_lowerCamelCase=37     ,						_lowerCamelCase="gelu"     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=512     ,						_lowerCamelCase=16     ,						_lowerCamelCase=2     ,						_lowerCamelCase=0.02     ,						_lowerCamelCase=3     ,						_lowerCamelCase=4     ,						_lowerCamelCase=None     ,						) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					parent
										__lowercase						      =					batch_size
										__lowercase						      =					seq_length
										__lowercase						      =					is_training
										__lowercase						      =					use_input_mask
										__lowercase						      =					use_token_type_ids
										__lowercase						      =					use_labels
										__lowercase						      =					vocab_size
										__lowercase						      =					hidden_size
										__lowercase						      =					input_embedding_size
										__lowercase						      =					output_embedding_size
										__lowercase						      =					num_hidden_layers
										__lowercase						      =					num_attention_heads
										__lowercase						      =					intermediate_size
										__lowercase						      =					hidden_act
										__lowercase						      =					hidden_dropout_prob
										__lowercase						      =					attention_probs_dropout_prob
										__lowercase						      =					max_position_embeddings
										__lowercase						      =					type_vocab_size
										__lowercase						      =					type_sequence_label_size
										__lowercase						      =					initializer_range
										__lowercase						      =					num_labels
										__lowercase						      =					num_choices
										__lowercase						      =					scope
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size					)

										__lowercase						      =					None
										if self.use_input_mask:
													__lowercase						      =					random_attention_mask([self.batch_size, self.seq_length]					)

										__lowercase						      =					None
										if self.use_token_type_ids:
													__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						self.type_vocab_size					)

										__lowercase						      =					None
										__lowercase						      =					None
										__lowercase						      =					None
										if self.use_labels:
													__lowercase						      =					ids_tensor([self.batch_size]     ,						self.type_sequence_label_size					)
													__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						self.num_labels					)
													__lowercase						      =					ids_tensor([self.batch_size]     ,						self.num_choices					)

										__lowercase						      =					RemBertConfig(
										    vocab_size=self.vocab_size     ,						hidden_size=self.hidden_size     ,						input_embedding_size=self.input_embedding_size     ,						output_embedding_size=self.output_embedding_size     ,						num_hidden_layers=self.num_hidden_layers     ,						num_attention_heads=self.num_attention_heads     ,						intermediate_size=self.intermediate_size     ,						hidden_act=self.hidden_act     ,						hidden_dropout_prob=self.hidden_dropout_prob     ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,						max_position_embeddings=self.max_position_embeddings     ,						type_vocab_size=self.type_vocab_size     ,						is_decoder=_lowerCamelCase     ,						initializer_range=self.initializer_range     ,						)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[Any]:




										'''simple docstring'''
										(
										    (
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				
										)						      =					self.prepare_config_and_inputs()

										__lowercase						      =					True
										__lowercase						      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
										__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						vocab_size=2					)

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					RemBertModel(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase					)
										__lowercase						      =					model(_lowerCamelCase     ,						token_type_ids=_lowerCamelCase					)
										__lowercase						      =					model(_lowerCamelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       Union[str, Any]:




										'''simple docstring'''
										__lowercase						      =					True
										__lowercase						      =					RemBertModel(_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						encoder_hidden_states=_lowerCamelCase     ,						encoder_attention_mask=_lowerCamelCase     ,						)
										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						encoder_hidden_states=_lowerCamelCase     ,						)
										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       int:




										'''simple docstring'''
										__lowercase						      =					RemBertForCausalLM(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						labels=_lowerCamelCase					)
										self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					RemBertForMaskedLM(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						labels=_lowerCamelCase					)
										self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.vocab_size)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					True
										__lowercase						      =					True
										__lowercase						      =					RemBertForCausalLM(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()

										# first forward pass
										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						encoder_hidden_states=_lowerCamelCase     ,						encoder_attention_mask=_lowerCamelCase     ,						use_cache=_lowerCamelCase     ,						)
										__lowercase						      =					outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										__lowercase						      =					ids_tensor((self.batch_size, 3)     ,						config.vocab_size					)
										__lowercase						      =					ids_tensor((self.batch_size, 3)     ,						vocab_size=2					)

										# append to next input_ids and
										__lowercase						      =					torch.cat([input_ids, next_tokens]     ,						dim=-1					)
										__lowercase						      =					torch.cat([input_mask, next_mask]     ,						dim=-1					)

										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						encoder_hidden_states=_lowerCamelCase     ,						encoder_attention_mask=_lowerCamelCase     ,						output_hidden_states=_lowerCamelCase     ,						)["hidden_states"][0]
										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						encoder_hidden_states=_lowerCamelCase     ,						encoder_attention_mask=_lowerCamelCase     ,						past_key_values=_lowerCamelCase     ,						output_hidden_states=_lowerCamelCase     ,						)["hidden_states"][0]

										# select random slice
										__lowercase						      =					ids_tensor((1,)     ,						output_from_past.shape[-1]					).item()
										__lowercase						      =					output_from_no_past[:, -3:, random_slice_idx].detach()
										__lowercase						      =					output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(_lowerCamelCase     ,						_lowerCamelCase     ,						atol=1e-3					)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					RemBertForQuestionAnswering(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						start_positions=_lowerCamelCase     ,						end_positions=_lowerCamelCase     ,						)
										self.parent.assertEqual(result.start_logits.shape     ,						(self.batch_size, self.seq_length)					)
										self.parent.assertEqual(result.end_logits.shape     ,						(self.batch_size, self.seq_length)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					self.num_labels
										__lowercase						      =					RemBertForSequenceClassification(_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						labels=_lowerCamelCase					)
										self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					self.num_labels
										__lowercase						      =					RemBertForTokenClassification(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(_lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						labels=_lowerCamelCase					)
										self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.seq_length, self.num_labels)					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					self.num_choices
										__lowercase						      =					RemBertForMultipleChoice(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					input_ids.unsqueeze(1					).expand(-1     ,						self.num_choices     ,						-1					).contiguous()
										__lowercase						      =					token_type_ids.unsqueeze(1					).expand(-1     ,						self.num_choices     ,						-1					).contiguous()
										__lowercase						      =					input_mask.unsqueeze(1					).expand(-1     ,						self.num_choices     ,						-1					).contiguous()
										__lowercase						      =					model(
										    _lowerCamelCase     ,						attention_mask=_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						labels=_lowerCamelCase     ,						)
										self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)					)





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




										'''simple docstring'''
										__lowercase						      =					self.prepare_config_and_inputs()
										(
										    (
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				
										)						      =					config_and_inputs
										__lowercase						      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict





@require_torch
class 				__a    (   __a				,      __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: Optional[Any]					      =      (
							    (
							        RemBertModel,
							        RemBertForMaskedLM,
							        RemBertForCausalLM,
							        RemBertForMultipleChoice,
							        RemBertForQuestionAnswering,
							        RemBertForSequenceClassification,
							        RemBertForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_lowerCamelCase				: Any					      =      (RemBertForCausalLM,) if is_torch_available() else ()
							_lowerCamelCase				: Optional[int]					      =      (
							    {
							        """feature-extraction""": RemBertModel,
							        """fill-mask""": RemBertForMaskedLM,
							        """question-answering""": RemBertForQuestionAnswering,
							        """text-classification""": RemBertForSequenceClassification,
							        """text-generation""": RemBertForCausalLM,
							        """token-classification""": RemBertForTokenClassification,
							        """zero-shot""": RemBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					RemBertModelTester(self					)
										__lowercase						      =					ConfigTester(self     ,						config_class=_lowerCamelCase     ,						hidden_size=37					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										self.config_tester.run_common_tests()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
													__lowercase						      =					type
													self.model_tester.create_and_check_model(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										# This regression test was failing with PyTorch < 1.3
										(
										    (
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				(
										    __lowercase
										)      ,				
										)						      =					self.model_tester.prepare_config_and_inputs_for_decoder()

										__lowercase						      =					None

										self.model_tester.create_and_check_model_as_decoder(
										    _lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						)





							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowercase						      =					RemBertModel.from_pretrained(_lowerCamelCase					)
													self.assertIsNotNone(_lowerCamelCase					)





@require_torch
class 				__a    (   unittest.TestCase					):



							'''simple docstring'''







							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										# Test exact values at the last hidden layer
										__lowercase						      =					RemBertModel.from_pretrained("google/rembert"					)
										__lowercase						      =					torch.tensor([[312, 56_498, 313, 2_125, 313]]					)
										__lowercase						      =					torch.tensor([[0, 0, 0, 1, 1]]					)
										with torch.no_grad():
													__lowercase						      =					model(_lowerCamelCase     ,						token_type_ids=_lowerCamelCase     ,						output_hidden_states=_lowerCamelCase					)

										__lowercase						      =					1_152

										__lowercase						      =					torch.Size((1, 5, hidden_size)					)
										self.assertEqual(output["last_hidden_state"].shape     ,						_lowerCamelCase					)

										__lowercase						      =					torch.tensor(
										    [
										        [
										            [0.0754, -0.2022, 0.1904],
										            [-0.3354, -0.3692, -0.4791],
										            [-0.2314, -0.6729, -0.0749],
										            [-0.0396, -0.3105, -0.4234],
										            [-0.1571, -0.0525, 0.5353],
										        ]
										    ]					)

										# Running on the original tf implementation gives slightly different results here.
										# Not clear why this variations is present
										# TODO: Find reason for discrepancy
										# expected_original_implementation = [[
										#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
										#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
										#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
										#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
										#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
										# ]]

										self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]     ,						_lowerCamelCase     ,						atol=1e-4					)					)







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





