




import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    XLMForMultipleChoice,
			    XLMForQuestionAnswering,
			    XLMForQuestionAnsweringSimple,
			    XLMForSequenceClassification,
			    XLMForTokenClassification,
			    XLMModel,
			    XLMWithLMHeadModel,
			)
			from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Union[str, Any]=13		,					_lowerCamelCase:   Dict=7		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   Optional[Any]=True		,					_lowerCamelCase:   str=True		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   Dict=False		,					_lowerCamelCase:   Tuple=False		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   List[Any]=99		,					_lowerCamelCase:   Optional[int]=0		,					_lowerCamelCase:   Union[str, Any]=32		,					_lowerCamelCase:   Optional[Any]=5		,					_lowerCamelCase:   str=4		,					_lowerCamelCase:   str=0.1		,					_lowerCamelCase:   Optional[int]=0.1		,					_lowerCamelCase:   List[Any]=5_12		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   Union[str, Any]=0.02		,					_lowerCamelCase:   str=2		,					_lowerCamelCase:   str=4		,					_lowerCamelCase:   Any="last"		,					_lowerCamelCase:   Optional[int]=True		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   Union[str, Any]=0		,					):
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    seq_length
											SCREAMING_SNAKE_CASE_   	=    is_training
											SCREAMING_SNAKE_CASE_   	=    use_input_lengths
											SCREAMING_SNAKE_CASE_   	=    use_token_type_ids
											SCREAMING_SNAKE_CASE_   	=    use_labels
											SCREAMING_SNAKE_CASE_   	=    gelu_activation
											SCREAMING_SNAKE_CASE_   	=    sinusoidal_embeddings
											SCREAMING_SNAKE_CASE_   	=    causal
											SCREAMING_SNAKE_CASE_   	=    asm
											SCREAMING_SNAKE_CASE_   	=    n_langs
											SCREAMING_SNAKE_CASE_   	=    vocab_size
											SCREAMING_SNAKE_CASE_   	=    n_special
											SCREAMING_SNAKE_CASE_   	=    hidden_size
											SCREAMING_SNAKE_CASE_   	=    num_hidden_layers
											SCREAMING_SNAKE_CASE_   	=    num_attention_heads
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    max_position_embeddings
											SCREAMING_SNAKE_CASE_   	=    type_sequence_label_size
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    num_labels
											SCREAMING_SNAKE_CASE_   	=    num_choices
											SCREAMING_SNAKE_CASE_   	=    summary_type
											SCREAMING_SNAKE_CASE_   	=    use_proj
											SCREAMING_SNAKE_CASE_   	=    scope
											SCREAMING_SNAKE_CASE_   	=    bos_token_id


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size		)
											SCREAMING_SNAKE_CASE_   	=    random_attention_mask([self.batch_size, self.seq_length]		)

											SCREAMING_SNAKE_CASE_   	=    None
											if self.use_input_lengths:
																		SCREAMING_SNAKE_CASE_   	=    (
																		    ids_tensor([self.batch_size]		,					vocab_size=2		) + self.seq_length - 2
																		)  # small variation of seq_length

											SCREAMING_SNAKE_CASE_   	=    None
											if self.use_token_type_ids:
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size, self.seq_length]		,					self.n_langs		)

											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size]		,					self.type_sequence_label_size		)
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels		)
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size]		,					2		).float()
																		SCREAMING_SNAKE_CASE_   	=    ids_tensor([self.batch_size]		,					self.num_choices		)

											SCREAMING_SNAKE_CASE_   	=    self.get_config()

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)


				def     _A							(							self:   Any		):
											return XLMConfig(
											    vocab_size=self.vocab_size		,					n_special=self.n_special		,					emb_dim=self.hidden_size		,					n_layers=self.num_hidden_layers		,					n_heads=self.num_attention_heads		,					dropout=self.hidden_dropout_prob		,					attention_dropout=self.attention_probs_dropout_prob		,					gelu_activation=self.gelu_activation		,					sinusoidal_embeddings=self.sinusoidal_embeddings		,					asm=self.asm		,					causal=self.causal		,					n_langs=self.n_langs		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					summary_type=self.summary_type		,					use_proj=self.use_proj		,					num_labels=self.num_labels		,					bos_token_id=self.bos_token_id		,					)


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Any		,					):
											SCREAMING_SNAKE_CASE_   	=    XLMModel(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					lengths=_lowerCamelCase		,					langs=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					langs=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)
											self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   int		,					):
											SCREAMING_SNAKE_CASE_   	=    XLMWithLMHeadModel(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					labels=_lowerCamelCase		)
											self.parent.assertEqual(result.loss.shape		,					()		)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   int		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   List[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    XLMForQuestionAnsweringSimple(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					start_positions=_lowerCamelCase		,					end_positions=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    outputs
											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     _A							(							self:   Dict		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   List[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    XLMForQuestionAnswering(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					start_positions=_lowerCamelCase		,					end_positions=_lowerCamelCase		,					cls_index=_lowerCamelCase		,					is_impossible=_lowerCamelCase		,					p_mask=_lowerCamelCase		,					)

											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					start_positions=_lowerCamelCase		,					end_positions=_lowerCamelCase		,					cls_index=_lowerCamelCase		,					is_impossible=_lowerCamelCase		,					)

											((SCREAMING_SNAKE_CASE_)	,	)   	=    result_with_labels.to_tuple()

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					start_positions=_lowerCamelCase		,					end_positions=_lowerCamelCase		)

											((SCREAMING_SNAKE_CASE_)	,	)   	=    result_with_labels.to_tuple()

											self.parent.assertEqual(result_with_labels.loss.shape		,					()		)
											self.parent.assertEqual(result.start_top_log_probs.shape		,					(self.batch_size, model.config.start_n_top)		)
											self.parent.assertEqual(result.start_top_index.shape		,					(self.batch_size, model.config.start_n_top)		)
											self.parent.assertEqual(
											    result.end_top_log_probs.shape		,					(self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
											self.parent.assertEqual(
											    result.end_top_index.shape		,					(self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
											self.parent.assertEqual(result.cls_logits.shape		,					(self.batch_size,)		)


				def     _A							(							self:   Any		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   Tuple		,					):
											SCREAMING_SNAKE_CASE_   	=    XLMForSequenceClassification(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					labels=_lowerCamelCase		)
											self.parent.assertEqual(result.loss.shape		,					()		)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size)		)


				def     _A							(							self:   str		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Union[str, Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    self.num_labels
											SCREAMING_SNAKE_CASE_   	=    XLMForTokenClassification(_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()

											SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		,					attention_mask=_lowerCamelCase		,					labels=_lowerCamelCase		)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   List[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    self.num_choices
											SCREAMING_SNAKE_CASE_   	=    XLMForMultipleChoice(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_   	=    input_ids.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
											SCREAMING_SNAKE_CASE_   	=    token_type_ids.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
											SCREAMING_SNAKE_CASE_   	=    input_mask.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
											SCREAMING_SNAKE_CASE_   	=    model(
											    _lowerCamelCase		,					attention_mask=_lowerCamelCase		,					token_type_ids=_lowerCamelCase		,					labels=_lowerCamelCase		,					)
											self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)		)





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.prepare_config_and_inputs()
											(
											    (
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	(
											    SCREAMING_SNAKE_CASE_
											)	,	
											)   	=    config_and_inputs
											SCREAMING_SNAKE_CASE_   	=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
											return config, inputs_dict






@require_torch
class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					(
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					(
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				SCREAMING_SNAKE_CASE__					:							str	      =					(
				    {
				        "feature-extraction": XLMModel,
				        "fill-mask": XLMWithLMHeadModel,
				        "question-answering": XLMForQuestionAnsweringSimple,
				        "text-classification": XLMForSequenceClassification,
				        "text-generation": XLMWithLMHeadModel,
				        "token-classification": XLMForTokenClassification,
				        "zero-shot": XLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   str		):
											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith('''Fast'''		)
											):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

											return False


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   int=False		):
											SCREAMING_SNAKE_CASE_   	=    super()._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		,					return_labels=_lowerCamelCase		)

											if return_labels:
																		if model_class.__name__ == "XLMForQuestionAnswering":
																									SCREAMING_SNAKE_CASE_   	=    torch.zeros(
																									    self.model_tester.batch_size		,					dtype=torch.long		,					device=_lowerCamelCase		)
																									SCREAMING_SNAKE_CASE_   	=    torch.zeros(
																									    self.model_tester.batch_size		,					dtype=torch.long		,					device=_lowerCamelCase		)

											return inputs_dict


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    XLMModelTester(self		)
											SCREAMING_SNAKE_CASE_   	=    ConfigTester(self		,					config_class=_lowerCamelCase		,					emb_dim=37		)


				def     _A							(							self:   Tuple		):
											self.config_tester.run_common_tests()


				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*_lowerCamelCase		)


				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*_lowerCamelCase		)


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*_lowerCamelCase		)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowerCamelCase		)


				def     _A							(							self:   Any		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[Any]=1		):
											self.assertIsInstance(_lowerCamelCase		,					_lowerCamelCase		)
											self.assertListEqual(
											    [isinstance(_lowerCamelCase		,					_lowerCamelCase		) for iter_attentions in attentions]		,					[True] * len(_lowerCamelCase		)		)
											self.assertEqual(len(_lowerCamelCase		)		,					(max_length - min_length) * num_beam_groups		)

											for idx, iter_attentions in enumerate(_lowerCamelCase		):
																		# adds PAD dummy token
																		SCREAMING_SNAKE_CASE_   	=    min_length + idx + 1
																		SCREAMING_SNAKE_CASE_   	=    min_length + idx + 1

																		SCREAMING_SNAKE_CASE_   	=    (
																		    batch_size * num_beam_groups,
																		    config.num_attention_heads,
																		    tgt_len,
																		    src_len,
																		)
																		# check attn size
																		self.assertListEqual(
																		    [layer_attention.shape for layer_attention in iter_attentions]		,					[expected_shape] * len(_lowerCamelCase		)		)


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Union[str, Any]=False		,					_lowerCamelCase:   Optional[int]=1		):
											self.assertIsInstance(_lowerCamelCase		,					_lowerCamelCase		)
											self.assertListEqual(
											    [isinstance(_lowerCamelCase		,					_lowerCamelCase		) for iter_hidden_states in hidden_states]		,					[True] * len(_lowerCamelCase		)		,					)
											self.assertEqual(len(_lowerCamelCase		)		,					(max_length - min_length) * num_beam_groups		)

											for idx, iter_hidden_states in enumerate(_lowerCamelCase		):
																		# adds PAD dummy token
																		SCREAMING_SNAKE_CASE_   	=    min_length + idx + 1
																		SCREAMING_SNAKE_CASE_   	=    (batch_size * num_beam_groups, seq_len, config.hidden_size)
																		# check hidden size
																		self.assertListEqual(
																		    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]		,					[expected_shape] * len(_lowerCamelCase		)		,					)
											pass





				@slow
				def     _A							(							self:   List[str]		):
											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE_   	=    XLMModel.from_pretrained(_lowerCamelCase		)
																		self.assertIsNotNone(_lowerCamelCase		)






@require_torch
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				@slow
				def     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''		)
											model.to(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch.tensor([[14, 4_47]]		,					dtype=torch.long		,					device=_lowerCamelCase		)  # the president
											SCREAMING_SNAKE_CASE_   	=    [
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											    14,
											    4_47,
											]  # the president the president the president the president the president the president the president the president the president the president
											# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
											SCREAMING_SNAKE_CASE_   	=    model.generate(_lowerCamelCase		,					do_sample=_lowerCamelCase		)
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()		,					_lowerCamelCase		)








import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    fname.split(os.path.sep     )[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''  ,    _lowerCAmelCase     ).groups()[0]






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    file_names
											SCREAMING_SNAKE_CASE_   	=    image_transform
											SCREAMING_SNAKE_CASE_   	=    label_to_id


				def __len__(							self:   Any		):
											return len(self.file_names		)





				def __getitem__(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.file_names[idx]
											SCREAMING_SNAKE_CASE_   	=    PIL.Image.open(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    raw_image.convert('''RGB'''		)
											if self.image_transform is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.image_transform(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    extract_label(_lowerCamelCase		)
											if self.label_to_id is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.label_to_id[label]
											return {"image": image, "label": label}






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							# Initialize accelerator
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(
														    cpu=args.cpu  ,    mixed_precision=args.mixed_precision  ,    log_with='''all'''  ,    project_dir=args.project_dir     )
							else:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(cpu=args.cpu  ,    mixed_precision=args.mixed_precision     )

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_   	=    config['''lr''']
							SCREAMING_SNAKE_CASE_   	=    int(config['''num_epochs''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''seed''']     )
							SCREAMING_SNAKE_CASE_   	=    int(config['''batch_size''']     )
							SCREAMING_SNAKE_CASE_   	=    config['''image_size''']
							if not isinstance(_lowerCAmelCase  ,    (list, tuple)     ):
														SCREAMING_SNAKE_CASE_   	=    (image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps  ,    '''isdigit'''     ):
														if args.checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					SCREAMING_SNAKE_CASE_   	=    int(args.checkpointing_steps     )
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    os.path.split(_lowerCAmelCase     )[-1].split('''.'''     )[0]
														accelerator.init_trackers(_lowerCAmelCase  ,    _lowerCAmelCase     )

							# Grab all the image filenames
							SCREAMING_SNAKE_CASE_   	=    [os.path.join(args.data_dir  ,    _lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith('''.jpg'''     )]

							# Build the label correspondences
							SCREAMING_SNAKE_CASE_   	=    [extract_label(_lowerCAmelCase     ) for fname in file_names]
							SCREAMING_SNAKE_CASE_   	=    list(set(_lowerCAmelCase     )     )
							id_to_label.sort()
							SCREAMING_SNAKE_CASE_   	=    {lbl: i for i, lbl in enumerate(_lowerCAmelCase     )}

							# Set the seed before splitting the data.
							np.random.seed(_lowerCAmelCase     )
							torch.manual_seed(_lowerCAmelCase     )
							torch.cuda.manual_seed_all(_lowerCAmelCase     )

							# Split our filenames between train and validation
							SCREAMING_SNAKE_CASE_   	=    np.random.permutation(len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    int(0.8 * len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    random_perm[:cut]
							SCREAMING_SNAKE_CASE_   	=    random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							SCREAMING_SNAKE_CASE_   	=    Compose([RandomResizedCrop(_lowerCAmelCase  ,    scale=(0.5, 1.0)     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset(
							    [file_names[i] for i in train_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# For evaluation, we use a deterministic Resize
							SCREAMING_SNAKE_CASE_   	=    Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset([file_names[i] for i in eval_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_   	=    create_model('''resnet50d'''  ,    pretrained=_lowerCAmelCase  ,    num_classes=len(_lowerCAmelCase     )     )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							SCREAMING_SNAKE_CASE_   	=    model.to(accelerator.device     )

							# Freezing the base model
							for param in model.parameters():
														SCREAMING_SNAKE_CASE_   	=    False
							for param in model.get_classifier().parameters():
														SCREAMING_SNAKE_CASE_   	=    True

							# We normalize the batches of images to be a bit faster.
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''mean''']     )[None, :, None, None].to(accelerator.device     )
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''std''']     )[None, :, None, None].to(accelerator.device     )

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_   	=    torch.optim.Adam(params=model.parameters()  ,    lr=lr / 2_5     )

							# Instantiate learning rate scheduler
							SCREAMING_SNAKE_CASE_   	=    OneCycleLR(optimizer=_lowerCAmelCase  ,    max_lr=_lowerCAmelCase  ,    epochs=_lowerCAmelCase  ,    steps_per_epoch=len(_lowerCAmelCase     )     )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(
							    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE_   	=    0
							# We also need to keep track of the starting epoch so files are named properly
							SCREAMING_SNAKE_CASE_   	=    0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
																					accelerator.load_state(args.resume_from_checkpoint     )
																					SCREAMING_SNAKE_CASE_   	=    os.path.basename(args.resume_from_checkpoint     )
														else:
																					# Get the most recent checkpoint
																					SCREAMING_SNAKE_CASE_   	=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime     )
																					SCREAMING_SNAKE_CASE_   	=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														SCREAMING_SNAKE_CASE_   	=    os.path.splitext(_lowerCAmelCase     )[0]

														if "epoch" in training_difference:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''epoch_'''  ,    ''''''     )     ) + 1
																					SCREAMING_SNAKE_CASE_   	=    None
														else:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''step_'''  ,    ''''''     )     )
																					SCREAMING_SNAKE_CASE_   	=    resume_step // len(_lowerCAmelCase     )
																					resume_step -= starting_epoch * len(_lowerCAmelCase     )

    # Now we train the model
							for epoch in range(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														model.train()
														if args.with_tracking:
																					SCREAMING_SNAKE_CASE_   	=    0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					SCREAMING_SNAKE_CASE_   	=    accelerator.skip_first_batches(_lowerCAmelCase  ,    _lowerCAmelCase     )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					SCREAMING_SNAKE_CASE_   	=    train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    torch.nn.functional.cross_entropy(_lowerCAmelCase  ,    batch['''label''']     )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(_lowerCAmelCase     )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
																												SCREAMING_SNAKE_CASE_   	=    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																																			accelerator.save_state(_lowerCAmelCase     )
														model.eval()
														SCREAMING_SNAKE_CASE_   	=    0
														SCREAMING_SNAKE_CASE_   	=    0
														for step, batch in enumerate(_lowerCAmelCase     ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    outputs.argmax(dim=-1     )
																					SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather_for_metrics((predictions, batch['''label'''])     )
																					SCREAMING_SNAKE_CASE_   	=    predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														SCREAMING_SNAKE_CASE_   	=    accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}: {1_0_0 * eval_metric:.2f}"     )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        '''accuracy''': 1_0_0 * eval_metric,
																					        '''train_loss''': total_loss.item() / len(_lowerCAmelCase     ),
																					        '''epoch''': epoch,
																					    }  ,    step=_lowerCAmelCase  ,    )
														if checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																					accelerator.save_state(_lowerCAmelCase     )

							if args.with_tracking:
														accelerator.end_training()

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    argparse.ArgumentParser(description='''Simple example of training script.'''     )
							parser.add_argument('''--data_dir'''  ,    required=_lowerCAmelCase  ,    help='''The data folder on disk.'''     )
							parser.add_argument('''--fp16'''  ,    action='''store_true'''  ,    help='''If passed, will use FP16 training.'''     )
							parser.add_argument(
							    '''--mixed_precision'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,    help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''  ,    )
							parser.add_argument('''--cpu'''  ,    action='''store_true'''  ,    help='''If passed, will train on the CPU.'''     )
							parser.add_argument(
							    '''--checkpointing_steps'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,    )
							parser.add_argument(
							    '''--output_dir'''  ,    type=_lowerCAmelCase  ,    default='''.'''  ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,    )
							parser.add_argument(
							    '''--resume_from_checkpoint'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''If the training should continue from a checkpoint folder.'''  ,    )
							parser.add_argument(
							    '''--with_tracking'''  ,    action='''store_true'''  ,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,    )
							parser.add_argument(
							    '''--project_dir'''  ,    type=_lowerCAmelCase  ,    default='''logs'''  ,    help='''Location on where to store experiment tracking logs` and relevent project information'''  ,    )
							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()
							SCREAMING_SNAKE_CASE_   	=    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(_lowerCAmelCase  ,    _lowerCAmelCase     )


if __name__ == "__main__":
			main()


