
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
							import torch

							from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Dict    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Dict=99    ,						snake_case_			:							List[Any]=13    ,						snake_case_			:							str=7    ,						snake_case_			:							Optional[Any]=9    ,						snake_case_			:							Union[str, Any]=True    ,						snake_case_			:							List[Any]=True    ,						snake_case_			:							List[Any]=False    ,						snake_case_			:							Dict=32    ,						snake_case_			:							Optional[Any]=5    ,						snake_case_			:							Tuple=4    ,						snake_case_			:							Optional[int]=37    ,						snake_case_			:							Tuple=8    ,						snake_case_			:							List[Any]=0.1    ,						snake_case_			:							Optional[int]=0.002    ,						snake_case_			:							Optional[int]=1    ,						snake_case_			:							List[Any]=0    ,						snake_case_			:							Union[str, Any]=0    ,						snake_case_			:							List[str]=None    ,						snake_case_			:							Any=None    ,						):
							UpperCamelCase_:				Optional[Any]						=    parent
							UpperCamelCase_:				Optional[int]						=    batch_size
							UpperCamelCase_:				List[str]						=    encoder_seq_length
							UpperCamelCase_:				str						=    decoder_seq_length
							# For common tests
							UpperCamelCase_:				List[Any]						=    self.decoder_seq_length
							UpperCamelCase_:				Dict						=    is_training
							UpperCamelCase_:				List[str]						=    use_attention_mask
							UpperCamelCase_:				Any						=    use_labels
							UpperCamelCase_:				List[Any]						=    vocab_size
							UpperCamelCase_:				Optional[int]						=    hidden_size
							UpperCamelCase_:				Optional[Any]						=    num_hidden_layers
							UpperCamelCase_:				Union[str, Any]						=    num_attention_heads
							UpperCamelCase_:				Any						=    d_ff
							UpperCamelCase_:				Optional[Any]						=    relative_attention_num_buckets
							UpperCamelCase_:				str						=    dropout_rate
							UpperCamelCase_:				List[Any]						=    initializer_factor
							UpperCamelCase_:				Any						=    eos_token_id
							UpperCamelCase_:				Dict						=    pad_token_id
							UpperCamelCase_:				Tuple						=    decoder_start_token_id
							UpperCamelCase_:				Union[str, Any]						=    None
							UpperCamelCase_:				List[str]						=    decoder_layers

			def    lowerCAmelCase__						(							self			:							str   ):
							return TaConfig.from_pretrained("""google/umt5-base"""   )

			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Tuple    ,						snake_case_			:							List[str]    ,						snake_case_			:							Dict=None    ,						snake_case_			:							int=None    ,						snake_case_			:							Optional[Any]=None    ,						snake_case_			:							int=None    ,						snake_case_			:							List[Any]=None    ,						):
							if attention_mask is None:
											UpperCamelCase_:				Any						=    input_ids.ne(config.pad_token_id   )
							if decoder_attention_mask is None:
											UpperCamelCase_:				List[Any]						=    decoder_input_ids.ne(config.pad_token_id   )
							if head_mask is None:
											UpperCamelCase_:				Any						=    torch.ones(config.num_hidden_layers    ,						config.num_attention_heads    ,						device=snake_case_   )
							if decoder_head_mask is None:
											UpperCamelCase_:				str						=    torch.ones(config.num_decoder_layers    ,						config.num_attention_heads    ,						device=snake_case_   )
							if cross_attn_head_mask is None:
											UpperCamelCase_:				List[str]						=    torch.ones(
											    config.num_decoder_layers    ,						config.num_attention_heads    ,						device=snake_case_   )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				List[str]						=    ids_tensor([self.batch_size, self.encoder_seq_length]    ,						self.vocab_size   )
							UpperCamelCase_:				Optional[Any]						=    ids_tensor([self.batch_size, self.decoder_seq_length]    ,						self.vocab_size   )

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for NllbMoe the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							UpperCamelCase_:				Union[str, Any]						=    input_ids.clamp(self.pad_token_id + 1   )
							UpperCamelCase_:				Any						=    decoder_input_ids.clamp(self.pad_token_id + 1   )

							UpperCamelCase_:				int						=    self.get_config()
							UpperCamelCase_:				List[Any]						=    config.num_attention_heads
							UpperCamelCase_:				Any						=    self.prepare_inputs_dict(snake_case_    ,						snake_case_    ,						snake_case_   )
							return config, input_dict

			def    lowerCAmelCase__						(							self			:							Dict   ):
							UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    self.prepare_config_and_inputs()
							return config, inputs_dict

			def    lowerCAmelCase__						(							self			:							str   ):
							return TaConfig(
							    vocab_size=166    ,						d_model=self.hidden_size    ,						d_ff=self.d_ff    ,						d_kv=self.hidden_size // self.num_attention_heads    ,						num_layers=self.num_hidden_layers    ,						num_decoder_layers=self.decoder_layers    ,						num_heads=self.num_attention_heads    ,						relative_attention_num_buckets=self.relative_attention_num_buckets    ,						dropout_rate=self.dropout_rate    ,						initializer_factor=self.initializer_factor    ,						eos_token_id=self.eos_token_id    ,						bos_token_id=self.pad_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.decoder_start_token_id    ,						)

			def    lowerCAmelCase__						(							self			:							Any   ):
							return TaConfig(
							    vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						d_ff=self.d_ff    ,						d_kv=self.hidden_size // self.num_attention_heads    ,						num_layers=self.num_hidden_layers    ,						num_decoder_layers=self.decoder_layers    ,						num_heads=self.num_attention_heads    ,						relative_attention_num_buckets=self.relative_attention_num_buckets    ,						dropout_rate=self.dropout_rate    ,						initializer_factor=self.initializer_factor    ,						eos_token_id=self.eos_token_id    ,						bos_token_id=self.pad_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.decoder_start_token_id    ,						)

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							int    ,						snake_case_			:							str    ,						snake_case_			:							Union[str, Any]    ,						snake_case_			:							List[Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							Any    ,						):
							UpperCamelCase_:				Tuple						=    UMTaModel(config=snake_case_   )
							model.to(snake_case_   )
							model.eval()
							UpperCamelCase_:				Union[str, Any]						=    model(
							    input_ids=snake_case_    ,						decoder_input_ids=snake_case_    ,						attention_mask=snake_case_    ,						decoder_attention_mask=snake_case_    ,						)
							UpperCamelCase_:				str						=    model(input_ids=snake_case_    ,						decoder_input_ids=snake_case_   )
							UpperCamelCase_:				List[Any]						=    result.last_hidden_state
							UpperCamelCase_:				Union[str, Any]						=    result.past_key_values
							UpperCamelCase_:				Union[str, Any]						=    result.encoder_last_hidden_state

							self.parent.assertEqual(encoder_output.size()    ,						(self.batch_size, self.encoder_seq_length, self.hidden_size)   )
							self.parent.assertEqual(decoder_output.size()    ,						(self.batch_size, self.decoder_seq_length, self.hidden_size)   )
							# There should be `num_layers` key value embeddings stored in decoder_past
							self.parent.assertEqual(len(snake_case_   )    ,						config.num_layers   )
							# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
							self.parent.assertEqual(len(decoder_past[0]   )    ,						4   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							List[Any]    ,						):
							UpperCamelCase_:				int						=    UMTaModel(config=snake_case_   ).get_decoder().to(snake_case_   ).eval()
							# first forward pass
							UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						use_cache=snake_case_   )
							UpperCamelCase_:				Any						=    model(snake_case_   )
							UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						use_cache=snake_case_   )

							self.parent.assertTrue(len(snake_case_   ) == len(snake_case_   )   )
							self.parent.assertTrue(len(snake_case_   ) == len(snake_case_   ) + 1   )

							UpperCamelCase_, UpperCamelCase_:				int						=    outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							UpperCamelCase_:				Union[str, Any]						=    ids_tensor((self.batch_size, 1)    ,						config.vocab_size   )

							# append to next input_ids and
							UpperCamelCase_:				Tuple						=    torch.cat([input_ids, next_tokens]    ,						dim=-1   )

							UpperCamelCase_:				List[str]						=    model(snake_case_   )["""last_hidden_state"""]
							UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						past_key_values=snake_case_   )["""last_hidden_state"""]

							# select random slice
							UpperCamelCase_:				List[str]						=    ids_tensor((1,)    ,						output_from_past.shape[-1]   ).item()
							UpperCamelCase_:				Dict						=    output_from_no_past[:, -1, random_slice_idx].detach()
							UpperCamelCase_:				Dict						=    output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(snake_case_    ,						snake_case_    ,						atol=1e-3   )   )




			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							List[Any]    ,						):
							UpperCamelCase_:				Dict						=    UMTaModel(config=snake_case_   ).to(snake_case_   ).half().eval()
							UpperCamelCase_:				List[str]						=    model(**snake_case_   )["""last_hidden_state"""]
							self.parent.assertFalse(torch.isnan(snake_case_   ).any().item()   )




@require_torch
class 							_UpperCamelCase					(       snake_case_  ,							snake_case_  ,							snake_case_  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			__UpperCamelCase   :    List[Any]          =			(UMTaForConditionalGeneration,) if is_torch_available() else ()
			__UpperCamelCase   :    Dict          =			(
			    {
			        """conversational""": UMTaForConditionalGeneration,
			        """feature-extraction""": UMTaModel,
			        """summarization""": UMTaForConditionalGeneration,
			        """text2text-generation""": UMTaForConditionalGeneration,
			        """translation""": UMTaForConditionalGeneration,
			        """question-answering""": UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			__UpperCamelCase   :    str          =			True
			__UpperCamelCase   :    str          =			False
			__UpperCamelCase   :    List[Any]          =			False
			__UpperCamelCase   :    Dict          =			True
			__UpperCamelCase   :    str          =			True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			__UpperCamelCase   :    Tuple          =			[0.8, 0.9]

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				str						=    UMTaModelTester(self   )

			@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""   )
			def    lowerCAmelCase__						(							self			:							Dict   ):
							UpperCamelCase_:				int						=    self.model_tester.prepare_config_and_inputs()
							UpperCamelCase_:				int						=    UMTaModel(config_and_inputs[0]   ).to(snake_case_   )
							with tempfile.TemporaryDirectory() as tmpdirname:
											torch.onnx.export(
											    snake_case_    ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,						f'''{tmpdirname}/t5_test.onnx'''    ,						export_params=snake_case_    ,						opset_version=9    ,						input_names=["""input_ids""", """decoder_input_ids"""]    ,						)

			@unittest.skipIf(torch_device == """cpu"""    ,						"""Cant do half precision"""   )
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							UpperCamelCase_:				Optional[Any]						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				List[Any]						=    ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
							UpperCamelCase_:				Tuple						=    self.model_tester.prepare_config_and_inputs()
							UpperCamelCase_:				int						=    config_and_inputs[0]
							UpperCamelCase_:				str						=    UMTaForConditionalGeneration(snake_case_   ).eval()
							model.to(snake_case_   )

							UpperCamelCase_:				List[Any]						=    {
							    """head_mask""": torch.zeros(config.num_layers    ,						config.num_heads    ,						device=snake_case_   ),
							    """decoder_head_mask""": torch.zeros(config.num_decoder_layers    ,						config.num_heads    ,						device=snake_case_   ),
							    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers    ,						config.num_heads    ,						device=snake_case_   ),
							}

							for attn_name, (name, mask) in zip(snake_case_    ,						head_masking.items()   ):
											UpperCamelCase_:				int						=    {name: mask}
											# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
											if name == "head_mask":
															UpperCamelCase_:				int						=    torch.ones(
															    config.num_decoder_layers    ,						config.num_heads    ,						device=snake_case_   )

											UpperCamelCase_:				Optional[int]						=    model.generate(
											    config_and_inputs[1]["""input_ids"""]    ,						num_beams=1    ,						max_length=3    ,						output_attentions=snake_case_    ,						return_dict_in_generate=snake_case_    ,						**snake_case_    ,						)
											# We check the state of decoder_attentions and cross_attentions just from the last step
											UpperCamelCase_:				Union[str, Any]						=    out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
											self.assertEqual(sum([w.sum().item() for w in attn_weights]   )    ,						0.0   )




			@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""   )
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							pass




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






			'''simple docstring'''

			@slow
			@unittest.skip(
			    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""   )
			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				List[Any]						=    UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""    ,						return_dict=snake_case_   ).to(snake_case_   )
							UpperCamelCase_:				Optional[int]						=    AutoTokenizer.from_pretrained("""google/umt5-small"""    ,						use_fast=snake_case_    ,						legacy=snake_case_   )
							UpperCamelCase_:				List[str]						=    [
							    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
							    """No se como puedo <extra_id_0>.""",
							    """This is the reason why we <extra_id_0> them.""",
							    """The <extra_id_0> walks in <extra_id_1>, seats""",
							    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
							]
							UpperCamelCase_:				List[str]						=    tokenizer(snake_case_    ,						return_tensors="""pt"""    ,						padding=snake_case_   ).input_ids
							# fmt: off
							UpperCamelCase_:				Tuple						=    torch.tensor(
							    [
							        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
							        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
							    ]   )
							# fmt: on
							torch.testing.assert_allclose(snake_case_    ,						snake_case_   )

							UpperCamelCase_:				List[str]						=    model.generate(input_ids.to(snake_case_   )   )
							UpperCamelCase_:				Union[str, Any]						=    [
							    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
							    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							]
							UpperCamelCase_:				List[Any]						=    tokenizer.batch_decode(snake_case_   )
							self.assertEqual(snake_case_    ,						snake_case_   )








import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__A							={
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None       ):
				# Initialise PyTorch model
				lowerCamelCase_         =     XLNetConfig.from_json_file(lowerCamelCase__       )

				lowerCamelCase_         =     finetuning_task.lower() if finetuning_task is not None else ""
				if finetuning_task in GLUE_TASKS_NUM_LABELS:
								print(F'Building PyTorch XLNetForSequenceClassification model from configuration: {config}'       )
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     GLUE_TASKS_NUM_LABELS[finetuning_task]
								lowerCamelCase_         =     XLNetForSequenceClassification(lowerCamelCase__       )
				elif "squad" in finetuning_task:
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     XLNetForQuestionAnswering(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     XLNetLMHeadModel(lowerCamelCase__       )

				# Load weights from tf checkpoint
				load_tf_weights_in_xlnet(lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				# Save pytorch-model
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				print(F'Save PyTorch model to {os.path.abspath(lowerCamelCase__       )}'       )
				torch.save(model.state_dict()      ,					lowerCamelCase__       )
				print(F'Save configuration file to {os.path.abspath(lowerCamelCase__       )}'       )
				with open(lowerCamelCase__      ,					"w"      ,					encoding="utf-8"       ) as f:
								f.write(config.to_json_string()       )


if __name__ == "__main__":
			__A							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--xlnet_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained XLNet model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
			)
			parser.add_argument(
			    '''--finetuning_task''',
			    default=None,
			    type=str,
			    help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
			)
			__A							=parser.parse_args()
			print(args)

			convert_xlnet_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
			)


