


"""simple docstring"""






import unittest

from transformers import AutoTokenizer, FalconConfig, 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 (
	    FalconForCausalLM,
	    FalconForQuestionAnswering,
	    FalconForSequenceClassification,
	    FalconForTokenClassification,
	    FalconModel,
	)


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=99       ,		UpperCAmelCase__=32       ,		UpperCAmelCase__=5       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=37       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=512       ,		UpperCAmelCase__=16       ,		UpperCAmelCase__=2       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=None       ,		):
								SCREAMING_SNAKE_CASE__     							=				parent
								SCREAMING_SNAKE_CASE__     							=				batch_size
								SCREAMING_SNAKE_CASE__     							=				seq_length
								SCREAMING_SNAKE_CASE__     							=				is_training
								SCREAMING_SNAKE_CASE__     							=				use_input_mask
								SCREAMING_SNAKE_CASE__     							=				use_token_type_ids
								SCREAMING_SNAKE_CASE__     							=				use_labels
								SCREAMING_SNAKE_CASE__     							=				vocab_size
								SCREAMING_SNAKE_CASE__     							=				hidden_size
								SCREAMING_SNAKE_CASE__     							=				num_hidden_layers
								SCREAMING_SNAKE_CASE__     							=				num_attention_heads
								SCREAMING_SNAKE_CASE__     							=				intermediate_size
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				hidden_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				max_position_embeddings
								SCREAMING_SNAKE_CASE__     							=				type_vocab_size
								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__     							=				scope



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size     )

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

								SCREAMING_SNAKE_CASE__     							=				None

								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]       ,		self.num_choices     )

								SCREAMING_SNAKE_CASE__     							=				self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



				def   lowerCAmelCase__		(						self     ):
								return FalconConfig(
								    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_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=UpperCAmelCase__       ,		initializer_range=self.initializer_range       ,		pad_token_id=1       ,		new_decoder_architecture=UpperCAmelCase__       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				FalconModel(config=UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__     )
								self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				FalconModel(UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(
								    UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		encoder_hidden_states=UpperCAmelCase__       ,		encoder_attention_mask=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				model(
								    UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		encoder_hidden_states=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__     )
								self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				FalconForCausalLM(config=UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		labels=UpperCAmelCase__     )
								self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				True
								SCREAMING_SNAKE_CASE__     							=				FalconForCausalLM(config=UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()

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

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

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

								SCREAMING_SNAKE_CASE__     							=				model(
								    UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		encoder_hidden_states=UpperCAmelCase__       ,		encoder_attention_mask=UpperCAmelCase__       ,		output_hidden_states=UpperCAmelCase__       ,		)["hidden_states"][0]
								SCREAMING_SNAKE_CASE__     							=				model(
								    UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		encoder_hidden_states=UpperCAmelCase__       ,		encoder_attention_mask=UpperCAmelCase__       ,		past_key_values=UpperCAmelCase__       ,		output_hidden_states=UpperCAmelCase__       ,		)["hidden_states"][0]

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






				def   lowerCAmelCase__		(						self     ):
								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__
								)							,      
								)     							=				config_and_inputs
								SCREAMING_SNAKE_CASE__     							=				{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			lowerCamelCase_		(			lowercase       ,  lowercase       ,  lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					(
				    (
				        FalconModel,
				        FalconForCausalLM,
				        FalconForSequenceClassification,
				        FalconForTokenClassification,
				        FalconForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase      :      str      							=					(FalconForCausalLM,) if is_torch_available() else ()
				_lowerCAmelCase      :      Optional[int]      							=					(
				    {
				        "feature-extraction": FalconModel,
				        "text-classification": FalconForSequenceClassification,
				        "text-generation": FalconForCausalLM,
				        "question-answering": FalconForQuestionAnswering,
				        "token-classification": FalconForTokenClassification,
				        "zero-shot": FalconForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowerCAmelCase      :      str      							=					False
				_lowerCAmelCase      :      Dict      							=					False



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				FalconModelTester(self     )
								SCREAMING_SNAKE_CASE__     							=				ConfigTester(self       ,		config_class=UpperCAmelCase__       ,		hidden_size=37     )



				def   lowerCAmelCase__		(						self     ):
								self.config_tester.run_common_tests()



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs()
								for alibi in [True, False]:
												SCREAMING_SNAKE_CASE__     							=				alibi
												self.model_tester.create_and_check_model(UpperCAmelCase__       ,		*UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE__     							=				3
								SCREAMING_SNAKE_CASE__     							=				input_dict["input_ids"]
								SCREAMING_SNAKE_CASE__     							=				input_ids.ne(1     ).to(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.model_tester.batch_size]       ,		self.model_tester.type_sequence_label_size     )
								SCREAMING_SNAKE_CASE__     							=				FalconForSequenceClassification(UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		labels=UpperCAmelCase__     )
								self.assertEqual(result.logits.shape       ,		(self.model_tester.batch_size, self.model_tester.num_labels)     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE__     							=				3
								SCREAMING_SNAKE_CASE__     							=				"single_label_classification"
								SCREAMING_SNAKE_CASE__     							=				input_dict["input_ids"]
								SCREAMING_SNAKE_CASE__     							=				input_ids.ne(1     ).to(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				ids_tensor([self.model_tester.batch_size]       ,		self.model_tester.type_sequence_label_size     )
								SCREAMING_SNAKE_CASE__     							=				FalconForSequenceClassification(UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		labels=UpperCAmelCase__     )
								self.assertEqual(result.logits.shape       ,		(self.model_tester.batch_size, self.model_tester.num_labels)     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE__     							=				input_dict["input_ids"]
								SCREAMING_SNAKE_CASE__     							=				FalconForCausalLM(UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		use_cache=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				input_ids.shape[0]
								SCREAMING_SNAKE_CASE__     							=				model._convert_to_rw_cache(result.past_key_values     )
								SCREAMING_SNAKE_CASE__     							=				model._convert_cache_to_standard_format(UpperCAmelCase__       ,		UpperCAmelCase__     )
								for layer in range(len(UpperCAmelCase__     )     ):
												for tensor_idx in range(2     ):
																self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3     )
																self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4     )
																self.assertTrue(
																    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]     )     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE__     							=				3
								SCREAMING_SNAKE_CASE__     							=				"multi_label_classification"
								SCREAMING_SNAKE_CASE__     							=				input_dict["input_ids"]
								SCREAMING_SNAKE_CASE__     							=				input_ids.ne(1     ).to(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]       ,		self.model_tester.type_sequence_label_size     ).to(torch.float     )
								SCREAMING_SNAKE_CASE__     							=				FalconForSequenceClassification(UpperCAmelCase__     )
								model.to(UpperCAmelCase__     )
								model.eval()
								SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		attention_mask=UpperCAmelCase__       ,		labels=UpperCAmelCase__     )
								self.assertEqual(result.logits.shape       ,		(self.model_tester.batch_size, self.model_tester.num_labels)     )






				def   lowerCAmelCase__		(						self     ):
								# Falcon can have different numbers of KV-heads than the number of query heads, so we need
								# to override this test to use the right head counts.
								for model_class in self.all_generative_model_classes:
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				self.model_tester.prepare_config_and_inputs_for_common()

												# If it doesn't support cache, pass the test
												if not hasattr(UpperCAmelCase__       ,		"use_cache"     ):
																return

												SCREAMING_SNAKE_CASE__     							=				model_class(UpperCAmelCase__     ).to(UpperCAmelCase__     )
												if "use_cache" not in inputs:
																SCREAMING_SNAKE_CASE__     							=				True
												SCREAMING_SNAKE_CASE__     							=				model(**UpperCAmelCase__     )

												# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
												if "past_key_values" not in outputs:
																return

												SCREAMING_SNAKE_CASE__     							=				(
												    getattr(UpperCAmelCase__       ,		"decoder_layers"       ,		UpperCAmelCase__     )
												    or getattr(UpperCAmelCase__       ,		"num_decoder_layers"       ,		UpperCAmelCase__     )
												    or config.num_hidden_layers
												)
												SCREAMING_SNAKE_CASE__     							=				getattr(UpperCAmelCase__       ,		"num_kv_heads"       ,		config.num_attention_heads     )
												SCREAMING_SNAKE_CASE__     							=				getattr(UpperCAmelCase__       ,		"d_model"       ,		config.hidden_size     )
												SCREAMING_SNAKE_CASE__     							=				embed_dim // num_attention_heads

												SCREAMING_SNAKE_CASE__     							=				outputs["past_key_values"]
												self.assertEqual(len(UpperCAmelCase__     )       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				inputs["input_ids"].shape
												for i in range(UpperCAmelCase__     ):
																if config.new_decoder_architecture:
																				SCREAMING_SNAKE_CASE__     							=				config.num_attention_heads
																elif config.multi_query:
																				SCREAMING_SNAKE_CASE__     							=				1
																self.assertEqual(len(past_kv[0]     )       ,		2     )  # K V for the decoder = 2
																self.assertEqual(
																    past_kv[i][0].shape       ,		(batch_size, num_attention_heads, seq_length, per_head_embed_dim)     )
																self.assertEqual(
																    past_kv[i][1].shape       ,		(batch_size, num_attention_heads, seq_length, per_head_embed_dim)     )


@require_torch
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"     )
								SCREAMING_SNAKE_CASE__     							=				FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"     )
								model.eval()
								model.to(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer("My favorite food is"       ,		return_tensors="pt"     ).to(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				(
								    "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
								)

								SCREAMING_SNAKE_CASE__     							=				model.generate(**UpperCAmelCase__       ,		do_sample=UpperCAmelCase__       ,		max_new_tokens=19     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.batch_decode(UpperCAmelCase__     )[0]

								self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								# The big models are way too big for the CI, so we use tiny random models that resemble their
								# architectures but with much smaller and fewer layers
								for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
												SCREAMING_SNAKE_CASE__     							=				AutoTokenizer.from_pretrained(UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				FalconForCausalLM.from_pretrained(UpperCAmelCase__     )
												model.eval()
												model.to(UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				tokenizer("My favorite food is"       ,		return_tensors="pt"     ).to(UpperCAmelCase__     )

												# We just test that these run without errors - the models are randomly initialized
												# and so the actual text outputs will be garbage
												model.generate(**UpperCAmelCase__       ,		do_sample=UpperCAmelCase__       ,		max_new_tokens=4     )
												model.generate(**UpperCAmelCase__       ,		do_sample=UpperCAmelCase__       ,		max_new_tokens=4     )
												model.generate(**UpperCAmelCase__       ,		num_beams=2       ,		max_new_tokens=4     )






				@slow
				def   lowerCAmelCase__		(						self     ):
								# The big models are way too big for the CI, so we use tiny random models that resemble their
								# architectures but with much smaller and fewer layers
								with torch.no_grad():
												for repo in [
												    "Rocketknight1/falcon-rw-1b",
												    "Rocketknight1/tiny-random-falcon-7b",
												    "Rocketknight1/tiny-random-falcon-40b",
												]:
																SCREAMING_SNAKE_CASE__     							=				AutoTokenizer.from_pretrained(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				FalconForCausalLM.from_pretrained(UpperCAmelCase__     )
																model.eval()
																model.to(device=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				tokenizer("My favorite food is"       ,		return_tensors="pt"     ).to(UpperCAmelCase__     )

																# Test results are the same with and without cache
																SCREAMING_SNAKE_CASE__     							=				model.generate(**UpperCAmelCase__       ,		do_sample=UpperCAmelCase__       ,		max_new_tokens=20       ,		use_cache=UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				model.generate(**UpperCAmelCase__       ,		do_sample=UpperCAmelCase__       ,		max_new_tokens=20       ,		use_cache=UpperCAmelCase__     )
																self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0     )




"""simple docstring"""






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
				_lowerCAmelCase      :      str      							=					field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
				_lowerCAmelCase      :      int      							=					field(
				    default=128       ,  metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }       ,  )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.task_name.lower()


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"train"
				_lowerCAmelCase      :      Union[str, Any]      							=					"dev"
				_lowerCAmelCase      :      List[Any]      							=					"test"


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      GlueDataTrainingArguments
				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[InputFeatures]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = Split.train       ,		UpperCAmelCase__ = None       ,		):
								warnings.warn(
								    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
								    "library. You can have a look at this example script for pointers: "
								    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"       ,		UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				args
								SCREAMING_SNAKE_CASE__     							=				glue_processors[args.task_name]()
								SCREAMING_SNAKE_CASE__     							=				glue_output_modes[args.task_name]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												try:
																SCREAMING_SNAKE_CASE__     							=				Split[mode]
												except KeyError:
																raise KeyError("mode is not a valid split name"     )
        # Load data features from cache or dataset file
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir       ,		f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE__     							=				label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
								with FileLock(UpperCAmelCase__     ):
												if os.path.exists(UpperCAmelCase__     ) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE__     							=				time.time()
																SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
																logger.info(
																    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''       ,		time.time() - start     )
												else:
																logger.info(f'''Creating features from dataset file at {args.data_dir}'''     )

																if mode == Split.dev:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_dev_examples(args.data_dir     )
																elif mode == Split.test:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_test_examples(args.data_dir     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_train_examples(args.data_dir     )
																if limit_length is not None:
																				SCREAMING_SNAKE_CASE__     							=				examples[:limit_length]
																SCREAMING_SNAKE_CASE__     							=				glue_convert_examples_to_features(
																    UpperCAmelCase__       ,		UpperCAmelCase__       ,		max_length=args.max_seq_length       ,		label_list=UpperCAmelCase__       ,		output_mode=self.output_mode       ,		)
																SCREAMING_SNAKE_CASE__     							=				time.time()
																torch.save(self.features       ,		UpperCAmelCase__     )
																# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																logger.info(
																    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''     )



				def __len__(						self     ):
								return len(self.features     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								return self.features[i]






				def   lowerCAmelCase__		(						self     ):
								return self.label_list
