import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXModel,
    )


class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=13			,			__lowercase=7			,			__lowercase=True			,			__lowercase=True			,			__lowercase=True			,			__lowercase=True			,			__lowercase=99			,			__lowercase=64			,			__lowercase=5			,			__lowercase=4			,			__lowercase=37			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=512			,			__lowercase=16			,			__lowercase=2			,			__lowercase=0.02			,			__lowercase=3			,			__lowercase=4			,			__lowercase=None			,			)	->     Union[str, Any]:
        __UpperCamelCase					:Optional[int]        =		parent
        __UpperCamelCase					:Dict        =		batch_size
        __UpperCamelCase					:Optional[int]        =		seq_length
        __UpperCamelCase					:List[str]        =		is_training
        __UpperCamelCase					:Tuple        =		use_input_mask
        __UpperCamelCase					:str        =		use_token_type_ids
        __UpperCamelCase					:Optional[int]        =		use_labels
        __UpperCamelCase					:Optional[int]        =		vocab_size
        __UpperCamelCase					:str        =		hidden_size
        __UpperCamelCase					:Union[str, Any]        =		num_hidden_layers
        __UpperCamelCase					:Optional[int]        =		num_attention_heads
        __UpperCamelCase					:str        =		intermediate_size
        __UpperCamelCase					:List[str]        =		hidden_act
        __UpperCamelCase					:Optional[int]        =		hidden_dropout_prob
        __UpperCamelCase					:Dict        =		attention_probs_dropout_prob
        __UpperCamelCase					:List[str]        =		max_position_embeddings
        __UpperCamelCase					:Optional[int]        =		type_vocab_size
        __UpperCamelCase					:Dict        =		type_sequence_label_size
        __UpperCamelCase					:int        =		initializer_range
        __UpperCamelCase					:List[str]        =		num_labels
        __UpperCamelCase					:Optional[int]        =		num_choices
        __UpperCamelCase					:List[Any]        =		scope
        __UpperCamelCase					:Union[str, Any]        =		vocab_size - 1







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:str        =		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size)

        __UpperCamelCase					:Union[str, Any]        =		None
        if self.use_input_mask:
            __UpperCamelCase					:Union[str, Any]        =		random_attention_mask([self.batch_size, self.seq_length])

        __UpperCamelCase					:Optional[int]        =		None
        if self.use_labels:
            __UpperCamelCase					:Any        =		ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels)

        __UpperCamelCase					:str        =		self.get_config()

        return config, input_ids, input_mask, token_labels







    def       UpperCamelCase__       (     self)	->     List[Any]:
        return GPTNeoXConfig(
            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=__lowercase			,			initializer_range=self.initializer_range			,			pad_token_id=self.pad_token_id			,			)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:str        =		self.prepare_config_and_inputs()

        __UpperCamelCase					:int        =		True

        return config, input_ids, input_mask, token_labels







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Any        =		GPTNeoXModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:int        =		model(__lowercase			,			attention_mask=__lowercase)
        __UpperCamelCase					:Tuple        =		model(__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     str:
        __UpperCamelCase					:str        =		True
        __UpperCamelCase					:Union[str, Any]        =		GPTNeoXModel(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:Optional[int]        =		model(__lowercase			,			attention_mask=__lowercase)
        self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:int        =		GPTNeoXForCausalLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:Dict        =		model(__lowercase			,			attention_mask=__lowercase			,			labels=__lowercase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:Any        =		self.num_labels
        __UpperCamelCase					:List[Any]        =		GPTNeoXForQuestionAnswering(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:str        =		model(__lowercase			,			attention_mask=__lowercase)
        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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		self.num_labels
        __UpperCamelCase					:Any        =		GPTNeoXForSequenceClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:Optional[int]        =		ids_tensor([self.batch_size]			,			self.type_sequence_label_size)
        __UpperCamelCase					:Optional[Any]        =		model(__lowercase			,			attention_mask=__lowercase			,			labels=__lowercase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     Tuple:
        __UpperCamelCase					:Optional[Any]        =		self.num_labels
        __UpperCamelCase					:Any        =		GPTNeoXForTokenClassification(__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase					:int        =		model(__lowercase			,			attention_mask=__lowercase			,			labels=__lowercase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Dict:
        __UpperCamelCase					:Optional[int]        =		True
        __UpperCamelCase					:List[str]        =		GPTNeoXForCausalLM(config=__lowercase)
        model.to(__lowercase)
        model.eval()

        # first forward pass
        __UpperCamelCase					:Tuple        =		model(__lowercase			,			attention_mask=__lowercase			,			use_cache=__lowercase)
        __UpperCamelCase					:str        =		outputs.past_key_values

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

        # append to next input_ids and
        __UpperCamelCase					:Any        =		torch.cat([input_ids, next_tokens]			,			dim=-1)
        __UpperCamelCase					:Optional[int]        =		torch.cat([input_mask, next_mask]			,			dim=-1)

        __UpperCamelCase					:Optional[int]        =		model(__lowercase			,			attention_mask=__lowercase			,			output_hidden_states=__lowercase)
        __UpperCamelCase					:List[str]        =		output_from_no_past['''hidden_states'''][0]
        __UpperCamelCase					:Dict        =		model(
            __lowercase			,			attention_mask=__lowercase			,			past_key_values=__lowercase			,			output_hidden_states=__lowercase			,			)['''hidden_states'''][0]

        # select random slice
        __UpperCamelCase					:List[str]        =		ids_tensor((1,)			,			output_from_past.shape[-1]).item()
        __UpperCamelCase					:Optional[int]        =		output_from_no_past[:, -3:, random_slice_idx].detach()
        __UpperCamelCase					:Dict        =		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(__lowercase			,			__lowercase			,			atol=1E-3))







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		self.prepare_config_and_inputs()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		config_and_inputs
        __UpperCamelCase					:List[Any]        =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict


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






    '''simple docstring'''






    a__     :						str      =				(
        (
            GPTNeoXModel,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a__     :						Union[str, Any]      =				(GPTNeoXForCausalLM,) if is_torch_available() else ()
    a__     :						Tuple      =				(
        {
            """feature-extraction""": GPTNeoXModel,
            """question-answering""": GPTNeoXForQuestionAnswering,
            """text-classification""": GPTNeoXForSequenceClassification,
            """text-generation""": GPTNeoXForCausalLM,
            """token-classification""": GPTNeoXForTokenClassification,
            """zero-shot""": GPTNeoXForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__     :						Optional[int]      =				False
    a__     :						Union[str, Any]      =				False
    a__     :						Any      =				False
    a__     :						str      =				False







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:str        =		GPTNeoXModelTester(self)
        __UpperCamelCase					:Union[str, Any]        =		ConfigTester(self			,			config_class=__lowercase			,			hidden_size=64			,			num_attention_heads=8)







    def       UpperCamelCase__       (     self)	->     Tuple:
        self.config_tester.run_common_tests()







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        # This regression test was failing with PyTorch < 1.3
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:str        =		self.model_tester.prepare_config_and_inputs_for_decoder()

        __UpperCamelCase					:int        =		None

        self.model_tester.create_and_check_model_as_decoder(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowercase			,			__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Union[str, Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:str        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowercase)







    @unittest.skip(reason='''Feed forward chunking is not implemented''')
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        pass







    @parameterized.expand([('''linear''',), ('''dynamic''',)])
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase					:Tuple        =		ids_tensor([1, 10]			,			config.vocab_size)
        __UpperCamelCase					:List[str]        =		ids_tensor([1, int(config.max_position_embeddings * 1.5)]			,			config.vocab_size)

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        __UpperCamelCase					:Tuple        =		GPTNeoXModel(__lowercase)
        original_model.to(__lowercase)
        original_model.eval()
        __UpperCamelCase					:Dict        =		original_model(__lowercase).last_hidden_state
        __UpperCamelCase					:Tuple        =		original_model(__lowercase).last_hidden_state

        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
        __UpperCamelCase					:Optional[Any]        =		{'''type''': scaling_type, '''factor''': 10.0}
        __UpperCamelCase					:str        =		GPTNeoXModel(__lowercase)
        scaled_model.to(__lowercase)
        scaled_model.eval()
        __UpperCamelCase					:List[Any]        =		scaled_model(__lowercase).last_hidden_state
        __UpperCamelCase					:int        =		scaled_model(__lowercase).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(__lowercase			,			__lowercase			,			atol=1E-5))
        else:
            self.assertFalse(torch.allclose(__lowercase			,			__lowercase			,			atol=1E-5))

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__lowercase			,			__lowercase			,			atol=1E-5))


@require_torch
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Union[str, Any]        =		AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped''')
        for checkpointing in [True, False]:
            __UpperCamelCase					:Optional[int]        =		GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped''')

            if checkpointing:
                model.gradient_checkpointing_enable()
            else:
                model.gradient_checkpointing_disable()
            model.to(__lowercase)

            __UpperCamelCase					:List[Any]        =		tokenizer('''My favorite food is'''			,			return_tensors='''pt''').to(__lowercase)
            # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
            # See: https://github.com/huggingface/transformers/pull/24193
            __UpperCamelCase					:Dict        =		'''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

            __UpperCamelCase					:List[str]        =		model.generate(**__lowercase			,			do_sample=__lowercase			,			max_new_tokens=20)
            __UpperCamelCase					:Union[str, Any]        =		tokenizer.batch_decode(__lowercase)[0]

            self.assertEqual(__lowercase			,			__lowercase)






import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__lowercase										= '''bert-base-cased'''
__lowercase										= '''google/pegasus-xsum'''
__lowercase										= [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowercase										= ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowercase										= '''patrickvonplaten/t5-tiny-random'''
__lowercase										= '''sshleifer/bart-tiny-random'''
__lowercase										= '''sshleifer/tiny-mbart'''
__lowercase										= '''sshleifer/tiny-marian-en-de'''



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		'''\n'''.join(SCREAMING_SNAKE_CASE     )
    Path(SCREAMING_SNAKE_CASE     ).open('''w'''     ).writelines(SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.source"""     )  ,      SCREAMING_SNAKE_CASE     )
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.target"""     )  ,      SCREAMING_SNAKE_CASE     )
    return tmp_dir


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    @slow
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:Dict        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:List[Any]        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Optional[int]        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:int        =		4
        __UpperCamelCase					:Any        =		8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        __UpperCamelCase					:str        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			src_lang=__lowercase			,			tgt_lang=__lowercase			,			)
        __UpperCamelCase					:Any        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert isinstance(__lowercase			,			__lowercase)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            __UpperCamelCase					:Optional[int]        =		shift_tokens_right(batch['''labels''']			,			tokenizer.pad_token_id)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch







    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        __UpperCamelCase					:Union[str, Any]        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:int        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Dict        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:Union[str, Any]        =		4
        __UpperCamelCase					:List[str]        =		LegacySeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=20			,			max_target_length=__lowercase			,			)
        __UpperCamelCase					:Dict        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

        __UpperCamelCase					:Union[str, Any]        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        __UpperCamelCase					:str        =		tmp_dir.joinpath('''train.source''').open().readlines()
        __UpperCamelCase					:int        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(__lowercase			,			__lowercase			,			128			,			__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		{x.name for x in tmp_dir.iterdir()}
        __UpperCamelCase					:int        =		{x.name for x in save_dir.iterdir()}
        __UpperCamelCase					:Optional[int]        =		save_dir.joinpath('''train.source''').open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(__lowercase) < len(__lowercase)
        assert len(__lowercase) == 1
        assert len(packed_examples[0]) == sum(len(__lowercase) for x in orig_examples)
        assert orig_paths == new_paths







    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,			reason='''This test requires fairseq''')
    def       UpperCamelCase__       (     self)	->     List[Any]:
        if not FAIRSEQ_AVAILABLE:
            return
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		self._get_dataset(max_len=64)
        __UpperCamelCase					:Union[str, Any]        =		64
        __UpperCamelCase					:Tuple        =		ds.make_dynamic_sampler(__lowercase			,			required_batch_size_multiple=__lowercase)
        __UpperCamelCase					:List[str]        =		[len(__lowercase) for x in batch_sampler]
        assert len(set(__lowercase)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(__lowercase) == len(__lowercase)  # no dropped or added examples
        __UpperCamelCase					:int        =		DataLoader(__lowercase			,			batch_sampler=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:List[str]        =		[]
        __UpperCamelCase					:int        =		[]
        for batch in data_loader:
            __UpperCamelCase					:List[Any]        =		batch['''input_ids'''].shape
            __UpperCamelCase					:Dict        =		src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            __UpperCamelCase					:Optional[int]        =		np.product(batch['''input_ids'''].shape)
            num_src_per_batch.append(__lowercase)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(__lowercase)
        assert num_src_per_batch[0] == max(__lowercase)
        if failures:
            raise AssertionError(f"""too many tokens in {len(__lowercase)} batches""")







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self._get_dataset(max_len=512)
        __UpperCamelCase					:Any        =		2
        __UpperCamelCase					:List[Any]        =		ds.make_sortish_sampler(__lowercase			,			shuffle=__lowercase)

        __UpperCamelCase					:List[Any]        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:Tuple        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2			,			sampler=__lowercase)

        __UpperCamelCase					:int        =		tokenizer.pad_token_id

        def count_pad_tokens(__lowercase			,			__lowercase="input_ids"):
            return [batch[k].eq(__lowercase).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(__lowercase			,			k='''labels''')) < sum(count_pad_tokens(__lowercase			,			k='''labels'''))
        assert sum(count_pad_tokens(__lowercase)) < sum(count_pad_tokens(__lowercase))
        assert len(__lowercase) == len(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase=1_000			,			__lowercase=128)	->     List[Any]:
        if os.getenv('''USE_REAL_DATA'''			,			__lowercase):
            __UpperCamelCase					:Optional[Any]        =		'''examples/seq2seq/wmt_en_ro'''
            __UpperCamelCase					:Dict        =		max_len * 2 * 64
            if not Path(__lowercase).joinpath('''train.len''').exists():
                save_len_file(__lowercase			,			__lowercase)
        else:
            __UpperCamelCase					:Union[str, Any]        =		'''examples/seq2seq/test_data/wmt_en_ro'''
            __UpperCamelCase					:Optional[int]        =		max_len * 4
            save_len_file(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:List[Any]        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			n_obs=__lowercase			,			)
        return ds, max_tokens, tokenizer







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._get_dataset()
        __UpperCamelCase					:List[str]        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=0			,			add_extra_examples=__lowercase))
        __UpperCamelCase					:Tuple        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=1			,			add_extra_examples=__lowercase))
        assert idsa.intersection(__lowercase) == set()







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained(__lowercase			,			use_fast=__lowercase)
        if tok_name == MBART_TINY:
            __UpperCamelCase					:Optional[Any]        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			src_lang='''EN'''			,			tgt_lang='''FR'''			,			)
            __UpperCamelCase					:Tuple        =		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            __UpperCamelCase					:Tuple        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			)
            __UpperCamelCase					:Optional[int]        =		train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(__lowercase) == 1 if tok_name == BART_TINY else len(__lowercase) == 0





