

"""simple docstring"""




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


A_				:      List[str]           =			"bert-base-cased"
A_				:      List[Any]           =			"google/pegasus-xsum"
A_				:      Dict           =			[" Sam ate lunch today.", "Sams lunch ingredients."]
A_				:      str           =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
A_				:      Tuple           =			"patrickvonplaten/t5-tiny-random"
A_				:      int           =			"sshleifer/bart-tiny-random"
A_				:      str           =			"sshleifer/tiny-mbart"
A_				:      Tuple           =			"sshleifer/tiny-marian-en-de"

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   """\n""".join(snake_case__     )
			Path(snake_case__     ).open("""w"""     ).writelines(snake_case__     )

def 						A							(  snake_case__     ):




			'''simple docstring'''


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





class  lowerCamelCase    (A__      ):

			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]     ,					)
			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    int							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
						SCREAMING_SNAKE_CASE__										=   max(len(tokenizer.encode(__UpperCAmelCase							)							) for a in ARTICLES							)
						SCREAMING_SNAKE_CASE__										=   max(len(tokenizer.encode(__UpperCAmelCase							)							) for a in SUMMARIES							)
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   8
						assert max_len_target > max_src_len  # Will be truncated
						assert max_len_source > max_src_len  # Will be truncated
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
						SCREAMING_SNAKE_CASE__										=   SeqaSeqDataset(
						    __UpperCAmelCase     ,					data_dir=__UpperCAmelCase     ,					type_path="""train"""     ,					max_source_length=__UpperCAmelCase     ,					max_target_length=__UpperCAmelCase     ,					src_lang=__UpperCAmelCase     ,					tgt_lang=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   DataLoader(__UpperCAmelCase     ,					batch_size=2     ,					collate_fn=train_dataset.collate_fn							)
						for batch in dataloader:
									assert isinstance(__UpperCAmelCase     ,					__UpperCAmelCase							)
									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
									SCREAMING_SNAKE_CASE__										=   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        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
						SCREAMING_SNAKE_CASE__										=   max(len(tokenizer.encode(__UpperCAmelCase							)							) for a in ARTICLES							)
						SCREAMING_SNAKE_CASE__										=   max(len(tokenizer.encode(__UpperCAmelCase							)							) for a in SUMMARIES							)
						SCREAMING_SNAKE_CASE__										=   4
						SCREAMING_SNAKE_CASE__										=   LegacySeqaSeqDataset(
						    __UpperCAmelCase     ,					data_dir=__UpperCAmelCase     ,					type_path="""train"""     ,					max_source_length=2_0     ,					max_target_length=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   DataLoader(__UpperCAmelCase     ,					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 2_0 >= 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        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""							)

						SCREAMING_SNAKE_CASE__										=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
						SCREAMING_SNAKE_CASE__										=   tmp_dir.joinpath("""train.source"""							).open().readlines()
						SCREAMING_SNAKE_CASE__										=   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
						pack_data_dir(__UpperCAmelCase     ,					__UpperCAmelCase     ,					1_2_8     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {x.name for x in tmp_dir.iterdir()}
						SCREAMING_SNAKE_CASE__										=   {x.name for x in save_dir.iterdir()}
						SCREAMING_SNAKE_CASE__										=   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(__UpperCAmelCase							) < len(__UpperCAmelCase							)
						assert len(__UpperCAmelCase							) == 1
						assert len(packed_examples[0]							) == sum(len(__UpperCAmelCase							) for x in orig_examples							)
						assert orig_paths == new_paths

			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE     ,					reason="""This test requires fairseq"""							)
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Any:
						if not FAIRSEQ_AVAILABLE:
									return
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self._get_dataset(max_len=6_4							)
						SCREAMING_SNAKE_CASE__										=   6_4
						SCREAMING_SNAKE_CASE__										=   ds.make_dynamic_sampler(__UpperCAmelCase     ,					required_batch_size_multiple=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   [len(__UpperCAmelCase							) for x in batch_sampler]
						assert len(set(__UpperCAmelCase							)							) > 1  # it's not dynamic batch size if every batch is the same length
						assert sum(__UpperCAmelCase							) == len(__UpperCAmelCase							)  # no dropped or added examples
						SCREAMING_SNAKE_CASE__										=   DataLoader(__UpperCAmelCase     ,					batch_sampler=__UpperCAmelCase     ,					collate_fn=ds.collate_fn     ,					num_workers=2							)
						SCREAMING_SNAKE_CASE__										=   []
						SCREAMING_SNAKE_CASE__										=   []
						for batch in data_loader:
									SCREAMING_SNAKE_CASE__										=   batch["""input_ids"""].shape
									SCREAMING_SNAKE_CASE__										=   src_shape[0]
									assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
									SCREAMING_SNAKE_CASE__										=   np.product(batch["""input_ids"""].shape							)
									num_src_per_batch.append(__UpperCAmelCase							)
									if num_src_tokens > (max_tokens * 1.1):
												failures.append(__UpperCAmelCase							)
						assert num_src_per_batch[0] == max(__UpperCAmelCase							)
						if failures:
									raise AssertionError(F"""too many tokens in {len(__UpperCAmelCase							)} batches"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self._get_dataset(max_len=5_1_2							)
						SCREAMING_SNAKE_CASE__										=   2
						SCREAMING_SNAKE_CASE__										=   ds.make_sortish_sampler(__UpperCAmelCase     ,					shuffle=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   DataLoader(__UpperCAmelCase     ,					batch_size=__UpperCAmelCase     ,					collate_fn=ds.collate_fn     ,					num_workers=2							)
						SCREAMING_SNAKE_CASE__										=   DataLoader(__UpperCAmelCase     ,					batch_size=__UpperCAmelCase     ,					collate_fn=ds.collate_fn     ,					num_workers=2     ,					sampler=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.pad_token_id

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

						assert sum(count_pad_tokens(__UpperCAmelCase     ,					k="""labels"""							)							) < sum(count_pad_tokens(__UpperCAmelCase     ,					k="""labels"""							)							)
						assert sum(count_pad_tokens(__UpperCAmelCase							)							) < sum(count_pad_tokens(__UpperCAmelCase							)							)
						assert len(__UpperCAmelCase							) == len(__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Union[str, Any]=1_0_0_0     ,					__UpperCAmelCase					:    Dict=1_2_8							) ->     Tuple:
						if os.getenv("""USE_REAL_DATA"""     ,					__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   """examples/seq2seq/wmt_en_ro"""
									SCREAMING_SNAKE_CASE__										=   max_len * 2 * 6_4
									if not Path(__UpperCAmelCase							).joinpath("""train.len"""							).exists():
												save_len_file(__UpperCAmelCase     ,					__UpperCAmelCase							)
						else:
									SCREAMING_SNAKE_CASE__										=   """examples/seq2seq/test_data/wmt_en_ro"""
									SCREAMING_SNAKE_CASE__										=   max_len * 4
									save_len_file(__UpperCAmelCase     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   SeqaSeqDataset(
						    __UpperCAmelCase     ,					data_dir=__UpperCAmelCase     ,					type_path="""train"""     ,					max_source_length=__UpperCAmelCase     ,					max_target_length=__UpperCAmelCase     ,					n_obs=__UpperCAmelCase     ,					)
						return ds, max_tokens, tokenizer

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Tuple:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self._get_dataset()
						SCREAMING_SNAKE_CASE__										=   set(DistributedSortishSampler(__UpperCAmelCase     ,					2_5_6     ,					num_replicas=2     ,					rank=0     ,					add_extra_examples=__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   set(DistributedSortishSampler(__UpperCAmelCase     ,					2_5_6     ,					num_replicas=2     ,					rank=1     ,					add_extra_examples=__UpperCAmelCase							)							)
						assert idsa.intersection(__UpperCAmelCase							) == set()





			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]     ,					)
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    Optional[Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained(__UpperCAmelCase     ,					use_fast=__UpperCAmelCase							)
						if tok_name == MBART_TINY:
									SCREAMING_SNAKE_CASE__										=   SeqaSeqDataset(
									    __UpperCAmelCase     ,					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"""     ,					)
									SCREAMING_SNAKE_CASE__										=   train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
						else:
									SCREAMING_SNAKE_CASE__										=   SeqaSeqDataset(
									    __UpperCAmelCase     ,					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     ,					)
									SCREAMING_SNAKE_CASE__										=   train_dataset.dataset_kwargs
									assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
									assert len(__UpperCAmelCase							) == 1 if tok_name == BART_TINY else len(__UpperCAmelCase							) == 0





"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


