


"""simple docstring"""


import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class 						_UpperCamelCase						(     UpperCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    tempfile.mkdtemp()
										__lowerCAmelCase       					=    5

										# Realm tok
										__lowerCAmelCase       					=    [
										    "[UNK]",
										    "[CLS]",
										    "[SEP]",
										    "[PAD]",
										    "[MASK]",
										    "test",
										    "question",
										    "this",
										    "is",
										    "the",
										    "first",
										    "second",
										    "third",
										    "fourth",
										    "fifth",
										    "record",
										    "want",
										    "##want",
										    "##ed",
										    "wa",
										    "un",
										    "runn",
										    "##ing",
										    ",",
										    "low",
										    "lowest",
										]
										__lowerCAmelCase       					=    os.path.join(self.tmpdirname					,     "realm_tokenizer"			)
										os.makedirs(__lowerCAmelCase					,     exist_ok=__lowerCAmelCase			)
										__lowerCAmelCase       					=    os.path.join(__lowerCAmelCase					,     VOCAB_FILES_NAMES["vocab_file"]			)
										with open(self.vocab_file					,     "w"					,     encoding="utf-8"			) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)

										__lowerCAmelCase       					=    os.path.join(self.tmpdirname					,     "realm_block_records"			)
										os.makedirs(__lowerCAmelCase					,     exist_ok=__lowerCAmelCase			)

							def     snake_case       (  self			):
										return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname					,     "realm_tokenizer"			)			)

							def     snake_case       (  self			):
										shutil.rmtree(self.tmpdirname			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    RealmConfig(num_block_records=self.num_block_records			)
										return config

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    Dataset.from_dict(
										    {
										        "id": ["0", "1"],
										        "question": ["foo", "bar"],
										        "answers": [["Foo", "Bar"], ["Bar"]],
										    }			)
										return dataset

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.array(
										    [
										        B"This is the first record",
										        B"This is the second record",
										        B"This is the third record",
										        B"This is the fourth record",
										        B"This is the fifth record",
										        B"This is a longer longer longer record",
										    ]					,     dtype=__lowerCAmelCase					,     )
										return block_records

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    RealmRetriever(
										    block_records=self.get_dummy_block_records()					,     tokenizer=self.get_tokenizer()					,     )
										return retriever

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_config()
										__lowerCAmelCase       					=    self.get_dummy_retriever()
										__lowerCAmelCase       					=    retriever.tokenizer

										__lowerCAmelCase       					=    np.array([0, 3]					,     dtype="long"			)
										__lowerCAmelCase       					=    tokenizer(["Test question"]			).input_ids
										__lowerCAmelCase       					=    tokenizer(
										    ["the fourth"]					,     add_special_tokens=__lowerCAmelCase					,     return_token_type_ids=__lowerCAmelCase					,     return_attention_mask=__lowerCAmelCase					,     ).input_ids
										__lowerCAmelCase       					=    config.reader_seq_len

										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    retriever(
										    __lowerCAmelCase					,     __lowerCAmelCase					,     answer_ids=__lowerCAmelCase					,     max_length=__lowerCAmelCase					,     return_tensors="np"			)

										self.assertEqual(len(__lowerCAmelCase			)					,     2			)
										self.assertEqual(len(__lowerCAmelCase			)					,     2			)
										self.assertEqual(len(__lowerCAmelCase			)					,     2			)
										self.assertEqual(concat_inputs.input_ids.shape					,     (2, 10)			)
										self.assertEqual(concat_inputs.attention_mask.shape					,     (2, 10)			)
										self.assertEqual(concat_inputs.token_type_ids.shape					,     (2, 10)			)
										self.assertEqual(concat_inputs.special_tokens_mask.shape					,     (2, 10)			)
										self.assertEqual(
										    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]			)					,     ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]					,     )
										self.assertEqual(
										    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]			)					,     ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]					,     )

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_config()
										__lowerCAmelCase       					=    self.get_dummy_retriever()
										__lowerCAmelCase       					=    retriever.tokenizer

										__lowerCAmelCase       					=    np.array([0, 3, 5]					,     dtype="long"			)
										__lowerCAmelCase       					=    tokenizer(["Test question"]			).input_ids
										__lowerCAmelCase       					=    tokenizer(
										    ["the fourth", "longer longer"]					,     add_special_tokens=__lowerCAmelCase					,     return_token_type_ids=__lowerCAmelCase					,     return_attention_mask=__lowerCAmelCase					,     ).input_ids
										__lowerCAmelCase       					=    config.reader_seq_len

										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    retriever(
										    __lowerCAmelCase					,     __lowerCAmelCase					,     answer_ids=__lowerCAmelCase					,     max_length=__lowerCAmelCase					,     return_tensors="np"			)

										self.assertEqual([False, True, True]					,     __lowerCAmelCase			)
										self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]					,     __lowerCAmelCase			)
										self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]					,     __lowerCAmelCase			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_dummy_retriever()
										retriever.save_pretrained(os.path.join(self.tmpdirname					,     "realm_block_records"			)			)

										# Test local path
										__lowerCAmelCase       					=    retriever.from_pretrained(os.path.join(self.tmpdirname					,     "realm_block_records"			)			)
										self.assertEqual(retriever.block_records[0]					,     B"This is the first record"			)

										# Test mocked remote path
										with patch("transformers.models.realm.retrieval_realm.hf_hub_download"			) as mock_hf_hub_download:
													__lowerCAmelCase       					=    os.path.join(
													    os.path.join(self.tmpdirname					,     "realm_block_records"			)					,     _REALM_BLOCK_RECORDS_FILENAME			)
													__lowerCAmelCase       					=    RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"			)

										self.assertEqual(retriever.block_records[0]					,     B"This is the first record"			)








"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "tf"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "pt"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"			) as vocab_file:
													vocab_file.write("\n".join(__a			)			)
													vocab_file.flush()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

										with TemporaryDirectory() as bert_save_dir:
													__lowerCAmelCase       					=    BertModel(BertConfig(vocab_size=len(__a			)			)			)
													model.save_pretrained(__a			)
													self._test_export(__a					,     "pt"					,     12					,     __a			)

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(Path(__a			)			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(__a			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    Path(__a			).joinpath("model.onnx"			)

													# Remove folder if exists
													if path.parent.exists():
																path.parent.rmdir()

													# Export
													convert(__a					,     __a					,     __a					,     __a					,     __a					,     **__a			)

													return path
										except Exception as e:
													self.fail(__a			)

							@require_torch
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "pt"			)

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

										__lowerCAmelCase       					=    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "tf"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    infer_shapes(__a					,     __a			)

										# Assert all variables are present
										self.assertEqual(len(__a			)					,     len(__a			)			)
										self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
										self.assertSequenceEqual(variable_names[:3]					,     __a			)
										self.assertSequenceEqual(variable_names[3:]					,     __a			)

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
													self.assertDictEqual(shapes[var_name]					,     {0: "batch", 1: "sequence"}			)

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["output_0"]					,     {0: "batch", 1: "sequence"}			)
										self.assertDictEqual(shapes["output_1"]					,     {0: "batch"}			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(__a			)					,     3			)

										# Should have exactly the same input names
										self.assertEqual(set(__a			)					,     set(__a			)			)

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(__a					,     (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])			)

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncNonContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(__a			)					,     1			)
										self.assertEqual(len(__a			)					,     1			)

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]					,     tokens["input_ids"]			)
										self.assertEqual(ordered_input_names[0]					,     "input_ids"			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




