


import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
   from test_module.custom_tokenization_fast import CustomTokenizerFast






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       0
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
             lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)
             self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
             self.assertGreater(len(UpperCamelCase_		)   ,							0		)

        for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
             lowercase_       :Union[str, Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)
             self.assertIsInstance(UpperCamelCase_   ,							(GPTaTokenizer, GPTaTokenizerFast)		)
             self.assertGreater(len(UpperCamelCase_		)   ,							0		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
        self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
        self.assertEqual(tokenizer.vocab_size   ,							12		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
        self.assertIsInstance(UpperCamelCase_   ,							(RobertaTokenizer, RobertaTokenizerFast)		)
        self.assertEqual(tokenizer.vocab_size   ,							20		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       AutoConfig.from_pretrained(UpperCamelCase_		)
        self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
        # Check that tokenizer_type ≠ model_type
        lowercase_       :int     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							config=UpperCamelCase_		)
        self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
        self.assertEqual(tokenizer.vocab_size   ,							12		)
   def 		UpperCamelCase  (     self		):
        with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.txt'''		)		)

             lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''bert'''   ,							use_fast=UpperCamelCase_		)
             self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

        with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.json'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.json'''		)		)
             shutil.copy('''./tests/fixtures/merges.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''merges.txt'''		)		)

             lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''gpt2'''   ,							use_fast=UpperCamelCase_		)
             self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.txt'''		)		)

             lowercase_       :Any     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''bert'''		)
             self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

        with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.json'''   ,							os.path.join(UpperCamelCase_   ,							'''vocab.json'''		)		)
             shutil.copy('''./tests/fixtures/merges.txt'''   ,							os.path.join(UpperCamelCase_   ,							'''merges.txt'''		)		)

             lowercase_       :Any     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							tokenizer_type='''gpt2'''		)
             self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        with pytest.raises(UpperCamelCase_		):
             AutoTokenizer.from_pretrained('''./'''   ,							tokenizer_type='''xxx'''		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
             lowercase_       :Dict     =       tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased'''		)
             self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)

             if isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
                  self.assertEqual(tokenizer.basic_tokenizer.do_lower_case   ,							UpperCamelCase_		)
             else:
                  self.assertEqual(tokenizer.do_lower_case   ,							UpperCamelCase_		)

             self.assertEqual(tokenizer.model_max_length   ,							512		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
             with self.assertRaisesRegex(
                 UpperCamelCase_   ,							'''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'''   ,							):
                  lowercase_       :Optional[Any]     =       tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists'''		)
   def 		UpperCamelCase  (     self		):
        # tests: https://github.com/huggingface/transformers/pull/13251
        # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
        # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
        lowercase_       :Any     =       TOKENIZER_MAPPING.values()
        lowercase_       :str     =       []

        for slow_tok, fast_tok in tokenizers:
             if slow_tok is not None:
                  tokenizer_names.append(slow_tok.__name__		)

             if fast_tok is not None:
                  tokenizer_names.append(fast_tok.__name__		)

        for tokenizer_name in tokenizer_names:
             # must find the right class
             tokenizer_class_from_name(UpperCamelCase_		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''   ,							use_fast=UpperCamelCase_		)   ,							UpperCamelCase_		)
        self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''		)   ,							UpperCamelCase_		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       AutoTokenizer.from_pretrained('''distilbert-base-uncased'''   ,							do_lower_case=UpperCamelCase_		)
        lowercase_       :Tuple     =       '''Hello, world. How are you?'''
        lowercase_       :Optional[int]     =       tokenizer.tokenize(UpperCamelCase_		)
        self.assertEqual('''[UNK]'''   ,							tokens[0]		)

        lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''   ,							do_lower_case=UpperCamelCase_		)
        lowercase_       :Dict     =       tokenizer.tokenize(UpperCamelCase_		)
        self.assertEqual('''[UNK]'''   ,							tokens[0]		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config'''		)
        self.assertEqual(type(UpperCamelCase_		)   ,							UpperCamelCase_		)
        self.assertEqual(tokenizer.model_max_length   ,							512		)
        self.assertEqual(tokenizer.vocab_size   ,							3_0000		)
        self.assertEqual(tokenizer.unk_token   ,							'''[UNK]'''		)
        self.assertEqual(tokenizer.padding_side   ,							'''right'''		)
        self.assertEqual(tokenizer.truncation_side   ,							'''right'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
        self.assertIsInstance(UpperCamelCase_   ,							(BertTokenizer, BertTokenizerFast)		)
        with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(UpperCamelCase_		)
             lowercase_       :int     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)

        self.assertIsInstance(UpperCamelCase_   ,							tokenizer.__class__		)
        self.assertEqual(tokenizera.vocab_size   ,							12		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       AutoTokenizer.from_pretrained('''ctrl'''		)
        # There is no fast CTRL so this always gives us a slow tokenizer.
        self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        # Check we can load the tokenizer config of an online model.
        lowercase_       :Union[str, Any]     =       get_tokenizer_config('''bert-base-cased'''		)
        lowercase_       :Dict     =       config.pop('''_commit_hash'''   ,							UpperCamelCase_		)
        # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
        self.assertEqual(UpperCamelCase_   ,							{'''do_lower_case''': False}		)

        # This model does not have a tokenizer_config so we get back an empty dict.
        lowercase_       :Dict     =       get_tokenizer_config(UpperCamelCase_		)
        self.assertDictEqual(UpperCamelCase_   ,							{}		)

        # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
        lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
        with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(UpperCamelCase_		)
             lowercase_       :Optional[Any]     =       get_tokenizer_config(UpperCamelCase_		)

        # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
        self.assertEqual(config['''tokenizer_class''']   ,							'''BertTokenizer'''		)
   def 		UpperCamelCase  (     self		):
        try:
             AutoConfig.register('''custom'''   ,							UpperCamelCase_		)

             AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(UpperCamelCase_		):
                  AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)

             lowercase_       :List[Any]     =       CustomTokenizer.from_pretrained(UpperCamelCase_		)
             with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer.save_pretrained(UpperCamelCase_		)

                  lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
                  self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

        finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
   @require_tokenizers
   def 		UpperCamelCase  (     self		):
        try:
             AutoConfig.register('''custom'''   ,							UpperCamelCase_		)

             # Can register in two steps
             AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,							(CustomTokenizer, None)		)
             AutoTokenizer.register(UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,							(CustomTokenizer, CustomTokenizerFast)		)

             del TOKENIZER_MAPPING._extra_content[CustomConfig]
             # Can register in one step
             AutoTokenizer.register(
                 UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]   ,							(CustomTokenizer, CustomTokenizerFast)		)

             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(UpperCamelCase_		):
                  AutoTokenizer.register(UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)

             # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
             # and that model does not have a tokenizer.json
             with tempfile.TemporaryDirectory() as tmp_dir:
                  lowercase_       :Optional[Any]     =       BertTokenizerFast.from_pretrained(UpperCamelCase_		)
                  bert_tokenizer.save_pretrained(UpperCamelCase_		)
                  lowercase_       :str     =       CustomTokenizerFast.from_pretrained(UpperCamelCase_		)

             with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer.save_pretrained(UpperCamelCase_		)

                  lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(UpperCamelCase_		)
                  self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

                  lowercase_       :Dict     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
                  self.assertIsInstance(UpperCamelCase_   ,							UpperCamelCase_		)

        finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
   def 		UpperCamelCase  (     self		):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(UpperCamelCase_		):
             lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''		)
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(UpperCamelCase_		):
             lowercase_       :Tuple     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)

        lowercase_       :List[str]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)
        self.assertTrue(tokenizer.special_attribute_present		)
        # Test tokenizer can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(UpperCamelCase_		)
             lowercase_       :Any     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							trust_remote_code=UpperCamelCase_		)
        self.assertTrue(reloaded_tokenizer.special_attribute_present		)

        if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
             self.assertEqual(reloaded_tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)

             # Test we can also load the slow version
             lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
             self.assertTrue(tokenizer.special_attribute_present		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
             # Test tokenizer can be reloaded.
             with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer.save_pretrained(UpperCamelCase_		)
                  lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
             self.assertEqual(reloaded_tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
             self.assertTrue(reloaded_tokenizer.special_attribute_present		)
        else:
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
             self.assertEqual(reloaded_tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
   @require_tokenizers
   def 		UpperCamelCase  (     self		):






        class      UpperCamelCase					(		lowercase__     ):


           '''simple docstring'''







           lowercase					:	List[str]									=False






        class      UpperCamelCase					(		lowercase__     ):


           '''simple docstring'''







           lowercase					:	Any									=NewTokenizer
           lowercase					:	Union[str, Any]									=False

        try:
             AutoConfig.register('''custom'''   ,							UpperCamelCase_		)
             AutoTokenizer.register(UpperCamelCase_   ,							slow_tokenizer_class=UpperCamelCase_		)
             AutoTokenizer.register(UpperCamelCase_   ,							fast_tokenizer_class=UpperCamelCase_		)
             # If remote code is not set, the default is to use local
             lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
             self.assertFalse(tokenizer.special_attribute_present		)
             lowercase_       :Any     =       AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''   ,							use_fast=UpperCamelCase_		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
             self.assertFalse(tokenizer.special_attribute_present		)

             # If remote code is disabled, we load the local one.
             lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
             self.assertFalse(tokenizer.special_attribute_present		)
             lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
             self.assertFalse(tokenizer.special_attribute_present		)

             # If remote is enabled, we load from the Hub
             lowercase_       :Optional[Any]     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)
             self.assertTrue(tokenizer.special_attribute_present		)
             lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
             self.assertTrue(tokenizer.special_attribute_present		)

        finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                  del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                  del TOKENIZER_MAPPING._extra_content[CustomConfig]
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       AutoTokenizer.from_pretrained(
            '''hf-internal-testing/test_dynamic_tokenizer_legacy'''   ,							trust_remote_code=UpperCamelCase_		)
        self.assertTrue(tokenizer.special_attribute_present		)
        if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizerFast'''		)

             # Test we can also load the slow version
             lowercase_       :Any     =       AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer_legacy'''   ,							trust_remote_code=UpperCamelCase_   ,							use_fast=UpperCamelCase_		)
             self.assertTrue(tokenizer.special_attribute_present		)
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
        else:
             self.assertEqual(tokenizer.__class__.__name__   ,							'''NewTokenizer'''		)
   def 		UpperCamelCase  (     self		):
        with self.assertRaisesRegex(
            UpperCamelCase_   ,							'''bert-base is not a local folder and is not a valid model identifier'''		):
             lowercase_       :List[str]     =       AutoTokenizer.from_pretrained('''bert-base'''		)
   def 		UpperCamelCase  (     self		):
        with self.assertRaisesRegex(
            UpperCamelCase_   ,							R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
             lowercase_       :int     =       AutoTokenizer.from_pretrained(UpperCamelCase_   ,							revision='''aaaaaa'''		)




   def 		UpperCamelCase  (     self		):
        # Make sure we have cached the tokenizer.
        lowercase_       :List[Any]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
        with RequestCounter() as counter:
             lowercase_       :Optional[int]     =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)
             self.assertEqual(counter.get_request_count   ,							0		)
             self.assertEqual(counter.head_request_count   ,							1		)
             self.assertEqual(counter.other_request_count   ,							0		)





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							):
        lowercase_       :Dict     =       parent
        lowercase_       :Optional[Any]     =       13
        lowercase_       :Optional[Any]     =       7
        lowercase_       :List[Any]     =       30
        lowercase_       :int     =       self.seq_length + self.mem_len
        lowercase_       :Any     =       15
        lowercase_       :Optional[Any]     =       True
        lowercase_       :List[Any]     =       True
        lowercase_       :Any     =       99
        lowercase_       :Optional[int]     =       [10, 50, 80]
        lowercase_       :Union[str, Any]     =       32
        lowercase_       :List[Any]     =       32
        lowercase_       :Tuple     =       4
        lowercase_       :Tuple     =       8
        lowercase_       :List[Any]     =       128
        lowercase_       :Any     =       2
        lowercase_       :Tuple     =       2
        lowercase_       :Dict     =       None
        lowercase_       :Optional[Any]     =       1
        lowercase_       :Optional[int]     =       0
        lowercase_       :List[str]     =       3
        lowercase_       :Optional[int]     =       self.vocab_size - 1
        lowercase_       :List[Any]     =       0.01
   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)
        lowercase_       :List[str]     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       None
        if self.use_labels:
             lowercase_       :str     =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

        lowercase_       :int     =       TransfoXLConfig(
            vocab_size=self.vocab_size   ,							mem_len=self.mem_len   ,							clamp_len=self.clamp_len   ,							cutoffs=self.cutoffs   ,							d_model=self.hidden_size   ,							d_embed=self.d_embed   ,							n_head=self.num_attention_heads   ,							d_head=self.d_head   ,							d_inner=self.d_inner   ,							div_val=self.div_val   ,							n_layer=self.num_hidden_layers   ,							eos_token_id=self.eos_token_id   ,							pad_token_id=self.vocab_size - 1   ,							init_range=self.init_range   ,							num_labels=self.num_labels   ,							)

        return (config, input_ids_a, input_ids_a, lm_labels)
   def 		UpperCamelCase  (     self		):
        random.seed(self.seed		)
        tf.random.set_seed(self.seed		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :List[Any]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Dict     =       {'''input_ids''': input_ids_a, '''mems''': mems_a}

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel(UpperCamelCase_		)

        lowercase_    ,      lowercase_       :int     =       model(UpperCamelCase_		).to_tuple()

        lowercase_       :Optional[int]     =       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
        lowercase_    ,      lowercase_       :Optional[int]     =       model(UpperCamelCase_		).to_tuple()

        lowercase_    ,      lowercase_       :Tuple     =       model([input_ids_a, mems_a]		).to_tuple()

        lowercase_       :Union[str, Any]     =       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        lowercase_    ,      lowercase_       :Union[str, Any]     =       model(UpperCamelCase_		).to_tuple()

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

        self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       TFTransfoXLForSequenceClassification(UpperCamelCase_		)
        lowercase_       :int     =       model(UpperCamelCase_		)
        self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :str     =       self.prepare_config_and_inputs()
        ((lowercase_)    ,      (lowercase_)    ,      (lowercase_)    ,      (lowercase_))       :Tuple     =       config_and_inputs
        lowercase_       :Dict     =       {'''input_ids''': input_ids_a}
        return config, inputs_dict






@require_tf
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	str									=(
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   lowercase					:	Dict									=() if is_tf_available() else ()
   lowercase					:	List[str]									=(
       {
           """feature-extraction""": TFTransfoXLModel,
           """text-classification""": TFTransfoXLForSequenceClassification,
           """text-generation""": TFTransfoXLLMHeadModel,
           """zero-shot""": TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   lowercase					:	Optional[int]									=False
   lowercase					:	Tuple									=False
   lowercase					:	Dict									=False
   lowercase					:	Dict									=False
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

        return False
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       TFTransfoXLModelTester(self		)
        lowercase_       :str     =       ConfigTester(self   ,							config_class=UpperCamelCase_   ,							d_embed=37		)
   def 		UpperCamelCase  (     self		):
        self.config_tester.run_common_tests()
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :str     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        self.model_tester.set_seed()
        lowercase_       :Optional[Any]     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        lowercase_    ,      lowercase_       :Any     =       self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_       :List[str]     =       [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
             lowercase_       :Dict     =       model_class(UpperCamelCase_		)
             assert isinstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer		)
             if model_class in list_other_models_with_output_ebd:
                  lowercase_       :str     =       model.get_output_embeddings()
                  assert isinstance(UpperCamelCase_   ,							tf.keras.layers.Layer		)
                  lowercase_       :Optional[int]     =       model.get_bias()
                  assert name is None
             else:
                  lowercase_       :List[Any]     =       model.get_output_embeddings()
                  assert x is None
                  lowercase_       :Dict     =       model.get_bias()
                  assert name is None
   def 		UpperCamelCase  (     self		):
        # TODO JP: Make TransfoXL XLA compliant
        pass
   @slow
   def 		UpperCamelCase  (     self		):
        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_       :Dict     =       TFTransfoXLModel.from_pretrained(UpperCamelCase_		)
             self.assertIsNotNone(UpperCamelCase_		)




   @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''		)
   def 		UpperCamelCase  (     self		):
        pass






@require_tf
class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   @unittest.skip('''Skip test until #12651 is resolved.'''		)
   @slow
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''		)
        # fmt: off
        lowercase_       :List[str]     =       tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]   ,							dtype=tf.intaa		)  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        lowercase_       :List[Any]     =       [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        lowercase_       :Any     =       model.generate(UpperCamelCase_   ,							max_length=200   ,							do_sample=UpperCamelCase_		)
        self.assertListEqual(output_ids[0].numpy().tolist()   ,							UpperCamelCase_		)

