'''simple docstring'''

import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

UpperCAmelCase     		=   {
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

UpperCAmelCase     		=   {
    'jukebox': 512,
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		VOCAB_FILES_NAMES
						UpperCAmelCase							:   Any									=		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:   Tuple									=		PRETRAINED_LYRIC_TOKENS_SIZES
						UpperCAmelCase							:   Tuple									=		["input_ids", "attention_mask"]


						def __init__( self     ,	A_     ,	A_     ,	A_     ,	A_=["v3", "v2", "v2"]     ,	A_=512     ,	A_=5     ,	A_="<|endoftext|>"     ,	**A_     ,	)     ->     List[str]:
							lowerCAmelCase   =     AddedToken(A_     ,	lstrip=A_     ,	rstrip=A_				) if isinstance(A_     ,	A_				) else unk_token
							super().__init__(
							    unk_token=A_     ,	n_genres=A_     ,	version=A_     ,	max_n_lyric_tokens=A_     ,	**A_     ,	)
							lowerCAmelCase   =     version
							lowerCAmelCase   =     max_n_lyric_tokens
							lowerCAmelCase   =     n_genres

							with open(A_     ,	encoding="""utf-8"""				) as vocab_handle:
								lowerCAmelCase   =     json.load(A_				)

							with open(A_     ,	encoding="""utf-8"""				) as vocab_handle:
								lowerCAmelCase   =     json.load(A_				)

							with open(A_     ,	encoding="""utf-8"""				) as vocab_handle:
								lowerCAmelCase   =     json.load(A_				)

							lowerCAmelCase   =     r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""
							# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
							if len(self.lyrics_encoder				) == 79:
								lowerCAmelCase   =     oov.replace(r"""\-'"""     ,	r"""\-+'"""				)

							lowerCAmelCase   =     regex.compile(A_				)
							lowerCAmelCase   =     {v: k for k, v in self.artists_encoder.items()}
							lowerCAmelCase   =     {v: k for k, v in self.genres_encoder.items()}
							lowerCAmelCase   =     {v: k for k, v in self.lyrics_encoder.items()}


						@property
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							return len(self.artists_encoder				) + len(self.genres_encoder				) + len(self.lyrics_encoder				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							return dict(self.artists_encoder     ,	self.genres_encoder     ,	self.lyrics_encoder				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     [self.artists_encoder.get(A_     ,	0				) for artist in list_artists]
							for genres in range(len(A_				)				):
								lowerCAmelCase   =     [self.genres_encoder.get(A_     ,	0				) for genre in list_genres[genres]]
								lowerCAmelCase   =     list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]				))

							lowerCAmelCase   =     [[self.lyrics_encoder.get(A_     ,	0				) for character in list_lyrics[0]], [], []]
							return artists_id, list_genres, lyric_ids


						def     __snake_case   ( self     ,	A_				)     ->     Tuple:
							return list(A_				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	**A_				)     ->     Union[str, Any]:
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self.prepare_for_tokenization(A_     ,	A_     ,	A_				)
							lowerCAmelCase   =     self._tokenize(A_				)
							return artist, genre, lyrics


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_ = False				)     ->     Tuple[str, str, str, Dict[str, Any]]:
							for idx in range(len(self.version				)				):
								if self.version[idx] == "v3":
									lowerCAmelCase   =     artists[idx].lower()
									lowerCAmelCase   =     [genres[idx].lower()]
								else:
									lowerCAmelCase   =     self._normalize(artists[idx]				) + """.v2"""
									lowerCAmelCase   =     [
									    self._normalize(A_				) + """.v2""" for genre in genres[idx].split("""_"""				)
									]  # split is for the full dictionary with combined genres

							if self.version[0] == "v2":
								lowerCAmelCase   =     regex.compile(r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""				)
								lowerCAmelCase   =     """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
								lowerCAmelCase   =     {vocab[index]: index + 1 for index in range(len(A_				)				)}
								lowerCAmelCase   =     0
								lowerCAmelCase   =     len(A_				) + 1
								lowerCAmelCase   =     self.vocab
								lowerCAmelCase   =     {v: k for k, v in self.vocab.items()}
								lowerCAmelCase   =     """"""
							else:
								lowerCAmelCase   =     regex.compile(r"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"""				)

							lowerCAmelCase   =     self._run_strip_accents(A_				)
							lowerCAmelCase   =     lyrics.replace("""\\"""     ,	"""\n"""				)
							lowerCAmelCase   =     self.out_of_vocab.sub(""""""     ,	A_				), [], []
							return artists, genres, lyrics


						def     __snake_case   ( self     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     unicodedata.normalize("""NFD"""     ,	A_				)
							lowerCAmelCase   =     []
							for char in text:
								lowerCAmelCase   =     unicodedata.category(A_				)
								if cat == "Mn":
									continue
								output.append(A_				)
							return "".join(A_				)


						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     (
							    [chr(A_				) for i in range(ord("""a"""				)     ,	ord("""z"""				) + 1				)]
							    + [chr(A_				) for i in range(ord("""A"""				)     ,	ord("""Z"""				) + 1				)]
							    + [chr(A_				) for i in range(ord("""0"""				)     ,	ord("""9"""				) + 1				)]
							    + ["""."""]
							)
							lowerCAmelCase   =     frozenset(A_				)
							lowerCAmelCase   =     re.compile(r"""_+"""				)
							lowerCAmelCase   =     """""".join([c if c in accepted else """_""" for c in text.lower()]				)
							lowerCAmelCase   =     pattern.sub("""_"""     ,	A_				).strip("""_"""				)
							return text


						def     __snake_case   ( self     ,	A_				)     ->     str:
							return " ".join(A_				)


						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = False				)     ->     Dict:
							# Convert to TensorType
							if not isinstance(A_     ,	A_				):
								lowerCAmelCase   =     TensorType(A_				)

							# Get a function reference for the correct framework
							if tensor_type == TensorType.TENSORFLOW:
								if not is_tf_available():
									raise ImportError(
									    """Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."""				)
								import tensorflow as tf

								lowerCAmelCase   =     tf.constant
								lowerCAmelCase   =     tf.is_tensor
							elif tensor_type == TensorType.PYTORCH:
								if not is_torch_available():
									raise ImportError("""Unable to convert output to PyTorch tensors format, PyTorch is not installed."""				)
								import torch

								lowerCAmelCase   =     torch.tensor
								lowerCAmelCase   =     torch.is_tensor
							elif tensor_type == TensorType.JAX:
								if not is_flax_available():
									raise ImportError("""Unable to convert output to JAX tensors format, JAX is not installed."""				)
								import jax.numpy as jnp  # noqa: F811

								lowerCAmelCase   =     jnp.array
								lowerCAmelCase   =     _is_jax
							else:
								lowerCAmelCase   =     np.asarray
								lowerCAmelCase   =     _is_numpy

							# Do the tensor conversion in batch

							try:
								if prepend_batch_axis:
									lowerCAmelCase   =     [inputs]

								if not is_tensor(A_				):
									lowerCAmelCase   =     as_tensor(A_				)
							except:  # noqa E722
								raise ValueError(
								    """Unable to create tensor, you should probably activate truncation and/or padding """
								    """with 'padding=True' 'truncation=True' to have batched tensors with the same length."""				)

							return inputs


						def __call__( self     ,	A_     ,	A_     ,	A_=""     ,	A_="pt"				)     ->     BatchEncoding:
							lowerCAmelCase   =     [0, 0, 0]
							lowerCAmelCase   =     [artist] * len(self.version				)
							lowerCAmelCase   =     [genres] * len(self.version				)

							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self.tokenize(A_     ,	A_     ,	A_				)
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     self._convert_token_to_id(A_     ,	A_     ,	A_				)

							lowerCAmelCase   =     [-INFINITY] * len(full_tokens[-1]				)
							lowerCAmelCase   =     [
							    self.convert_to_tensors(
							        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]     ,	tensor_type=A_				)
							    for i in range(len(self.version				)				)
							]
							return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks}				)


						def     __snake_case   ( self     ,	A_     ,	A_ = None				)     ->     Tuple[str]:
							if not os.path.isdir(A_				):
								logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
								return

							lowerCAmelCase   =     os.path.join(
							    A_     ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""artists_file"""]				)
							with open(A_     ,	"""w"""     ,	encoding="""utf-8"""				) as f:
								f.write(json.dumps(self.artists_encoder     ,	ensure_ascii=A_				)				)

							lowerCAmelCase   =     os.path.join(
							    A_     ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""genres_file"""]				)
							with open(A_     ,	"""w"""     ,	encoding="""utf-8"""				) as f:
								f.write(json.dumps(self.genres_encoder     ,	ensure_ascii=A_				)				)

							lowerCAmelCase   =     os.path.join(
							    A_     ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""lyrics_file"""]				)
							with open(A_     ,	"""w"""     ,	encoding="""utf-8"""				) as f:
								f.write(json.dumps(self.lyrics_encoder     ,	ensure_ascii=A_				)				)

							return (artists_file, genres_file, lyrics_file)




						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     str:
							lowerCAmelCase   =     self.artists_decoder.get(A_				)
							lowerCAmelCase   =     [self.genres_decoder.get(A_				) for genre in genres_index]
							lowerCAmelCase   =     [self.lyrics_decoder.get(A_				) for character in lyric_index]
							return artist, genres, lyrics




'''simple docstring'''

from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertModel,
				)

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=7     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=99     ,	A_=32     ,	A_=2     ,	A_=4     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=512     ,	A_=16     ,	A_=2     ,	A_=0.0_2     ,	A_=3     ,	A_=4     ,	A_=None     ,	)     ->     Dict:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     13
							lowerCAmelCase   =     7
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True
							lowerCAmelCase   =     99
							lowerCAmelCase   =     384
							lowerCAmelCase   =     2
							lowerCAmelCase   =     4
							lowerCAmelCase   =     37
							lowerCAmelCase   =     """gelu"""
							lowerCAmelCase   =     0.1
							lowerCAmelCase   =     0.1
							lowerCAmelCase   =     512
							lowerCAmelCase   =     16
							lowerCAmelCase   =     2
							lowerCAmelCase   =     0.0_2
							lowerCAmelCase   =     3
							lowerCAmelCase   =     4
							lowerCAmelCase   =     128
							lowerCAmelCase   =     2
							lowerCAmelCase   =     9
							lowerCAmelCase   =     1
							lowerCAmelCase   =     None


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size				)

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

							lowerCAmelCase   =     None
							if self.use_token_type_ids:
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size				)

							lowerCAmelCase   =     None
							lowerCAmelCase   =     None
							lowerCAmelCase   =     None
							if self.use_labels:
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.type_sequence_label_size				)
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels				)
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.num_choices				)

							lowerCAmelCase   =     ConvBertConfig(
							    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     ,	initializer_range=self.initializer_range     ,	return_dict=A_     ,	)

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


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     int:
							lowerCAmelCase   =     TFConvBertModel(config=A_				)
							lowerCAmelCase   =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

							lowerCAmelCase   =     [input_ids, input_mask]
							lowerCAmelCase   =     model(A_				)

							lowerCAmelCase   =     model(A_				)

							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     TFConvBertForMaskedLM(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.vocab_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     TFConvBertForSequenceClassification(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Any:
							lowerCAmelCase   =     self.num_choices
							lowerCAmelCase   =     TFConvBertForMultipleChoice(config=A_				)
							lowerCAmelCase   =     tf.tile(tf.expand_dims(A_     ,	1				)     ,	(1, self.num_choices, 1)				)
							lowerCAmelCase   =     tf.tile(tf.expand_dims(A_     ,	1				)     ,	(1, self.num_choices, 1)				)
							lowerCAmelCase   =     tf.tile(tf.expand_dims(A_     ,	1				)     ,	(1, self.num_choices, 1)				)
							lowerCAmelCase   =     {
							    """input_ids""": multiple_choice_inputs_ids,
							    """attention_mask""": multiple_choice_input_mask,
							    """token_type_ids""": multiple_choice_token_type_ids,
							}
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.num_choices)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     TFConvBertForTokenClassification(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     TFConvBertForQuestionAnswering(config=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}

							lowerCAmelCase   =     model(A_				)
							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     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       
							)   =     config_and_inputs
							lowerCAmelCase   =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict







@require_tf
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		(
						    (
						        TFConvBertModel,
						        TFConvBertForMaskedLM,
						        TFConvBertForQuestionAnswering,
						        TFConvBertForSequenceClassification,
						        TFConvBertForTokenClassification,
						        TFConvBertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase							:   Union[str, Any]									=		(
						    {
						        "feature-extraction": TFConvBertModel,
						        "fill-mask": TFConvBertForMaskedLM,
						        "question-answering": TFConvBertForQuestionAnswering,
						        "text-classification": TFConvBertForSequenceClassification,
						        "token-classification": TFConvBertForTokenClassification,
						        "zero-shot": TFConvBertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCAmelCase							:   Union[str, Any]									=		False
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Dict									=		False


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     TFConvBertModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	hidden_size=37				)


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


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A_				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*A_				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_				)


						@slow
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     True
							lowerCAmelCase   =     True

							if hasattr(A_     ,	"""use_cache"""				):
								lowerCAmelCase   =     True

							lowerCAmelCase   =     getattr(self.model_tester     ,	"""encoder_seq_length"""     ,	self.model_tester.seq_length				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""key_length"""     ,	A_				)

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_				)
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     len(model(A_				)				)

								with tempfile.TemporaryDirectory() as tmpdirname:
									model.save_pretrained(A_     ,	saved_model=A_				)
									lowerCAmelCase   =     os.path.join(A_     ,	"""saved_model"""     ,	"""1"""				)
									lowerCAmelCase   =     tf.keras.models.load_model(A_				)
									lowerCAmelCase   =     model(A_				)

									if self.is_encoder_decoder:
										lowerCAmelCase   =     outputs["""encoder_hidden_states"""]
										lowerCAmelCase   =     outputs["""encoder_attentions"""]
									else:
										lowerCAmelCase   =     outputs["""hidden_states"""]
										lowerCAmelCase   =     outputs["""attentions"""]

									self.assertEqual(len(A_				)     ,	A_				)

									lowerCAmelCase   =     getattr(
									    self.model_tester     ,	"""expected_num_hidden_layers"""     ,	self.model_tester.num_hidden_layers + 1				)

									self.assertEqual(len(A_				)     ,	A_				)
									self.assertListEqual(
									    list(output_hidden_states[0].shape[-2:]				)     ,	[self.model_tester.seq_length, self.model_tester.hidden_size]     ,	)

									self.assertEqual(len(A_				)     ,	self.model_tester.num_hidden_layers				)
									self.assertListEqual(
									    list(output_attentions[0].shape[-3:]				)     ,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]     ,	)


						@slow
						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""				)
							self.assertIsNotNone(A_				)




						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     True
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""decoder_seq_length"""     ,	self.model_tester.seq_length				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""encoder_seq_length"""     ,	self.model_tester.seq_length				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""key_length"""     ,	A_				)
							lowerCAmelCase   =     getattr(self.model_tester     ,	"""key_length"""     ,	A_				)

							def check_decoder_attentions_output(A_				):
								lowerCAmelCase   =     len(A_				)
								self.assertEqual(out_len % 2     ,	0				)
								lowerCAmelCase   =     outputs.decoder_attentions
								self.assertEqual(len(A_				)     ,	self.model_tester.num_hidden_layers				)
								self.assertListEqual(
								    list(decoder_attentions[0].shape[-3:]				)     ,	[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]     ,	)

							def check_encoder_attentions_output(A_				):
								lowerCAmelCase   =     [
								    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
								]
								self.assertEqual(len(A_				)     ,	self.model_tester.num_hidden_layers				)
								self.assertListEqual(
								    list(attentions[0].shape[-3:]				)     ,	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]     ,	)

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     True
								lowerCAmelCase   =     False
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)
								lowerCAmelCase   =     len(A_				)
								self.assertEqual(config.output_hidden_states     ,	A_				)
								check_encoder_attentions_output(A_				)

								if self.is_encoder_decoder:
									lowerCAmelCase   =     model_class(A_				)
									lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)
									self.assertEqual(config.output_hidden_states     ,	A_				)
									check_decoder_attentions_output(A_				)

								# Check that output attentions can also be changed via the config
								del inputs_dict["output_attentions"]
								lowerCAmelCase   =     True
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)
								self.assertEqual(config.output_hidden_states     ,	A_				)
								check_encoder_attentions_output(A_				)

								# Check attention is always last and order is fine
								lowerCAmelCase   =     True
								lowerCAmelCase   =     True
								lowerCAmelCase   =     model_class(A_				)
								lowerCAmelCase   =     model(self._prepare_for_class(A_     ,	A_				)				)

								self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)     ,	len(A_				)				)
								self.assertEqual(model.config.output_hidden_states     ,	A_				)
								check_encoder_attentions_output(A_				)







@require_tf
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""				)
							lowerCAmelCase   =     tf.constant([[0, 1, 2, 3, 4, 5]]				)
							lowerCAmelCase   =     model(A_				)[0]

							lowerCAmelCase   =     [1, 6, 768]
							self.assertEqual(output.shape     ,	A_				)

							lowerCAmelCase   =     tf.constant(
							    [
							        [
							            [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
							            [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
							            [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
							        ]
							    ]				)
							tf.debugging.assert_near(output[:, :3, :3]     ,	A_     ,	atol=1e-4				)