import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
					    VideoMAEForPreTraining,
					    VideoMAEForVideoClassification,
					    VideoMAEModel,
					)
					from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from transformers import VideoMAEImageProcessor





class 					lowercase__						:


						def __init__(       self	:				Tuple    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Any=13    ,		UpperCamelCase__	:				Union[str, Any]=10    ,		UpperCamelCase__	:				Optional[int]=3    ,		UpperCamelCase__	:				Optional[int]=2    ,		UpperCamelCase__	:				Dict=2    ,		UpperCamelCase__	:				Union[str, Any]=2    ,		UpperCamelCase__	:				Union[str, Any]=True    ,		UpperCamelCase__	:				Union[str, Any]=True    ,		UpperCamelCase__	:				List[Any]=32    ,		UpperCamelCase__	:				int=5    ,		UpperCamelCase__	:				Optional[Any]=4    ,		UpperCamelCase__	:				int=37    ,		UpperCamelCase__	:				int="gelu"    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				int=0.1    ,		UpperCamelCase__	:				Any=10    ,		UpperCamelCase__	:				Tuple=0.02    ,		UpperCamelCase__	:				Any=0.9    ,		UpperCamelCase__	:				List[Any]=None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							parent
												SCREAMING_SNAKE_CASE							:    Tuple							=							batch_size
												SCREAMING_SNAKE_CASE							:    Any							=							image_size
												SCREAMING_SNAKE_CASE							:    Tuple							=							num_channels
												SCREAMING_SNAKE_CASE							:    List[Any]							=							patch_size
												SCREAMING_SNAKE_CASE							:    Any							=							tubelet_size
												SCREAMING_SNAKE_CASE							:    int							=							num_frames
												SCREAMING_SNAKE_CASE							:    int							=							is_training
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							use_labels
												SCREAMING_SNAKE_CASE							:    int							=							hidden_size
												SCREAMING_SNAKE_CASE							:    str							=							num_hidden_layers
												SCREAMING_SNAKE_CASE							:    str							=							num_attention_heads
												SCREAMING_SNAKE_CASE							:    List[Any]							=							intermediate_size
												SCREAMING_SNAKE_CASE							:    str							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Any							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    List[Any]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    Tuple							=							type_sequence_label_size
												SCREAMING_SNAKE_CASE							:    Dict							=							initializer_range
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							mask_ratio
												SCREAMING_SNAKE_CASE							:    int							=							scope

												# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(image_size // patch_size) ** 2
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(num_frames // tubelet_size) * self.num_patches_per_frame

												# use this variable to define bool_masked_pos
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							int(mask_ratio * self.seq_length						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							floats_tensor(
												    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							None
												if self.use_labels:
																		SCREAMING_SNAKE_CASE							:    str							=							ids_tensor([self.batch_size]    ,		self.type_sequence_label_size						)

												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_config()

												return config, pixel_values, labels


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												return VideoMAEConfig(
												    image_size=self.image_size    ,		patch_size=self.patch_size    ,		num_channels=self.num_channels    ,		num_frames=self.num_frames    ,		tubelet_size=self.tubelet_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    ,		is_decoder=UpperCamelCase__    ,		initializer_range=self.initializer_range    ,		)


						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							VideoMAEModel(config=UpperCamelCase__						)
												model.to(UpperCamelCase__						)
												model.eval()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__						)
												self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)						)


						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							VideoMAEForPreTraining(UpperCamelCase__						)
												model.to(UpperCamelCase__						)
												model.eval()
												# important: each video needs to have the same number of masked patches
												# hence we define a single mask, which we then repeat for each example in the batch
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.ones((self.num_masks,)						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.cat([mask, torch.zeros(self.seq_length - mask.size(0						)						)]						)
												SCREAMING_SNAKE_CASE							:    Any							=							mask.expand(self.batch_size    ,		-1						).bool()

												SCREAMING_SNAKE_CASE							:    List[str]							=							model(UpperCamelCase__    ,		UpperCamelCase__						)
												# model only returns predictions for masked patches
												SCREAMING_SNAKE_CASE							:    List[Any]							=							mask.sum().item()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							3 * self.tubelet_size * self.patch_size**2
												self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, num_masked_patches, decoder_num_labels)						)



						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							config_and_inputs
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{'''pixel_values''': pixel_values}
												return config, inputs_dict






@require_torch
class 					lowercase__						(   UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase):
						UpperCamelCase_     =		(
						    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
						)
						UpperCamelCase_     =		(
						    {"""feature-extraction""": VideoMAEModel, """video-classification""": VideoMAEForVideoClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase_     =		False
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False
						UpperCamelCase_     =		False


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							VideoMAEModelTester(self						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							ConfigTester(self    ,		config_class=UpperCamelCase__    ,		has_text_modality=UpperCamelCase__    ,		hidden_size=37						)


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				Optional[int]=False						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							copy.deepcopy(UpperCamelCase__						)

												if model_class == VideoMAEForPreTraining:
																		# important: each video needs to have the same number of masked patches
																		# hence we define a single mask, which we then repeat for each example in the batch
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							torch.ones((self.model_tester.num_masks,)						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0						)						)]						)
																		SCREAMING_SNAKE_CASE							:    str							=							mask.expand(self.model_tester.batch_size    ,		-1						).bool()
																		SCREAMING_SNAKE_CASE							:    Any							=							bool_masked_pos.to(UpperCamelCase__						)

												if return_labels:
																		if model_class in [
																		    *get_values(UpperCamelCase__						),
																		]:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							torch.zeros(
																								    self.model_tester.batch_size    ,		dtype=torch.long    ,		device=UpperCamelCase__						)

												return inputs_dict


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												self.config_tester.run_common_tests()


						@unittest.skip(reason='''VideoMAE does not use inputs_embeds'''						)
						def        __A       (       self	:				int						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Tuple							=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							model_class(UpperCamelCase__						)
																		self.assertIsInstance(model.get_input_embeddings()    ,		(nn.Module)						)
																		SCREAMING_SNAKE_CASE							:    Any							=							model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(UpperCamelCase__    ,		nn.Linear						)						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							model_class(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							inspect.signature(model.forward						)
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[*signature.parameters.keys()]

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							['''pixel_values''']
																		self.assertListEqual(arg_names[:1]    ,		UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__						)


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							VideoMAEModel.from_pretrained(UpperCamelCase__						)
																		self.assertIsNotNone(UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												if not self.has_attentions:
																		pass

												else:
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							self.model_tester.prepare_config_and_inputs_for_common()
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True

																		for model_class in self.all_model_classes:
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model_tester.seq_length - self.model_tester.num_masks
																								SCREAMING_SNAKE_CASE							:    Any							=							(
																								    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																								)

																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							True
																								SCREAMING_SNAKE_CASE							:    int							=							False
																								SCREAMING_SNAKE_CASE							:    int							=							True
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model_class(UpperCamelCase__						)
																								model.to(UpperCamelCase__						)
																								model.eval()
																								with torch.no_grad():
																														SCREAMING_SNAKE_CASE							:    Tuple							=							model(**self._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__						)						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							outputs.attentions
																								self.assertEqual(len(UpperCamelCase__						)    ,		self.model_tester.num_hidden_layers						)

																								# check that output_attentions also work using config
																								del inputs_dict["output_attentions"]
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							True
																								SCREAMING_SNAKE_CASE							:    List[str]							=							model_class(UpperCamelCase__						)
																								model.to(UpperCamelCase__						)
																								model.eval()
																								with torch.no_grad():
																														SCREAMING_SNAKE_CASE							:    Dict							=							model(**self._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__						)						)
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							outputs.attentions
																								self.assertEqual(len(UpperCamelCase__						)    ,		self.model_tester.num_hidden_layers						)

																								self.assertListEqual(
																								    list(attentions[0].shape[-3:]						)    ,		[self.model_tester.num_attention_heads, seq_len, seq_len]    ,		)
																								SCREAMING_SNAKE_CASE							:    str							=							len(UpperCamelCase__						)

																								# Check attention is always last and order is fine
																								SCREAMING_SNAKE_CASE							:    Tuple							=							True
																								SCREAMING_SNAKE_CASE							:    Tuple							=							True
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model_class(UpperCamelCase__						)
																								model.to(UpperCamelCase__						)
																								model.eval()
																								with torch.no_grad():
																														SCREAMING_SNAKE_CASE							:    Optional[int]							=							model(**self._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__						)						)

																								self.assertEqual(out_len + 1    ,		len(UpperCamelCase__						)						)

																								SCREAMING_SNAKE_CASE							:    str							=							outputs.attentions

																								self.assertEqual(len(UpperCamelCase__						)    ,		self.model_tester.num_hidden_layers						)
																								self.assertListEqual(
																								    list(self_attentions[0].shape[-3:]						)    ,		[self.model_tester.num_attention_heads, seq_len, seq_len]    ,		)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												def check_hidden_states_output(UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				Any						):
																		SCREAMING_SNAKE_CASE							:    int							=							model_class(UpperCamelCase__						)
																		model.to(UpperCamelCase__						)
																		model.eval()

																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							model(**self._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__						)						)

																		SCREAMING_SNAKE_CASE							:    List[Any]							=							outputs.hidden_states
																		SCREAMING_SNAKE_CASE							:    Any							=							self.model_tester.num_hidden_layers + 1
																		self.assertEqual(len(UpperCamelCase__						)    ,		UpperCamelCase__						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.model_tester.seq_length - self.model_tester.num_masks
																		SCREAMING_SNAKE_CASE							:    str							=							num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]						)    ,		[seq_length, self.model_tester.hidden_size]    ,		)

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:    int							=							True
																		check_hidden_states_output(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							True

																		check_hidden_states_output(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)



						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												pass






def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''		,  filename='''eating_spaghetti.npy'''		,  repo_type='''dataset'''						)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							np.load(_lowercase						)
						return list(_lowercase						)





@require_torch
@require_vision
class 					lowercase__						(   unittest.TestCase):


						@cached_property
						def        __A       (       self	:				Any						):





												'''simple docstring'''





												return (
												    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]    ,		image_std=[0.5, 0.5, 0.5]						)
												    if is_vision_available()
												    else None
												)


						@slow
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics'''						).to(
												    UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    int							=							self.default_image_processor
												SCREAMING_SNAKE_CASE							:    Any							=							prepare_video()
												SCREAMING_SNAKE_CASE							:    List[str]							=							image_processor(UpperCamelCase__    ,		return_tensors='''pt'''						).to(UpperCamelCase__						)

												# forward pass
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:    Any							=							model(**UpperCamelCase__						)

												# verify the logits
												SCREAMING_SNAKE_CASE							:    str							=							torch.Size((1, 400)						)
												self.assertEqual(outputs.logits.shape    ,		UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							torch.tensor([0.3669, -0.0688, -0.2421]						).to(UpperCamelCase__						)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		UpperCamelCase__    ,		atol=1E-4						)						)



						@slow
						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''						).to(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.default_image_processor
												SCREAMING_SNAKE_CASE							:    List[Any]							=							prepare_video()
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image_processor(UpperCamelCase__    ,		return_tensors='''pt'''						).to(UpperCamelCase__						)

												# add boolean mask, indicating which patches to mask
												SCREAMING_SNAKE_CASE							:    str							=							hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''    ,		filename='''bool_masked_pos.pt'''						)
												SCREAMING_SNAKE_CASE							:    Dict							=							torch.load(UpperCamelCase__						)

												# forward pass
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model(**UpperCamelCase__						)

												# verify the logits
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.Size([1, 1408, 1536]						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.tensor(
												    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]    ,		device=UpperCamelCase__						)
												self.assertEqual(outputs.logits.shape    ,		UpperCamelCase__						)
												self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,		UpperCamelCase__    ,		atol=1E-4						)						)

												# verify the loss (`config.norm_pix_loss` = `True`)
												SCREAMING_SNAKE_CASE							:    Any							=							torch.tensor([0.5142]    ,		device=UpperCamelCase__						)
												self.assertTrue(torch.allclose(outputs.loss    ,		UpperCamelCase__    ,		atol=1E-4						)						)

												# verify the loss (`config.norm_pix_loss` = `False`)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''    ,		norm_pix_loss=UpperCamelCase__						).to(
												    UpperCamelCase__						)

												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							model(**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.tensor(torch.tensor([0.6469]						)    ,		device=UpperCamelCase__						)
												self.assertTrue(torch.allclose(outputs.loss    ,		UpperCamelCase__    ,		atol=1E-4						)						)






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


__UpperCamelCase		:       Optional[Any]									=     ['gpt2']
__UpperCamelCase		:       str									=     'gpt2'

if is_tf_available():





					class 					lowercase__						(   tf.Module):


											def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





																	'''simple docstring'''





																	super().__init__()
																	SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer
																	SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Dict							=							TFGPTaLMHeadModel.from_config(UpperCamelCase__						)



											@tf.function(input_signature=(tf.TensorSpec((None,)    ,		tf.string    ,		name='''text'''						),)						)
											def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenized['''input_ids'''].to_tensor()

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

																	SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)['''logits''']

																	return outputs





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


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[GPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												SCREAMING_SNAKE_CASE							:    List[str]							=							[TFGPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers						) == len(self.tf_tokenizers						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							[
												    '''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							:    Dict							=							list(zip(self.test_sentences    ,		self.test_sentences[::-1]						)						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for tokenizer, tf_tokenizer in zip(self.tokenizers    ,		self.tf_tokenizers						):
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer([test_inputs]    ,		return_tensors='''tf'''						)
																								SCREAMING_SNAKE_CASE							:    Any							=							tf_tokenizer([test_inputs]						)

																								for key in python_outputs.keys():
																														# convert them to numpy to avoid messing with ragged tensors
																														SCREAMING_SNAKE_CASE							:    int							=							python_outputs[key].numpy()
																														SCREAMING_SNAKE_CASE							:    int							=							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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.function(UpperCamelCase__						)
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.constant(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[str]							=							compiled_tokenizer(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							tf_tokenizer(UpperCamelCase__						)

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


						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    str							=							ModelToSave(tokenizer=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.serving(UpperCamelCase__						)  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''saved.model'''
																								tf.saved_model.save(UpperCamelCase__    ,		UpperCamelCase__    ,		signatures={'''serving_default''': model.serving}						)
																								SCREAMING_SNAKE_CASE							:    str							=							tf.saved_model.load(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    int							=							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        __A       (       self	:				List[str]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__						)  # Build model with some sample inputs

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFGPTaTokenizer.from_config(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							model_from_config(UpperCamelCase__						)

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



						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE							:    Tuple							=							12_3123

																		for max_length in [3, 5, 1024]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__    ,		max_length=UpperCamelCase__						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							out['''input_ids'''].numpy().shape[1]

																								assert out_length == max_length





