

"""simple docstring"""




import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import BertGenerationDecoder, BertGenerationEncoder





class  lowerCamelCase    :

			def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Dict=1_3     ,					__UpperCAmelCase					:    List[Any]=7     ,					__UpperCAmelCase					:    str=True     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    List[Any]=9_9     ,					__UpperCAmelCase					:    Optional[int]=3_2     ,					__UpperCAmelCase					:    str=5     ,					__UpperCAmelCase					:    Union[str, Any]=4     ,					__UpperCAmelCase					:    Union[str, Any]=3_7     ,					__UpperCAmelCase					:    Union[str, Any]="gelu"     ,					__UpperCAmelCase					:    List[Any]=0.1     ,					__UpperCAmelCase					:    Tuple=0.1     ,					__UpperCAmelCase					:    Tuple=5_0     ,					__UpperCAmelCase					:    Dict=0.02     ,					__UpperCAmelCase					:    int=True     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   seq_length
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_input_mask
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   hidden_size
						SCREAMING_SNAKE_CASE__										=   num_hidden_layers
						SCREAMING_SNAKE_CASE__										=   num_attention_heads
						SCREAMING_SNAKE_CASE__										=   intermediate_size
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   use_labels
						SCREAMING_SNAKE_CASE__										=   scope

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size							)

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

						if self.use_labels:
									SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size							)

						SCREAMING_SNAKE_CASE__										=   self.get_config()

						return config, input_ids, input_mask, token_labels

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Dict:
						return BertGenerationConfig(
						    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     ,					is_decoder=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Union[str, Any]:
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   self.prepare_config_and_inputs()

						SCREAMING_SNAKE_CASE__										=   True
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
						SCREAMING_SNAKE_CASE__										=   ids_tensor([self.batch_size, self.seq_length]     ,					vocab_size=2							)

						return (
						    config,
						    input_ids,
						    input_mask,
						    token_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    int     ,					**__UpperCAmelCase					:    Dict     ,					) ->     str:
						SCREAMING_SNAKE_CASE__										=   BertGenerationEncoder(config=__UpperCAmelCase							)
						model.to(__UpperCAmelCase							)
						model.eval()
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    List[Any]     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Any     ,					**__UpperCAmelCase					:    str     ,					) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   True
						SCREAMING_SNAKE_CASE__										=   BertGenerationEncoder(config=__UpperCAmelCase							)
						model.to(__UpperCAmelCase							)
						model.eval()
						SCREAMING_SNAKE_CASE__										=   model(
						    __UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase     ,					encoder_attention_mask=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   model(
						    __UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase     ,					)
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					**__UpperCAmelCase					:    List[str]     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   True
						SCREAMING_SNAKE_CASE__										=   True
						SCREAMING_SNAKE_CASE__										=   BertGenerationDecoder(config=__UpperCAmelCase							).to(__UpperCAmelCase							).eval()

						# first forward pass
						SCREAMING_SNAKE_CASE__										=   model(
						    __UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase     ,					encoder_attention_mask=__UpperCAmelCase     ,					use_cache=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						SCREAMING_SNAKE_CASE__										=   ids_tensor((self.batch_size, 3)     ,					config.vocab_size							)
						SCREAMING_SNAKE_CASE__										=   ids_tensor((self.batch_size, 3)     ,					vocab_size=2							)

						# append to next input_ids and
						SCREAMING_SNAKE_CASE__										=   torch.cat([input_ids, next_tokens]     ,					dim=-1							)
						SCREAMING_SNAKE_CASE__										=   torch.cat([input_mask, next_mask]     ,					dim=-1							)

						SCREAMING_SNAKE_CASE__										=   model(
						    __UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase     ,					encoder_attention_mask=__UpperCAmelCase     ,					output_hidden_states=__UpperCAmelCase     ,					)["""hidden_states"""][0]
						SCREAMING_SNAKE_CASE__										=   model(
						    __UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					encoder_hidden_states=__UpperCAmelCase     ,					encoder_attention_mask=__UpperCAmelCase     ,					past_key_values=__UpperCAmelCase     ,					output_hidden_states=__UpperCAmelCase     ,					)["""hidden_states"""][0]

						# select random slice
						SCREAMING_SNAKE_CASE__										=   ids_tensor((1,)     ,					output_from_past.shape[-1]							).item()
						SCREAMING_SNAKE_CASE__										=   output_from_no_past[:, -3:, random_slice_idx].detach()
						SCREAMING_SNAKE_CASE__										=   output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(__UpperCAmelCase     ,					__UpperCAmelCase     ,					atol=1e-3							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    List[str]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    int     ,					*__UpperCAmelCase					:    Dict     ,					) ->     str:
						SCREAMING_SNAKE_CASE__										=   BertGenerationDecoder(__UpperCAmelCase							)
						model.to(__UpperCAmelCase							)
						model.eval()
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					labels=__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.vocab_size)							)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__										=   {"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict





@require_torch
class  lowerCamelCase    (A__      ,A__      ,A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Dict       		=				(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
			lowerCamelCase__ :		Optional[int]       		=				(BertGenerationDecoder,) if is_torch_available() else ()
			lowerCamelCase__ :		Optional[Any]       		=				(
			    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
			    if is_torch_available()
			    else {}
			)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   BertGenerationEncoderTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					hidden_size=3_7							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Dict:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     int:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__										=   """bert"""
						self.model_tester.create_and_check_model(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Tuple:
						# This regression test was failing with PyTorch < 1.3
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   self.model_tester.prepare_config_and_inputs_for_decoder()

						SCREAMING_SNAKE_CASE__										=   None

						self.model_tester.create_and_check_model_as_decoder(
						    __UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     int:
						SCREAMING_SNAKE_CASE__										=   BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""							)
						self.assertIsNotNone(__UpperCAmelCase							)





@require_torch
class  lowerCamelCase    (unittest.TestCase      ):

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]							)
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)[0]
						SCREAMING_SNAKE_CASE__										=   torch.Size([1, 8, 1_0_2_4]							)
						self.assertEqual(output.shape     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]							)
						self.assertTrue(torch.allclose(output[:, :3, :3]     ,					__UpperCAmelCase     ,					atol=1e-4							)							)





@require_torch
class  lowerCamelCase    (unittest.TestCase      ):

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]							)
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)[0]
						SCREAMING_SNAKE_CASE__										=   torch.Size([1, 8, 5_0_3_5_8]							)
						self.assertEqual(output.shape     ,					__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]							)
						self.assertTrue(torch.allclose(output[:, :3, :3]     ,					__UpperCAmelCase     ,					atol=1e-4							)							)





"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

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


if is_tf_available():
						import tensorflow as tf

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


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

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

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





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

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

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

												return outputs





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

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

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

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

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

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

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

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

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

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

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

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

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





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

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

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

												assert out_length == max_length


