"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				_UpperCamelCase								=		"platform"

				import jax
				import jax.numpy as jnp

				from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    shift_tokens_right,
				)

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  ):


							if attention_mask is None:
														A__       :     int								=			np.where(input_ids != config.pad_token_id			,  1			,  0							)
							if decoder_attention_mask is None:
														A__       :     int								=			np.where(decoder_input_ids != config.pad_token_id			,  1			,  0							)
							if head_mask is None:
														A__       :     Union[str, Any]								=			np.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														A__       :     Optional[int]								=			np.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														A__       :     str								=			np.ones((config.decoder_layers, config.decoder_attention_heads)							)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=13   ,      snake_case_=7   ,      snake_case_=True   ,      snake_case_=False   ,      snake_case_=99   ,      snake_case_=16   ,      snake_case_=2   ,      snake_case_=4   ,      snake_case_=4   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=32   ,      snake_case_=2   ,      snake_case_=1   ,      snake_case_=0   ,      snake_case_=0.02   ,      ):







										'''simple docstring'''


										A__       :     Optional[Any]								=			parent
										A__       :     Any								=			batch_size
										A__       :     List[Any]								=			seq_length
										A__       :     Dict								=			is_training
										A__       :     Dict								=			use_labels
										A__       :     Any								=			vocab_size
										A__       :     str								=			hidden_size
										A__       :     str								=			num_hidden_layers
										A__       :     Any								=			num_attention_heads
										A__       :     Optional[int]								=			intermediate_size
										A__       :     Dict								=			hidden_act
										A__       :     int								=			hidden_dropout_prob
										A__       :     int								=			attention_probs_dropout_prob
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     List[str]								=			eos_token_id
										A__       :     str								=			pad_token_id
										A__       :     Tuple								=			bos_token_id
										A__       :     int								=			initializer_range





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,      self.vocab_size							)   ,      3   ,      self.vocab_size							)
										A__       :     List[str]								=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,      dtype=np.intaa							))   ,      -1							)

										A__       :     List[str]								=			shift_tokens_right(__UpperCAmelCase   ,      1   ,      2							)

										A__       :     str								=			BlenderbotSmallConfig(
										    vocab_size=self.vocab_size   ,      d_model=self.hidden_size   ,      encoder_layers=self.num_hidden_layers   ,      decoder_layers=self.num_hidden_layers   ,      encoder_attention_heads=self.num_attention_heads   ,      decoder_attention_heads=self.num_attention_heads   ,      encoder_ffn_dim=self.intermediate_size   ,      decoder_ffn_dim=self.intermediate_size   ,      dropout=self.hidden_dropout_prob   ,      attention_dropout=self.attention_probs_dropout_prob   ,      max_position_embeddings=self.max_position_embeddings   ,      eos_token_id=self.eos_token_id   ,      bos_token_id=self.bos_token_id   ,      pad_token_id=self.pad_token_id   ,      initializer_range=self.initializer_range   ,      use_cache=__UpperCAmelCase   ,      )
										A__       :     Tuple								=			prepare_blenderbot_inputs_dict(__UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)
										return config, inputs_dict





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Optional[Any]								=			self.prepare_config_and_inputs()
										return config, inputs_dict





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			20
										A__       :     Dict								=			model_class_name(__UpperCAmelCase							)

										A__       :     Optional[Any]								=			model.encode(inputs_dict["""input_ids"""]							)

										A__				,							A__       :     Tuple								=			(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

										A__       :     Optional[Any]								=			model.init_cache(decoder_input_ids.shape[0]   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)
										A__       :     List[str]								=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,      dtype="""i4"""							)

										A__       :     Dict								=			jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )
										A__       :     Tuple								=			model.decode(
										    decoder_input_ids[:, :-1]   ,      __UpperCAmelCase   ,      decoder_attention_mask=__UpperCAmelCase   ,      past_key_values=__UpperCAmelCase   ,      decoder_position_ids=__UpperCAmelCase   ,      )

										A__       :     Dict								=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,      dtype="""i4"""							)
										A__       :     Optional[int]								=			model.decode(
										    decoder_input_ids[:, -1:]   ,      __UpperCAmelCase   ,      decoder_attention_mask=__UpperCAmelCase   ,      past_key_values=outputs_cache.past_key_values   ,      decoder_position_ids=__UpperCAmelCase   ,      )

										A__       :     Optional[Any]								=			model.decode(__UpperCAmelCase   ,      __UpperCAmelCase							)

										A__       :     int								=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
										self.parent.assertTrue(diff < 1E-3   ,      msg=F'''Max diff is {diff}'''							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			20
										A__       :     str								=			model_class_name(__UpperCAmelCase							)

										A__       :     List[Any]								=			model.encode(inputs_dict["""input_ids"""]							)

										A__				,							A__       :     str								=			(
										    inputs_dict["""decoder_input_ids"""],
										    inputs_dict["""decoder_attention_mask"""],
										)

										A__       :     int								=			jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
										    ]   ,      axis=-1   ,      )

										A__       :     Dict								=			model.init_cache(decoder_input_ids.shape[0]   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)
										A__       :     Dict								=			jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )

										A__       :     str								=			model.decode(
										    decoder_input_ids[:, :-1]   ,      __UpperCAmelCase   ,      decoder_attention_mask=__UpperCAmelCase   ,      past_key_values=__UpperCAmelCase   ,      decoder_position_ids=__UpperCAmelCase   ,      )
										A__       :     Any								=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,      dtype="""i4"""							)
										A__       :     List[Any]								=			model.decode(
										    decoder_input_ids[:, -1:]   ,      __UpperCAmelCase   ,      past_key_values=outputs_cache.past_key_values   ,      decoder_attention_mask=__UpperCAmelCase   ,      decoder_position_ids=__UpperCAmelCase   ,      )

										A__       :     Optional[int]								=			model.decode(__UpperCAmelCase   ,      __UpperCAmelCase   ,      decoder_attention_mask=__UpperCAmelCase							)

										A__       :     Any								=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
										self.parent.assertTrue(diff < 1E-3   ,      msg=F'''Max diff is {diff}'''							)





@require_flax
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				99





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			np.array(
										    [
										        [71, 82, 18, 33, 46, 91, 2],
										        [68, 34, 26, 58, 30, 82, 2],
										        [5, 97, 17, 39, 94, 40, 2],
										        [76, 83, 94, 25, 70, 78, 2],
										        [87, 59, 41, 35, 48, 66, 2],
										        [55, 13, 16, 58, 5, 2, 1],  # note padding
										        [64, 27, 31, 51, 12, 75, 2],
										        [52, 64, 86, 17, 83, 39, 2],
										        [48, 61, 9, 24, 71, 82, 2],
										        [26, 1, 60, 48, 22, 13, 2],
										        [21, 5, 62, 28, 14, 76, 2],
										        [45, 98, 37, 86, 59, 48, 2],
										        [70, 70, 50, 9, 28, 0, 2],
										    ]   ,      dtype=np.intaa   ,      )

										A__       :     List[str]								=			input_ids.shape[0]
										A__       :     int								=			BlenderbotSmallConfig(
										    vocab_size=self.vocab_size   ,      d_model=24   ,      encoder_layers=2   ,      decoder_layers=2   ,      encoder_attention_heads=2   ,      decoder_attention_heads=2   ,      encoder_ffn_dim=32   ,      decoder_ffn_dim=32   ,      max_position_embeddings=48   ,      eos_token_id=2   ,      pad_token_id=1   ,      bos_token_id=0   ,      )
										return config, input_ids, batch_size





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__				,							A__       :     Any								=			self._get_config_and_data()
										A__       :     Optional[int]								=			FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
										A__       :     Dict								=			lm_model(input_ids=__UpperCAmelCase							)
										A__       :     Union[str, Any]								=			(batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["""logits"""].shape   ,      __UpperCAmelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			BlenderbotSmallConfig(
										    vocab_size=self.vocab_size   ,      d_model=14   ,      encoder_layers=2   ,      decoder_layers=2   ,      encoder_attention_heads=2   ,      decoder_attention_heads=2   ,      encoder_ffn_dim=8   ,      decoder_ffn_dim=8   ,      max_position_embeddings=48   ,      )
										A__       :     str								=			FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase							)
										A__       :     Optional[int]								=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,      dtype=np.intaa							)
										A__       :     Union[str, Any]								=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,      dtype=np.intaa							)
										A__       :     Any								=			lm_model(input_ids=__UpperCAmelCase   ,      decoder_input_ids=__UpperCAmelCase							)
										A__       :     List[str]								=			(*summary.shape, config.vocab_size)
										self.assertEqual(outputs["""logits"""].shape   ,      __UpperCAmelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,      dtype=np.intaa							)
										A__       :     List[Any]								=			shift_tokens_right(__UpperCAmelCase   ,      1   ,      2							)
										A__       :     str								=			np.equal(__UpperCAmelCase   ,      1							).astype(np.floataa							).sum()
										A__       :     int								=			np.equal(__UpperCAmelCase   ,      1							).astype(np.floataa							).sum()
										self.assertEqual(shifted.shape   ,      input_ids.shape							)
										self.assertEqual(__UpperCAmelCase   ,      n_pad_before - 1							)
										self.assertTrue(np.equal(shifted[:, 0]   ,      2							).all()							)





@require_flax
class 						__UpperCAmelCase    (A__							,   unittest.TestCase							,   A__     ):







			'''simple docstring'''
			_UpperCamelCase  :						Any					     =				True
			_UpperCamelCase  :						Any					     =				(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			_UpperCamelCase  :						Optional[int]					     =				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			FlaxBlenderbotSmallModelTester(self							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(__UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     List[str]								=			self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__							):
																								A__       :     Union[str, Any]								=			self._prepare_for_class(__UpperCAmelCase   ,      __UpperCAmelCase							)
																								A__       :     Optional[int]								=			model_class(__UpperCAmelCase							)

																								@jax.jit
																								def encode_jitted(snake_case_   ,      snake_case_=None   ,      **snake_case_							):
																															return model.encode(input_ids=__UpperCAmelCase   ,      attention_mask=__UpperCAmelCase							)

																								with self.subTest("""JIT Enabled"""							):
																															A__       :     List[Any]								=			encode_jitted(**__UpperCAmelCase							).to_tuple()

																								with self.subTest("""JIT Disabled"""							):
																															with jax.disable_jit():
																																						A__       :     Any								=			encode_jitted(**__UpperCAmelCase							).to_tuple()

																								self.assertEqual(len(__UpperCAmelCase							)   ,      len(__UpperCAmelCase							)							)
																								for jitted_output, output in zip(__UpperCAmelCase   ,      __UpperCAmelCase							):
																															self.assertEqual(jitted_output.shape   ,      output.shape							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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

										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__							):
																								A__       :     Optional[int]								=			model_class(__UpperCAmelCase							)
																								A__       :     Optional[int]								=			model.encode(inputs_dict["""input_ids"""]   ,      inputs_dict["""attention_mask"""]							)

																								A__       :     Tuple								=			{
																								    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																								    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																								    """encoder_outputs""": encoder_outputs,
																								}

																								@jax.jit
																								def decode_jitted(snake_case_   ,      snake_case_   ,      snake_case_							):
																															return model.decode(
																															    decoder_input_ids=__UpperCAmelCase   ,      decoder_attention_mask=__UpperCAmelCase   ,      encoder_outputs=__UpperCAmelCase   ,      )

																								with self.subTest("""JIT Enabled"""							):
																															A__       :     Union[str, Any]								=			decode_jitted(**__UpperCAmelCase							).to_tuple()

																								with self.subTest("""JIT Disabled"""							):
																															with jax.disable_jit():
																																						A__       :     int								=			decode_jitted(**__UpperCAmelCase							).to_tuple()

																								self.assertEqual(len(__UpperCAmelCase							)   ,      len(__UpperCAmelCase							)							)
																								for jitted_output, output in zip(__UpperCAmelCase   ,      __UpperCAmelCase							):
																															self.assertEqual(jitted_output.shape   ,      output.shape							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_class_name in self.all_model_classes:
																	A__       :     int								=			model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""							)
																	# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																	A__       :     Union[str, Any]								=			np.ones((1, 1)							) * model.config.eos_token_id
																	A__       :     Union[str, Any]								=			model(__UpperCAmelCase							)
																	self.assertIsNotNone(__UpperCAmelCase							)








"""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


