import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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		:       Optional[Any]									=     'platform'

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)
def       A	(    _lowercase		,  _lowercase		,  _lowercase=None		,  _lowercase=None		,  _lowercase=None		,  _lowercase=None		,  _lowercase=None		,  _lowercase=None		,  ):
						if attention_mask is None:
												SCREAMING_SNAKE_CASE							:    Dict							=							np.where(input_ids != config.pad_token_id		,  1		,  0						)
						if decoder_attention_mask is None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							np.where(decoder_input_ids != config.pad_token_id		,  1		,  0						)
						if head_mask is None:
												SCREAMING_SNAKE_CASE							:    List[str]							=							np.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							np.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							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 					lowercase__						:


						def __init__(       self	:				List[str]    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				Union[str, Any]=13    ,		UpperCamelCase__	:				Any=7    ,		UpperCamelCase__	:				Any=True    ,		UpperCamelCase__	:				Tuple=False    ,		UpperCamelCase__	:				int=99    ,		UpperCamelCase__	:				Optional[Any]=16    ,		UpperCamelCase__	:				int=2    ,		UpperCamelCase__	:				Tuple=4    ,		UpperCamelCase__	:				List[Any]=4    ,		UpperCamelCase__	:				Any="gelu"    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				Optional[Any]=0.1    ,		UpperCamelCase__	:				List[Any]=32    ,		UpperCamelCase__	:				Tuple=2    ,		UpperCamelCase__	:				Union[str, Any]=1    ,		UpperCamelCase__	:				Optional[int]=0    ,		UpperCamelCase__	:				Union[str, Any]=0.02    ,		):





												'''simple docstring'''





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


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Dict							=							shift_tokens_right(UpperCamelCase__    ,		1    ,		2						)

												SCREAMING_SNAKE_CASE							:    Any							=							BlenderbotConfig(
												    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__    ,		)
												SCREAMING_SNAKE_CASE							:    int							=							prepare_blenderbot_inputs_dict(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												return config, inputs_dict


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.prepare_config_and_inputs()
												return config, inputs_dict


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							20
												SCREAMING_SNAKE_CASE							:    List[str]							=							model_class_name(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							model.encode(inputs_dict['''input_ids''']						)

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model.init_cache(decoder_input_ids.shape[0]    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    str							=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,		dtype='''i4'''						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							model.decode(
												    decoder_input_ids[:, :-1]    ,		UpperCamelCase__    ,		decoder_attention_mask=UpperCamelCase__    ,		past_key_values=UpperCamelCase__    ,		decoder_position_ids=UpperCamelCase__    ,		)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype='''i4'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							model.decode(
												    decoder_input_ids[:, -1:]    ,		UpperCamelCase__    ,		decoder_attention_mask=UpperCamelCase__    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=UpperCamelCase__    ,		)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.decode(UpperCamelCase__    ,		UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							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        __A       (       self	:				Dict    ,		UpperCamelCase__	:				Dict    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							20
												SCREAMING_SNAKE_CASE							:    str							=							model_class_name(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Any							=							model.encode(inputs_dict['''input_ids''']						)

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

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

												SCREAMING_SNAKE_CASE							:    Any							=							model.init_cache(decoder_input_ids.shape[0]    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)

												SCREAMING_SNAKE_CASE							:    int							=							model.decode(
												    decoder_input_ids[:, :-1]    ,		UpperCamelCase__    ,		decoder_attention_mask=UpperCamelCase__    ,		past_key_values=UpperCamelCase__    ,		decoder_position_ids=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype='''i4'''						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							model.decode(
												    decoder_input_ids[:, -1:]    ,		UpperCamelCase__    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=UpperCamelCase__    ,		decoder_position_ids=UpperCamelCase__    ,		)

												SCREAMING_SNAKE_CASE							:    int							=							model.decode(UpperCamelCase__    ,		UpperCamelCase__    ,		decoder_attention_mask=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    List[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 					lowercase__						(   unittest.TestCase):
						UpperCamelCase_     =		99


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							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    ,		)

												SCREAMING_SNAKE_CASE							:    Dict							=							input_ids.shape[0]
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							BlenderbotConfig(
												    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        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[int]							=							self._get_config_and_data()
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							FlaxBlenderbotForConditionalGeneration(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							lm_model(input_ids=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							(batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['''logits'''].shape    ,		UpperCamelCase__						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							BlenderbotConfig(
												    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    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							FlaxBlenderbotForConditionalGeneration(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Any							=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,		dtype=np.intaa						)
												SCREAMING_SNAKE_CASE							:    Dict							=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,		dtype=np.intaa						)
												SCREAMING_SNAKE_CASE							:    Any							=							lm_model(input_ids=UpperCamelCase__    ,		decoder_input_ids=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(*summary.shape, config.vocab_size)
												self.assertEqual(outputs['''logits'''].shape    ,		UpperCamelCase__						)



						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,		dtype=np.intaa						)
												SCREAMING_SNAKE_CASE							:    int							=							shift_tokens_right(UpperCamelCase__    ,		1    ,		2						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							np.equal(UpperCamelCase__    ,		1						).astype(np.floataa						).sum()
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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 					lowercase__						(   UpperCamelCase_	,							unittest.TestCase	,							UpperCamelCase_):
						UpperCamelCase_     =		True
						UpperCamelCase_     =		(
						    (
						        FlaxBlenderbotModel,
						        FlaxBlenderbotForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						UpperCamelCase_     =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							FlaxBlenderbotModelTester(self						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							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        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Any							=							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        __A       (       self	:				Optional[Any]						):





												'''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:
																		with self.subTest(model_class.__name__						):
																								SCREAMING_SNAKE_CASE							:    Dict							=							self._prepare_for_class(UpperCamelCase__    ,		UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Dict							=							model_class(UpperCamelCase__						)

																								@jax.jit
																								def encode_jitted(UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[Any]=None    ,		**UpperCamelCase__	:				Optional[int]						):
																														return model.encode(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)

																								with self.subTest('''JIT Enabled'''						):
																														SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							encode_jitted(**UpperCamelCase__						).to_tuple()

																								with self.subTest('''JIT Disabled'''						):
																														with jax.disable_jit():
																																				SCREAMING_SNAKE_CASE							:    str							=							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        __A       (       self	:				Dict						):





												'''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:
																		with self.subTest(model_class.__name__						):
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							model_class(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.encode(inputs_dict['''input_ids''']    ,		inputs_dict['''attention_mask''']						)

																								SCREAMING_SNAKE_CASE							:    Dict							=							{
																								    '''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(UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				str						):
																														return model.decode(
																														    decoder_input_ids=UpperCamelCase__    ,		decoder_attention_mask=UpperCamelCase__    ,		encoder_outputs=UpperCamelCase__    ,		)

																								with self.subTest('''JIT Enabled'''						):
																														SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							decode_jitted(**UpperCamelCase__						).to_tuple()

																								with self.subTest('''JIT Disabled'''						):
																														with jax.disable_jit():
																																				SCREAMING_SNAKE_CASE							:    Tuple							=							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        __A       (       self	:				Dict						):





												'''simple docstring'''





												for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''						)
																		# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							np.ones((1, 1)						) * model.config.eos_token_id
																		SCREAMING_SNAKE_CASE							:    Tuple							=							model(UpperCamelCase__						)
																		self.assertIsNotNone(UpperCamelCase__						)



						@unittest.skipUnless(jax_device != '''cpu'''    ,		'''3B test too slow on CPU.'''						)
						@slow
						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
												SCREAMING_SNAKE_CASE							:    List[Any]							=							{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

												SCREAMING_SNAKE_CASE							:    List[str]							=							FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,		from_pt=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''						)

												SCREAMING_SNAKE_CASE							:    int							=							['''Sam''']
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tokenizer(UpperCamelCase__    ,		return_tensors='''jax'''						)

												SCREAMING_SNAKE_CASE							:    Dict							=							model.generate(**UpperCamelCase__    ,		**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    str							=							'''Sam is a great name. It means "sun" in Gaelic.'''

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer.batch_decode(UpperCamelCase__    ,		**UpperCamelCase__						)
												assert generated_txt[0].strip() == tgt_text






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





