


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case	:						Dict         =							logging.get_logger(__name__)

_snake_case	:						Dict         =							{
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    "xmod"

							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    Any=30522       ,      lowerCamelCase			:    Union[str, Any]=768       ,      lowerCamelCase			:    Union[str, Any]=12       ,      lowerCamelCase			:    List[str]=12       ,      lowerCamelCase			:    List[Any]=3072       ,      lowerCamelCase			:    List[Any]="gelu"       ,      lowerCamelCase			:    Union[str, Any]=0.1       ,      lowerCamelCase			:    Dict=0.1       ,      lowerCamelCase			:    Any=512       ,      lowerCamelCase			:    Any=2       ,      lowerCamelCase			:    List[str]=0.02       ,      lowerCamelCase			:    Union[str, Any]=1E-12       ,      lowerCamelCase			:    List[Any]=1       ,      lowerCamelCase			:    Union[str, Any]=0       ,      lowerCamelCase			:    List[str]=2       ,      lowerCamelCase			:    List[str]="absolute"       ,      lowerCamelCase			:    Dict=True       ,      lowerCamelCase			:    List[Any]=None       ,      lowerCamelCase			:    Union[str, Any]=False       ,      lowerCamelCase			:    int=2       ,      lowerCamelCase			:    Union[str, Any]=False       ,      lowerCamelCase			:    List[Any]=True       ,      lowerCamelCase			:    Union[str, Any]=True       ,      lowerCamelCase			:    Optional[Any]=("en_XX",)       ,      lowerCamelCase			:    List[Any]=None       ,      **lowerCamelCase			:    Union[str, Any]       ,      )      ->       Optional[Any]:
														super().__init__(pad_token_id=lowerCamelCase       ,      bos_token_id=lowerCamelCase       ,      eos_token_id=lowerCamelCase       ,      **lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  vocab_size
														__snake_case						:     List[Any]									=  hidden_size
														__snake_case						:     Any									=  num_hidden_layers
														__snake_case						:     Tuple									=  num_attention_heads
														__snake_case						:     List[str]									=  hidden_act
														__snake_case						:     Optional[int]									=  intermediate_size
														__snake_case						:     Optional[int]									=  hidden_dropout_prob
														__snake_case						:     Dict									=  attention_probs_dropout_prob
														__snake_case						:     Any									=  max_position_embeddings
														__snake_case						:     Optional[Any]									=  type_vocab_size
														__snake_case						:     List[Any]									=  initializer_range
														__snake_case						:     List[str]									=  layer_norm_eps
														__snake_case						:     str									=  position_embedding_type
														__snake_case						:     int									=  use_cache
														__snake_case						:     List[str]									=  classifier_dropout
														__snake_case						:     Tuple									=  pre_norm
														__snake_case						:     Any									=  adapter_reduction_factor
														__snake_case						:     str									=  adapter_layer_norm
														__snake_case						:     Optional[Any]									=  adapter_reuse_layer_norm
														__snake_case						:     List[Any]									=  ln_before_adapter
														__snake_case						:     List[str]									=  list(lowerCamelCase							)
														__snake_case						:     int									=  default_language

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							@property
							def        __snake_case   (				self			:    Optional[Any]							)      ->       Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					__snake_case						:     Dict									=  {0: "batch", 1: "choice", 2: "sequence"}
														else:
																					__snake_case						:     Dict									=  {0: "batch", 1: "sequence"}
														return OrderedDict(
														    [
														        ("input_ids", dynamic_axis),
														        ("attention_mask", dynamic_axis),
														    ]							)




"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_=None		,	A_=None					):
	if attention_mask is None:
		lowerCAmelCase__      :		Optional[int]    							=      tf.cast(tf.math.not_equal(A_		,	config.pad_token_id					)		,	tf.inta					)
	return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					lowercase__												=			OPTConfig
					lowercase__												=			{}
					lowercase__												=			"gelu"







					def __init__(			self     :							str  ,lowercase_     :							List[Any]  ,lowercase_     :							Optional[int]=1_3  ,lowercase_     :							str=7  ,lowercase_     :							int=True  ,lowercase_     :							List[str]=False  ,lowercase_     :							Optional[int]=9_9  ,lowercase_     :							Union[str, Any]=1_6  ,lowercase_     :							Any=2  ,lowercase_     :							Tuple=4  ,lowercase_     :							Union[str, Any]=4  ,lowercase_     :							int="gelu"  ,lowercase_     :							Tuple=0.1  ,lowercase_     :							Optional[Any]=0.1  ,lowercase_     :							List[str]=2_0  ,lowercase_     :							Union[str, Any]=2  ,lowercase_     :							Union[str, Any]=1  ,lowercase_     :							Tuple=0  ,lowercase_     :							List[Any]=1_6  ,lowercase_     :							Union[str, Any]=1_6  ,):
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Tuple    							=      batch_size
						lowerCAmelCase__      :		Dict    							=      seq_length
						lowerCAmelCase__      :		Optional[Any]    							=      is_training
						lowerCAmelCase__      :		Optional[Any]    							=      use_labels
						lowerCAmelCase__      :		Union[str, Any]    							=      vocab_size
						lowerCAmelCase__      :		Optional[int]    							=      hidden_size
						lowerCAmelCase__      :		int    							=      num_hidden_layers
						lowerCAmelCase__      :		Dict    							=      num_attention_heads
						lowerCAmelCase__      :		Union[str, Any]    							=      intermediate_size
						lowerCAmelCase__      :		Union[str, Any]    							=      hidden_act
						lowerCAmelCase__      :		int    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Any    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		int    							=      max_position_embeddings
						lowerCAmelCase__      :		Any    							=      eos_token_id
						lowerCAmelCase__      :		List[Any]    							=      pad_token_id
						lowerCAmelCase__      :		Any    							=      bos_token_id
						lowerCAmelCase__      :		Dict    							=      embed_dim
						lowerCAmelCase__      :		int    							=      word_embed_proj_dim
						lowerCAmelCase__      :		Union[str, Any]    							=      False







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Optional[int]    							=      ids_tensor([self.batch_size, self.seq_length - 1]  ,self.vocab_size						)
						lowerCAmelCase__      :		List[Any]    							=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)  ,1						)
						lowerCAmelCase__      :		int    							=      tf.concat([input_ids, eos_tensor]  ,axis=1						)

						lowerCAmelCase__      :		Union[str, Any]    							=      self.config_cls(
						    vocab_size=self.vocab_size  ,hidden_size=self.hidden_size  ,num_hidden_layers=self.num_hidden_layers  ,num_attention_heads=self.num_attention_heads  ,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  ,embed_dim=self.embed_dim  ,word_embed_proj_dim=self.word_embed_proj_dim  ,is_encoder_decoder=lowercase_  ,**self.config_updates  ,)
						lowerCAmelCase__      :		Optional[Any]    							=      prepare_opt_inputs_dict(lowercase_  ,lowercase_						)
						return config, inputs_dict







					def 			__lowerCAmelCase					(			self     :							List[Any]  ,lowercase_     :							int  ,lowercase_     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      TFOPTModel(config=lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      inputs_dict['''input_ids''']

						lowerCAmelCase__      :		Optional[int]    							=      input_ids[:1, :]
						lowerCAmelCase__      :		List[str]    							=      inputs_dict['''attention_mask'''][:1, :]
						lowerCAmelCase__      :		Any    							=      1

						# first forward pass
						lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,attention_mask=lowercase_  ,use_cache=lowercase_						)

						lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						lowerCAmelCase__      :		List[Any]    							=      ids_tensor((self.batch_size, 3)  ,config.vocab_size						)
						lowerCAmelCase__      :		Tuple    							=      tf.cast(ids_tensor((self.batch_size, 3)  ,2						)  ,tf.inta						)

						# append to next input_ids and
						lowerCAmelCase__      :		Optional[Any]    							=      tf.concat([input_ids, next_tokens]  ,axis=-1						)
						lowerCAmelCase__      :		Tuple    							=      tf.concat([attention_mask, next_attn_mask]  ,axis=-1						)

						lowerCAmelCase__      :		Any    							=      model(lowercase_  ,attention_mask=lowercase_						)[0]
						lowerCAmelCase__      :		Any    							=      model(lowercase_  ,attention_mask=lowercase_  ,past_key_values=lowercase_						)[0]

						self.parent.assertEqual(next_tokens.shape[1]  ,output_from_past.shape[1]						)

						# select random slice
						lowerCAmelCase__      :		str    							=      int(ids_tensor((1,)  ,output_from_past.shape[-1]						)						)
						lowerCAmelCase__      :		List[str]    							=      output_from_no_past[:, -3:, random_slice_idx]
						lowerCAmelCase__      :		Union[str, Any]    							=      output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(lowercase_  ,lowercase_  ,rtol=1E-3						)







@require_tf
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
					lowercase__												=			(TFOPTForCausalLM,) if is_tf_available() else ()
					lowercase__												=			(
					    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
					)
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			10







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      TFOPTModelTester(self						)
						lowerCAmelCase__      :		Optional[Any]    							=      ConfigTester(self  ,config_class=lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Any    							=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

						def _get_word_embedding_weight(lowercase_     :							Optional[Any]  ,lowercase_     :							List[Any]						):
							if hasattr(lowercase_  ,'''weight'''						):
								return embedding_layer.weight
							else:
								# Here we build the word embeddings weights if not exists.
								# And then we retry to get the attribute once built.
								model.build()
								if hasattr(lowercase_  ,'''weight'''						):
									return embedding_layer.weight
								else:
									return None

						for model_class in self.all_model_classes:
							for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
								# build the embeddings
								lowerCAmelCase__      :		Optional[int]    							=      model_class(config=lowercase_						)
								lowerCAmelCase__      :		List[Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_input_embeddings()						)
								lowerCAmelCase__      :		Optional[Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_output_embeddings()						)

								# reshape the embeddings
								model.resize_token_embeddings(lowercase_						)
								lowerCAmelCase__      :		Union[str, Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_input_embeddings()						)
								lowerCAmelCase__      :		List[Any]    							=      _get_word_embedding_weight(lowercase_  ,model.get_output_embeddings()						)

								# check that the resized embeddings size matches the desired size.
								lowerCAmelCase__      :		Optional[Any]    							=      size if size is not None else config.vocab_size

								self.assertEqual(new_input_embeddings.shape[0]  ,lowercase_						)

								# check that weights remain the same after resizing
								lowerCAmelCase__      :		Optional[Any]    							=      True
								for pa, pa in zip(old_input_embeddings.value()  ,new_input_embeddings.value()						):
									if tf.math.reduce_sum(tf.math.abs(pa - pa						)						) > 0:
										lowerCAmelCase__      :		str    							=      False
								self.assertTrue(lowercase_						)

								if old_output_embeddings is not None and new_output_embeddings is not None:
									self.assertEqual(new_output_embeddings.shape[0]  ,lowercase_						)

									lowerCAmelCase__      :		Dict    							=      True
									for pa, pa in zip(old_output_embeddings.value()  ,new_output_embeddings.value()						):
										if tf.math.reduce_sum(tf.math.abs(pa - pa						)						) > 0:
											lowerCAmelCase__      :		List[Any]    							=      False
									self.assertTrue(lowercase_						)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	return tf.constant(A_		,	dtype=tf.intaa					)







@require_tf
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			99







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Dict    							=      tf.ones((4, 1)  ,dtype=tf.intaa						) * 2
						lowerCAmelCase__      :		Any    							=      tf.concat([ids_tensor((4, 6)  ,self.vocab_size - 3						) + 3, eos_column_vector]  ,axis=1						)
						lowerCAmelCase__      :		str    							=      input_ids.shape[0]
						lowerCAmelCase__      :		str    							=      OPTConfig(
						    vocab_size=self.vocab_size  ,hidden_size=2_4  ,num_hidden_layers=2  ,num_attention_heads=2  ,ffn_dim=3_2  ,max_position_embeddings=4_8  ,eos_token_id=2  ,pad_token_id=1  ,bos_token_id=0  ,)
						return config, input_ids, batch_size







@require_sentencepiece
@require_tf
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@slow
					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Tuple    							=      TFOPTModel.from_pretrained('''facebook/opt-350m'''						)
						lowerCAmelCase__      :		str    							=      _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
						lowerCAmelCase__      :		List[Any]    							=      tf.not_equal(lowercase_  ,model.config.pad_token_id						)
						with tf.GradientTape():
							lowerCAmelCase__      :		List[str]    							=      model(input_ids=lowercase_  ,attention_mask=lowercase_						).last_hidden_state
						lowerCAmelCase__      :		Optional[int]    							=      (1, 1_1, 5_1_2)
						self.assertEqual(output.shape  ,lowercase_						)
						lowerCAmelCase__      :		Any    							=      tf.constant(
						    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]						)
						self.assertTrue(np.allclose(output[:, :3, :3]  ,lowercase_  ,atol=4E-3						)						)

						lowerCAmelCase__      :		Any    							=      tf.function(lowercase_  ,jit_compile=lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      xla_generate(lowercase_  ,lowercase_						)[0]
						self.assertTrue(np.allclose(output[:, :3, :3]  ,lowercase_  ,atol=4E-2						)						)







@require_tf
@slow
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						super().setUp()
						lowerCAmelCase__      :		Optional[int]    							=      '''facebook/opt-350m'''







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Optional[int]    							=      TFOPTForCausalLM.from_pretrained(self.path_model						)
						lowerCAmelCase__      :		Union[str, Any]    							=      GPTaTokenizer.from_pretrained(self.path_model						)

						lowerCAmelCase__      :		int    							=      [
						    '''Today is a beautiful day and I want to''',
						    '''In the city of''',
						    '''Paris is the capital of France and''',
						    '''Computers and mobile phones have taken''',
						]
						# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
						lowerCAmelCase__      :		Optional[Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''  ,padding=lowercase_  ,add_special_tokens=lowercase_						)
						lowerCAmelCase__      :		int    							=      tf.math.reduce_mean(model(inputs.input_ids  ,attention_mask=inputs.attention_mask						)[0]  ,axis=-1						)
						lowerCAmelCase__      :		List[str]    							=      tf.constant(
						    [
						        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
						        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
						        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
						        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
						    ]						)
						self.assertTrue(np.allclose(lowercase_  ,lowercase_  ,atol=1E-4						)						)

						lowerCAmelCase__      :		int    							=      tf.function(lowercase_  ,jit_compile=lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      tf.math.reduce_mean(xla_generate(inputs.input_ids  ,attention_mask=inputs.attention_mask						)[0]  ,axis=-1						)
						self.assertTrue(np.allclose(lowercase_  ,lowercase_  ,atol=1E-4						)						)







@require_tf
@slow
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return [
						    "Today is a beautiful day and I want",
						    "In the city of",
						    "Paris is the capital of France and",
						    "Computers and mobile phones have taken",
						]







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Union[str, Any]    							=      '''facebook/opt-125m'''

						lowerCAmelCase__      :		str    							=      [
						    '''Today is a beautiful day and I want to''',
						    '''In the city of New York, the city''',
						    '''Paris is the capital of France and the capital''',
						    '''Computers and mobile phones have taken over the''',
						]

						lowerCAmelCase__      :		Any    							=      []
						lowerCAmelCase__      :		int    							=      GPTaTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		int    							=      TFOPTForCausalLM.from_pretrained(lowercase_						)

						for prompt in self.prompts:
							lowerCAmelCase__      :		Union[str, Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''						).input_ids

							lowerCAmelCase__      :		Any    							=      model.generate(lowercase_  ,max_length=1_0						)

							lowerCAmelCase__      :		Optional[int]    							=      tokenizer.batch_decode(lowercase_  ,skip_special_tokens=lowercase_						)
							predicted_outputs += generated_string

						self.assertListEqual(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Union[str, Any]    							=      '''facebook/opt-350m'''

						lowerCAmelCase__      :		Optional[Any]    							=      GPTaTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      TFOPTForCausalLM.from_pretrained(lowercase_						)

						lowerCAmelCase__      :		Optional[Any]    							=      '''left'''

						# use different length sentences to test batching
						lowerCAmelCase__      :		Dict    							=      [
						    '''Hello, my dog is a little''',
						    '''Today, I''',
						]

						lowerCAmelCase__      :		Union[str, Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''  ,padding=lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      inputs['''input_ids''']

						lowerCAmelCase__      :		str    							=      model.generate(input_ids=lowercase_  ,attention_mask=inputs['''attention_mask''']						)

						lowerCAmelCase__      :		Optional[Any]    							=      tokenizer(sentences[0]  ,return_tensors='''tf'''						).input_ids
						lowerCAmelCase__      :		Optional[Any]    							=      model.generate(input_ids=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      inputs_non_padded.shape[-1] - tf.math.reduce_sum(
						    tf.cast(inputs['''attention_mask'''][-1]  ,tf.intaa						)						)
						lowerCAmelCase__      :		Any    							=      tokenizer(sentences[1]  ,return_tensors='''tf'''						).input_ids
						lowerCAmelCase__      :		Optional[int]    							=      model.generate(input_ids=lowercase_  ,max_length=model.config.max_length - num_paddings						)

						lowerCAmelCase__      :		Any    							=      tokenizer.batch_decode(lowercase_  ,skip_special_tokens=lowercase_						)
						lowerCAmelCase__      :		List[Any]    							=      tokenizer.decode(output_non_padded[0]  ,skip_special_tokens=lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      tokenizer.decode(output_padded[0]  ,skip_special_tokens=lowercase_						)

						lowerCAmelCase__      :		List[Any]    							=      [
						    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
						    '''Today, I was in the middle of a conversation with a friend about the''',
						]
						self.assertListEqual(lowercase_  ,lowercase_						)
						self.assertListEqual(lowercase_  ,[non_padded_sentence, padded_sentence]						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		List[Any]    							=      '''facebook/opt-350m'''

						lowerCAmelCase__      :		Tuple    							=      [
						    '''Today is a beautiful day and I want to''',
						    '''In the city of San Francisco, the city''',
						    '''Paris is the capital of France and the capital''',
						    '''Computers and mobile phones have taken over the''',
						]

						lowerCAmelCase__      :		int    							=      []
						lowerCAmelCase__      :		int    							=      GPTaTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		List[str]    							=      TFOPTForCausalLM.from_pretrained(lowercase_						)

						for prompt in self.prompts:
							lowerCAmelCase__      :		Optional[Any]    							=      tokenizer(lowercase_  ,return_tensors='''tf'''						).input_ids

							lowerCAmelCase__      :		Optional[Any]    							=      model.generate(lowercase_  ,max_length=1_0						)

							lowerCAmelCase__      :		str    							=      tokenizer.batch_decode(lowercase_  ,skip_special_tokens=lowercase_						)
							predicted_outputs += generated_string

						self.assertListEqual(lowercase_  ,lowercase_						)




