

"""simple docstring"""





import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Tuple    							=      np.inf

	def set_batch_size(A_					) -> None:
		nonlocal batch_size
		if isinstance(A_		,	A_					):
			lowerCAmelCase__      :		str    							=      min(A_		,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
		elif isinstance(A_		,	A_					):
			lowerCAmelCase__      :		Optional[Any]    							=      min(A_		,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
		elif isinstance(A_		,	A_					) and feature.dtype == "binary":
			lowerCAmelCase__      :		int    							=      min(A_		,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

	_visit(A_		,	A_					)

	return None if batch_size is np.inf else batch_size







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Dict  ,lowercase_     :							NestedDataStructureLike[PathLike]  ,lowercase_     :							Optional[NamedSplit] = None  ,lowercase_     :							Optional[Features] = None  ,lowercase_     :							str = None  ,lowercase_     :							bool = False  ,lowercase_     :							bool = False  ,lowercase_     :							Optional[int] = None  ,**lowercase_     :							List[Any]  ,):
						super().__init__(
						    lowercase_  ,split=lowercase_  ,features=lowercase_  ,cache_dir=lowercase_  ,keep_in_memory=lowercase_  ,streaming=lowercase_  ,num_proc=lowercase_  ,**lowercase_  ,)
						lowerCAmelCase__      :		Optional[int]    							=      path_or_paths if isinstance(lowercase_  ,lowercase_						) else {self.split: path_or_paths}
						lowerCAmelCase__      :		str    							=      _PACKAGED_DATASETS_MODULES['''parquet'''][1]
						lowerCAmelCase__      :		Optional[Any]    							=      Parquet(
						    cache_dir=lowercase_  ,data_files=lowercase_  ,features=lowercase_  ,hash=lowercase_  ,**lowercase_  ,)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						# Build iterable dataset
						if self.streaming:
							lowerCAmelCase__      :		Dict    							=      self.builder.as_streaming_dataset(split=self.split						)
						# Build regular (map-style) dataset
						else:
							lowerCAmelCase__      :		int    							=      None
							lowerCAmelCase__      :		Tuple    							=      None
							lowerCAmelCase__      :		Optional[int]    							=      None
							lowerCAmelCase__      :		int    							=      None

							self.builder.download_and_prepare(
							    download_config=lowercase_  ,download_mode=lowercase_  ,verification_mode=lowercase_  ,base_path=lowercase_  ,num_proc=self.num_proc  ,)
							lowerCAmelCase__      :		List[str]    							=      self.builder.as_dataset(
							    split=self.split  ,verification_mode=lowercase_  ,in_memory=self.keep_in_memory						)
						return dataset







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					def __init__(			self     :							Optional[Any]  ,lowercase_     :							Dataset  ,lowercase_     :							Union[PathLike, BinaryIO]  ,lowercase_     :							Optional[int] = None  ,**lowercase_     :							Optional[int]  ,):
						lowerCAmelCase__      :		Dict    							=      dataset
						lowerCAmelCase__      :		List[str]    							=      path_or_buf
						lowerCAmelCase__      :		List[Any]    							=      batch_size or get_writer_batch_size(dataset.features						)
						lowerCAmelCase__      :		str    							=      parquet_writer_kwargs







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		int    							=      self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

						if isinstance(self.path_or_buf  ,(str, bytes, os.PathLike)						):
							with open(self.path_or_buf  ,'''wb+'''						) as buffer:
								lowerCAmelCase__      :		Optional[Any]    							=      self._write(file_obj=lowercase_  ,batch_size=lowercase_  ,**self.parquet_writer_kwargs						)
						else:
							lowerCAmelCase__      :		List[str]    							=      self._write(file_obj=self.path_or_buf  ,batch_size=lowercase_  ,**self.parquet_writer_kwargs						)
						return written







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							BinaryIO  ,lowercase_     :							int  ,**lowercase_     :							Dict						):
						lowerCAmelCase__      :		Optional[Any]    							=      0
						lowerCAmelCase__      :		Dict    							=      parquet_writer_kwargs.pop('''path_or_buf'''  ,lowercase_						)
						lowerCAmelCase__      :		int    							=      self.dataset.features.arrow_schema

						lowerCAmelCase__      :		Optional[int]    							=      pq.ParquetWriter(lowercase_  ,schema=lowercase_  ,**lowercase_						)

						for offset in logging.tqdm(
						    range(0  ,len(self.dataset						)  ,lowercase_						)  ,unit='''ba'''  ,disable=not logging.is_progress_bar_enabled()  ,desc='''Creating parquet from Arrow format'''  ,):
							lowerCAmelCase__      :		Optional[int]    							=      query_table(
							    table=self.dataset._data  ,key=slice(lowercase_  ,offset + batch_size						)  ,indices=self.dataset._indices if self.dataset._indices is not None else None  ,)
							writer.write_table(lowercase_						)
							written += batch.nbytes
						writer.close()
						return written







"""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_						)




