




import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_lowerCamelCase			:	List[str]							 =     datasets.utils.logging.get_logger(__name__)


@dataclass
class        UpperCamelCase_					(				datasets.BuilderConfig  ):

    '''simple docstring'''






    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   "utf-8"
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   True  # deprecated
    UpperCAmelCase__													=   None  # deprecated
    UpperCAmelCase__													=   10 << 20  # 10MB
    UpperCAmelCase__													=   None


class        UpperCamelCase_					(				datasets.ArrowBasedBuilder  ):

    '''simple docstring'''






    UpperCAmelCase__													=   JsonConfig


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           if self.config.block_size is not None:
                  logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''')
                  A__   				=					self.config.block_size
           if self.config.use_threads is not True:
                  logger.warning(
                      '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''')
           if self.config.newlines_in_values is not None:
                  raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''')
           return datasets.DatasetInfo(features=self.config.features)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str)		->Optional[Any]:





           '''simple docstring'''

           if not self.config.data_files:
                  raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""")
           A__   				=					dl_manager.download_and_extract(self.config.data_files)
           if isinstance(UpperCAmelCase__      ,				(str, list, tuple)):
                  A__   				=					data_files
                  if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         A__   				=					[files]
                  A__   				=					[dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,				gen_kwargs={'''files''': files})]
           A__   				=					[]
           for split_name, files in data_files.items():
                  if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         A__   				=					[files]
                  A__   				=					[dl_manager.iter_files(UpperCAmelCase__) for file in files]
                  splits.append(datasets.SplitGenerator(name=UpperCAmelCase__      ,				gen_kwargs={'''files''': files}))
           return splits


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			pa.Table)		->pa.Table:





           '''simple docstring'''

           if self.config.features is not None:
                  # adding missing columns
                  for column_name in set(self.config.features) - set(pa_table.column_names):
                         A__   				=					self.config.features.arrow_schema.field(UpperCAmelCase__).type
                         A__   				=					pa_table.append_column(UpperCAmelCase__      ,				pa.array([None] * len(UpperCAmelCase__)      ,				type=UpperCAmelCase__))
                  # more expensive cast to support nested structures with keys in a different order
                  # allows str <-> int/float or str to Audio for example
                  A__   				=					table_cast(UpperCAmelCase__      ,				self.config.features.arrow_schema)
           return pa_table





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[Any])		->List[str]:





           '''simple docstring'''

           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__)):
                  # If the file is one json object and if we need to look at the list of items in one specific field
                  if self.config.field is not None:
                         with open(UpperCAmelCase__      ,				encoding=self.config.encoding      ,				errors=self.config.encoding_errors) as f:
                                A__   				=					json.load(UpperCAmelCase__)

                         # We keep only the field we are interested in
                         A__   				=					dataset[self.config.field]

                         # We accept two format: a list of dicts or a dict of lists
                         if isinstance(UpperCAmelCase__      ,				(list, tuple)):
                                A__   				=					set().union(*[row.keys() for row in dataset])
                                A__   				=					{col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                         else:
                                A__   				=					dataset
                         A__   				=					pa.Table.from_pydict(UpperCAmelCase__)
                         yield file_idx, self._cast_table(UpperCAmelCase__)

                  # If the file has one json object per line
                  else:
                         with open(UpperCAmelCase__      ,				'''rb''') as f:
                                A__   				=					0
                                # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                                # Set a default minimum value of 16kB if the chunk size is really small
                                A__   				=					max(self.config.chunksize // 32      ,				16 << 10)
                                A__   				=					(
                                    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                                )
                                while True:
                                       A__   				=					f.read(self.config.chunksize)
                                       if not batch:
                                              break
                                       # Finish current line
                                       try:
                                              batch += f.readline()
                                       except (AttributeError, io.UnsupportedOperation):
                                              batch += readline(UpperCAmelCase__)
                                       # PyArrow only accepts utf-8 encoded bytes
                                       if self.config.encoding != "utf-8":
                                              A__   				=					batch.decode(self.config.encoding      ,				errors=UpperCAmelCase__).encode('''utf-8''')
                                       try:
                                              while True:
                                                     try:
                                                            A__   				=					paj.read_json(
                                                                io.BytesIO(UpperCAmelCase__)      ,				read_options=paj.ReadOptions(block_size=UpperCAmelCase__))
                                                            break
                                                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                            if (
                                                                isinstance(UpperCAmelCase__      ,				pa.ArrowInvalid)
                                                                and "straddling" not in str(UpperCAmelCase__)
                                                                or block_size > len(UpperCAmelCase__)
                                                            ):
                                                                   raise
                                                            else:
                                                                   # Increase the block size in case it was too small.
                                                                   # The block size will be reset for the next file.
                                                                   logger.debug(
                                                                       f"""Batch of {len(UpperCAmelCase__)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""")
                                                                   block_size *= 2
                                       except pa.ArrowInvalid as e:
                                              try:
                                                     with open(
                                                         UpperCAmelCase__      ,				encoding=self.config.encoding      ,				errors=self.config.encoding_errors) as f:
                                                            A__   				=					json.load(UpperCAmelCase__)
                                              except json.JSONDecodeError:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise e
                                              # If possible, parse the file as a list of json objects and exit the loop
                                              if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):  # list is the only sequence type supported in JSON
                                                     try:
                                                            A__   				=					set().union(*[row.keys() for row in dataset])
                                                            A__   				=					{col: [row.get(UpperCAmelCase__) for row in dataset] for col in keys}
                                                            A__   				=					pa.Table.from_pydict(UpperCAmelCase__)
                                                     except (pa.ArrowInvalid, AttributeError) as e:
                                                            logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                            raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
                                                     yield file_idx, self._cast_table(UpperCAmelCase__)
                                                     break
                                              else:
                                                     logger.error(f"""Failed to read file '{file}' with error {type(UpperCAmelCase__)}: {e}""")
                                                     raise ValueError(
                                                         f"""Not able to read records in the JSON file at {file}. """
                                                         f"""You should probably indicate the field of the JSON file containing your records. """
                                                         f"""This JSON file contain the following fields: {str(list(dataset.keys()))}. """
                                                         f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                       yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase__)
                                       batch_idx += 1








from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )


@require_tf
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   XGLMConfig
    UpperCAmelCase__													=   {}
    UpperCAmelCase__													=   '''gelu'''


    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=14      ,				UpperCAmelCase__   :			Tuple=7      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=99      ,				UpperCAmelCase__   :			Union[str, Any]=32      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			Dict=4      ,				UpperCAmelCase__   :			List[str]=37      ,				UpperCAmelCase__   :			Union[str, Any]="gelu"      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=512      ,				UpperCAmelCase__   :			List[Any]=0.02      ,				)		->str:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_input_mask
           A__   				=					use_labels
           A__   				=					vocab_size
           A__   				=					d_model
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					ffn_dim
           A__   				=					activation_function
           A__   				=					activation_dropout
           A__   				=					attention_dropout
           A__   				=					max_position_embeddings
           A__   				=					initializer_range
           A__   				=					None
           A__   				=					0
           A__   				=					2
           A__   				=					1


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           return XGLMConfig.from_pretrained('''facebook/xglm-564M''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           A__   				=					tf.clip_by_value(
               ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)      ,				clip_value_min=0      ,				clip_value_max=3)

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

           A__   				=					self.get_config()

           A__   				=					floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,				2)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''simple docstring'''

           return XGLMConfig(
               vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				num_layers=self.num_hidden_layers      ,				attention_heads=self.num_attention_heads      ,				ffn_dim=self.ffn_dim      ,				activation_function=self.activation_function      ,				activation_dropout=self.activation_dropout      ,				attention_dropout=self.attention_dropout      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				use_cache=UpperCAmelCase__      ,				bos_token_id=self.bos_token_id      ,				eos_token_id=self.eos_token_id      ,				pad_token_id=self.pad_token_id      ,				return_dict=UpperCAmelCase__      ,				)





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()

           (
               (
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							
           )   				=					config_and_inputs

           A__   				=					{
               '''input_ids''': input_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict


@require_tf
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__													=   (TFXGLMForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__													=   (
        {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					TFXGLMModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				n_embd=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[str]:





           '''simple docstring'''

           for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					TFXGLMModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)





    @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           super().test_resize_token_embeddings()


@require_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]=True)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					tf.convert_to_tensor([[2, 268, 9_865]]      ,				dtype=tf.intaa)  # The dog
           # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
           # fmt: off
           A__   				=					[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
           # fmt: on
           A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				num_beams=1)
           if verify_outputs:
                  self.assertListEqual(output_ids[0].numpy().tolist()      ,				UpperCAmelCase__)


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')
           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')

           tf.random.set_seed(0)
           A__   				=					tokenizer('''Today is a nice day and'''      ,				return_tensors='''tf''')
           A__   				=					tokenized.input_ids
           # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
           with tf.device(''':/CPU:0'''):
                  A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				seed=[7, 0])
           A__   				=					tokenizer.decode(output_ids[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					(
               '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
           )
           self.assertEqual(UpperCAmelCase__      ,				UpperCAmelCase__)





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')

           A__   				=					'''left'''

           # use different length sentences to test batching
           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When''',
               '''Hello, my dog is a little''',
           ]

           A__   				=					tokenizer(UpperCAmelCase__      ,				return_tensors='''tf'''      ,				padding=UpperCAmelCase__)
           A__   				=					inputs['''input_ids''']

           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				attention_mask=inputs['''attention_mask''']      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[0]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[1]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer.batch_decode(UpperCAmelCase__      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_non_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
               '''a single''',
               '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
           ]
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				[non_padded_sentence, padded_sentence])


