

import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    SqueezeBertForMaskedLM,
							    SqueezeBertForMultipleChoice,
							    SqueezeBertForQuestionAnswering,
							    SqueezeBertForSequenceClassification,
							    SqueezeBertForTokenClassification,
							    SqueezeBertModel,
							)



class       __snake_case  (    UpperCamelCase__       ):

		def __init__(					self						,		_A						,		_A=13						,		_A=7						,		_A=True						,		_A=True						,		_A=False						,		_A=True						,		_A=99						,		_A=32						,		_A=5						,		_A=4						,		_A=64						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=16						,		_A=2						,		_A=0.0_2						,		_A=3						,		_A=4						,		_A=None						,		_A=2						,		_A=2						,		_A=2						,		_A=2						,		_A=4						,		_A=1						,		):
						SCREAMING_SNAKE_CASE_									=						parent
						SCREAMING_SNAKE_CASE_									=						batch_size
						SCREAMING_SNAKE_CASE_									=						seq_length
						SCREAMING_SNAKE_CASE_									=						is_training
						SCREAMING_SNAKE_CASE_									=						use_input_mask
						SCREAMING_SNAKE_CASE_									=						use_token_type_ids
						SCREAMING_SNAKE_CASE_									=						use_labels
						SCREAMING_SNAKE_CASE_									=						vocab_size
						SCREAMING_SNAKE_CASE_									=						hidden_size
						SCREAMING_SNAKE_CASE_									=						num_hidden_layers
						SCREAMING_SNAKE_CASE_									=						num_attention_heads
						SCREAMING_SNAKE_CASE_									=						intermediate_size
						SCREAMING_SNAKE_CASE_									=						hidden_act
						SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
						SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_									=						max_position_embeddings
						SCREAMING_SNAKE_CASE_									=						type_vocab_size
						SCREAMING_SNAKE_CASE_									=						type_sequence_label_size
						SCREAMING_SNAKE_CASE_									=						initializer_range
						SCREAMING_SNAKE_CASE_									=						num_labels
						SCREAMING_SNAKE_CASE_									=						num_choices
						SCREAMING_SNAKE_CASE_									=						scope
						SCREAMING_SNAKE_CASE_									=						q_groups
						SCREAMING_SNAKE_CASE_									=						k_groups
						SCREAMING_SNAKE_CASE_									=						v_groups
						SCREAMING_SNAKE_CASE_									=						post_attention_groups
						SCREAMING_SNAKE_CASE_									=						intermediate_groups
						SCREAMING_SNAKE_CASE_									=						output_groups

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size)

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

						SCREAMING_SNAKE_CASE_									=						None
						SCREAMING_SNAKE_CASE_									=						None
						SCREAMING_SNAKE_CASE_									=						None
						if self.use_labels:
										SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size]						,		self.type_sequence_label_size)
										SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size, self.seq_length]						,		self.num_labels)
										SCREAMING_SNAKE_CASE_									=						ids_tensor([self.batch_size]						,		self.num_choices)

						SCREAMING_SNAKE_CASE_									=						self.get_config()

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

		def 						lowerCAmelCase__     (					self):
						return SqueezeBertConfig(
						    embedding_size=self.hidden_size						,		vocab_size=self.vocab_size						,		hidden_size=self.hidden_size						,		num_hidden_layers=self.num_hidden_layers						,		num_attention_heads=self.num_attention_heads						,		intermediate_size=self.intermediate_size						,		hidden_act=self.hidden_act						,		attention_probs_dropout_prob=self.hidden_dropout_prob						,		attention_dropout=self.attention_probs_dropout_prob						,		max_position_embeddings=self.max_position_embeddings						,		initializer_range=self.initializer_range						,		q_groups=self.q_groups						,		k_groups=self.k_groups						,		v_groups=self.v_groups						,		post_attention_groups=self.post_attention_groups						,		intermediate_groups=self.intermediate_groups						,		output_groups=self.output_groups						,		)

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						SqueezeBertModel(config=__A)
						model.to(__A)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(__A						,		__A)
						SCREAMING_SNAKE_CASE_									=						model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.seq_length, self.hidden_size))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						SqueezeBertForMaskedLM(config=__A)
						model.to(__A)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(__A						,		attention_mask=__A						,		labels=__A)
						self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						SqueezeBertForQuestionAnswering(config=__A)
						model.to(__A)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(
						    __A						,		attention_mask=__A						,		start_positions=__A						,		end_positions=__A)
						self.parent.assertEqual(result.start_logits.shape						,		(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape						,		(self.batch_size, self.seq_length))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						self.num_labels
						SCREAMING_SNAKE_CASE_									=						SqueezeBertForSequenceClassification(__A)
						model.to(__A)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						model(__A						,		attention_mask=__A						,		labels=__A)
						self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						self.num_labels
						SCREAMING_SNAKE_CASE_									=						SqueezeBertForTokenClassification(config=__A)
						model.to(__A)
						model.eval()

						SCREAMING_SNAKE_CASE_									=						model(__A						,		attention_mask=__A						,		labels=__A)
						self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.num_labels))

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						self.num_choices
						SCREAMING_SNAKE_CASE_									=						SqueezeBertForMultipleChoice(config=__A)
						model.to(__A)
						model.eval()
						SCREAMING_SNAKE_CASE_									=						input_ids.unsqueeze(1).expand(-1						,		self.num_choices						,		-1).contiguous()
						SCREAMING_SNAKE_CASE_									=						input_mask.unsqueeze(1).expand(-1						,		self.num_choices						,		-1).contiguous()
						SCREAMING_SNAKE_CASE_									=						model(
						    __A						,		attention_mask=__A						,		labels=__A						,		)
						self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_choices))


		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
						((SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_)							,			(SCREAMING_SNAKE_CASE_))									=						config_and_inputs
						SCREAMING_SNAKE_CASE_									=						{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict






@require_torch
class       __snake_case  (    UpperCamelCase__     , UpperCamelCase__     , unittest.TestCase       ):
		__lowerCAmelCase     :	List[Any]						 =  (
		    (
		        SqueezeBertModel,
		        SqueezeBertForMaskedLM,
		        SqueezeBertForMultipleChoice,
		        SqueezeBertForQuestionAnswering,
		        SqueezeBertForSequenceClassification,
		        SqueezeBertForTokenClassification,
		    )
		    if is_torch_available()
		    else None
		)
		__lowerCAmelCase     :	Tuple						 =  (
		    {
		        'feature-extraction': SqueezeBertModel,
		        'fill-mask': SqueezeBertForMaskedLM,
		        'question-answering': SqueezeBertForQuestionAnswering,
		        'text-classification': SqueezeBertForSequenceClassification,
		        'token-classification': SqueezeBertForTokenClassification,
		        'zero-shot': SqueezeBertForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowerCAmelCase     :	int						 =  False
		__lowerCAmelCase     :	Any						 =  True
		__lowerCAmelCase     :	Dict						 =  False

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						SqueezeBertModelTester(self)
						SCREAMING_SNAKE_CASE_									=						ConfigTester(self						,		config_class=__A						,		dim=37)

		def 						lowerCAmelCase__     (					self):
						self.config_tester.run_common_tests()

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_model(*__A)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_masked_lm(*__A)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_question_answering(*__A)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__A)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_token_classification(*__A)

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__A)


		@slow
		def 						lowerCAmelCase__     (					self):



						for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_									=						SqueezeBertModel.from_pretrained(__A)
										self.assertIsNotNone(__A)






@require_sentencepiece
@require_tokenizers
@require_torch
class       __snake_case  (    unittest.TestCase       ):

		@slow
		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')

						SCREAMING_SNAKE_CASE_									=						torch.tensor([[1, 29414, 232, 328, 740, 1140, 12695, 69, 13, 1588, 2]])
						SCREAMING_SNAKE_CASE_									=						model(__A)[0]
						SCREAMING_SNAKE_CASE_									=						torch.Size((1, 3))
						self.assertEqual(output.shape						,		__A)
						SCREAMING_SNAKE_CASE_									=						torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
						self.assertTrue(torch.allclose(__A						,		__A						,		atol=1E-4))





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


UpperCamelCase__  :      Optional[int]			=    datasets.utils.logging.get_logger(__name__)



@dataclass
class       __snake_case  (    datasets.BuilderConfig       ):
  __lowerCAmelCase     :	Optional[datasets.Features]						 =  None
  __lowerCAmelCase     :	str						 =  "utf-8"
  __lowerCAmelCase     :	Optional[str]						 =  None
  __lowerCAmelCase     :	Optional[str]						 =  None
  __lowerCAmelCase     :	bool						 =  True  # deprecated
  __lowerCAmelCase     :	Optional[int]						 =  None  # deprecated
  __lowerCAmelCase     :	int						 =  10 << 20  # 10MB
  __lowerCAmelCase     :	Optional[bool]						 =  None






class       __snake_case  (    datasets.ArrowBasedBuilder       ):
  __lowerCAmelCase     :	int						 =  JsonConfig

  def 						lowerCAmelCase__     (					self):
      if self.config.block_size is not None:
          logger.warning('The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead')
          SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self						,		_A):
      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}""")
      SCREAMING_SNAKE_CASE_									=						dl_manager.download_and_extract(self.config.data_files)
      if isinstance(_A						,		(str, list, tuple)):
          SCREAMING_SNAKE_CASE_									=						data_files
          if isinstance(_A						,		_A):
              SCREAMING_SNAKE_CASE_									=						[files]
          SCREAMING_SNAKE_CASE_									=						[dl_manager.iter_files(_A) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,		gen_kwargs={'files': files})]
      SCREAMING_SNAKE_CASE_									=						[]
      for split_name, files in data_files.items():
          if isinstance(_A						,		_A):
              SCREAMING_SNAKE_CASE_									=						[files]
          SCREAMING_SNAKE_CASE_									=						[dl_manager.iter_files(_A) for file in files]
          splits.append(datasets.SplitGenerator(name=_A						,		gen_kwargs={'files': files}))
      return splits

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


  def 						lowerCAmelCase__     (					self						,		_A):
      for file_idx, file in enumerate(itertools.chain.from_iterable(_A)):
          # 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(_A						,		encoding=self.config.encoding						,		errors=self.config.encoding_errors) as f:
                  SCREAMING_SNAKE_CASE_									=						json.load(_A)

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

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

          # If the file has one json object per line
          else:
              with open(_A						,		'rb') as f:
                  SCREAMING_SNAKE_CASE_									=						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
                  SCREAMING_SNAKE_CASE_									=						max(self.config.chunksize // 32						,		16 << 10)
                  SCREAMING_SNAKE_CASE_									=						(
                      self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
                  )
                  while True:
                      SCREAMING_SNAKE_CASE_									=						f.read(self.config.chunksize)
                      if not batch:
                          break
                      # Finish current line
                      try:
                          batch += f.readline()
                      except (AttributeError, io.UnsupportedOperation):
                          batch += readline(_A)
                      # PyArrow only accepts utf-8 encoded bytes
                      if self.config.encoding != "utf-8":
                          SCREAMING_SNAKE_CASE_									=						batch.decode(self.config.encoding						,		errors=_A).encode('utf-8')
                      try:
                          while True:
                              try:
                                  SCREAMING_SNAKE_CASE_									=						paj.read_json(
                                      io.BytesIO(_A)						,		read_options=paj.ReadOptions(block_size=_A))
                                  break
                              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                  if (
                                      isinstance(_A						,		pa.ArrowInvalid)
                                      and "straddling" not in str(_A)
                                      or block_size > len(_A)
                                  ):
                                      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(_A)} 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(
                                  _A						,		encoding=self.config.encoding						,		errors=self.config.encoding_errors) as f:
                                  SCREAMING_SNAKE_CASE_									=						json.load(_A)
                          except json.JSONDecodeError:
                              logger.error(f"""Failed to read file '{file}' with error {type(_A)}: {e}""")
                              raise e
                          # If possible, parse the file as a list of json objects and exit the loop
                          if isinstance(_A						,		_A):  # list is the only sequence type supported in JSON
                              try:
                                  SCREAMING_SNAKE_CASE_									=						set().union(*[row.keys() for row in dataset])
                                  SCREAMING_SNAKE_CASE_									=						{col: [row.get(_A) for row in dataset] for col in keys}
                                  SCREAMING_SNAKE_CASE_									=						pa.Table.from_pydict(_A)
                              except (pa.ArrowInvalid, AttributeError) as e:
                                  logger.error(f"""Failed to read file '{file}' with error {type(_A)}: {e}""")
                                  raise ValueError(f"""Not able to read records in the JSON file at {file}.""") from None
                              yield file_idx, self._cast_table(_A)
                              break
                          else:
                              logger.error(f"""Failed to read file '{file}' with error {type(_A)}: {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(_A)
                      batch_idx += 1


