code: 
  
  import json
  import os
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from contextlib import AbstractContextManager
  from typing import Dict, List, Optional
  
  import requests
  
  from ..utils import logging
  from . import BaseTransformersCLICommand
  
  
  A_		:			str										=   logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  A_		:			Optional[Any]										=   'lfs-multipart-upload'
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      @staticmethod
      def      lowerCAmelCase_						(lowercase__  )     ->      Any:
          __UpperCAmelCase									=			parser.add_parser(
              '''lfs-enable-largefiles'''     ,      help=(
                  '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
              )     ,      )
          enable_parser.add_argument('''path'''     ,      type=a_     ,      help='''Local path to repository you want to configure.'''  )
          enable_parser.set_defaults(func=lambda lowercase__  : LfsEnableCommand(a_  )  )
  
          __UpperCAmelCase									=			parser.add_parser(
              a_     ,      help=(
                  '''Deprecated: use `huggingface-cli` instead. '''
                  '''Command will get called by git-lfs, do not call it directly.'''
              )     ,      )
          upload_parser.set_defaults(func=lambda lowercase__  : LfsUploadCommand(a_  )  )
  
  
  
  
  
  
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__  )     ->      Optional[int]:
          __UpperCAmelCase									=			args
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          warnings.warn(
              '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''  )
          __UpperCAmelCase									=			os.path.abspath(self.args.path  )
          if not os.path.isdir(a_  ):
              print('''This does not look like a valid git repo.'''  )
              exit(1  )
          subprocess.run(
              '''git config lfs.customtransfer.multipart.path transformers-cli'''.split()     ,      check=a_     ,      cwd=a_  )
          subprocess.run(
              F'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()     ,      check=a_     ,      cwd=a_     ,      )
          print('''Local repo set up for largefiles'''  )
  
  
  
  
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			json.dumps(UpperCamelCase__      ) + '''\n'''
      sys.stdout.write(UpperCamelCase__      )
      sys.stdout.flush()
  
  
  def       __a       (   )      ->     Optional[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			json.loads(sys.stdin.readline().strip()      )
  
      if "terminate" in (msg.get('''type'''      ), msg.get('''event'''      )):
          # terminate message received
          return None
  
      if msg.get('''event'''      ) not in ("download", "upload"):
          logger.critical('''Received unexpected message'''      )
          sys.exit(1      )
  
      return msg
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          __UpperCAmelCase									=			filepath
          __UpperCAmelCase									=			seek_from
          __UpperCAmelCase									=			read_limit
          __UpperCAmelCase									=			0
  
  
      def __enter__(self  )     ->      List[str]:
          __UpperCAmelCase									=			open(self.filepath     ,      '''rb'''  )
          self.f.seek(self.seek_from  )
          return self
  
  
      def __len__(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			os.fstat(self.f.fileno()  ).st_size
          return min(self.read_limit     ,      total_length - self.seek_from  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__=-1  )     ->      Optional[Any]:
          if self.n_seen >= self.read_limit:
              return b""
          __UpperCAmelCase									=			self.read_limit - self.n_seen
          __UpperCAmelCase									=			self.f.read(remaining_amount if n < 0 else min(a_     ,      a_  )  )
          self.n_seen += len(a_  )
          return data
  
  
      def __iter__(self  )     ->      Union[str, Any]:
          yield self.read(n=4 * 1_024 * 1_024  )
  
  
  
  
  
  
      def __exit__(self     ,      *lowercase__  )     ->      Tuple:
          self.f.close()
  
  
  
  
  
  
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__  )     ->      str:
          __UpperCAmelCase									=			args
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          # Immediately after invoking a custom transfer process, git-lfs
          # sends initiation data to the process over stdin.
          # This tells the process useful information about the configuration.
          __UpperCAmelCase									=			json.loads(sys.stdin.readline().strip()  )
          if not (init_msg.get('''event'''  ) == "init" and init_msg.get('''operation'''  ) == "upload"):
              write_msg({'''error''': {'''code''': 32, '''message''': '''Wrong lfs init operation'''}}  )
              sys.exit(1  )
  
          # The transfer process should use the information it needs from the
          # initiation structure, and also perform any one-off setup tasks it
          # needs to do. It should then respond on stdout with a simple empty
          # confirmation structure, as follows:
          write_msg({}  )
  
          # After the initiation exchange, git-lfs will send any number of
          # transfer requests to the stdin of the transfer process, in a serial sequence.
          while True:
              __UpperCAmelCase									=			read_msg()
              if msg is None:
                  # When all transfers have been processed, git-lfs will send
                  # a terminate event to the stdin of the transfer process.
                  # On receiving this message the transfer process should
                  # clean up and terminate. No response is expected.
                  sys.exit(0  )
  
              __UpperCAmelCase									=			msg['''oid''']
              __UpperCAmelCase									=			msg['''path''']
              __UpperCAmelCase									=			msg['''action''']['''href''']
              __UpperCAmelCase									=			msg['''action''']['''header''']
              __UpperCAmelCase									=			int(header.pop('''chunk_size'''  )  )
              __UpperCAmelCase									=			list(header.values()  )
  
              __UpperCAmelCase									=			[]
              for i, presigned_url in enumerate(a_  ):
                  with FileSlice(a_     ,      seek_from=i * chunk_size     ,      read_limit=a_  ) as data:
                      __UpperCAmelCase									=			requests.put(a_     ,      data=a_  )
                      r.raise_for_status()
                      parts.append(
                          {
                              '''etag''': r.headers.get('''etag'''  ),
                              '''partNumber''': i + 1,
                          }  )
                      # In order to support progress reporting while data is uploading / downloading,
                      # the transfer process should post messages to stdout
                      write_msg(
                          {
                              '''event''': '''progress''',
                              '''oid''': oid,
                              '''bytesSoFar''': (i + 1) * chunk_size,
                              '''bytesSinceLast''': chunk_size,
                          }  )
                      # Not precise but that's ok.
  
              __UpperCAmelCase									=			requests.post(
                  a_     ,      json={
                      '''oid''': oid,
                      '''parts''': parts,
                  }     ,      )
              r.raise_for_status()
  
              write_msg({'''event''': '''complete''', '''oid''': oid}  )
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
         import tensorflow as tf
  
         from transformers import (
             TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFXLMForMultipleChoice,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForSequenceClassification,
             TFXLMForTokenClassification,
             TFXLMModel,
             TFXLMWithLMHeadModel,
             XLMConfig,
         )
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def __init__(self     ,      lowercase__     ,      )     ->      Tuple:
          __UpperCAmelCase									=			parent
          __UpperCAmelCase									=			13
          __UpperCAmelCase									=			7
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			False
          __UpperCAmelCase									=			False
          __UpperCAmelCase									=			False
          __UpperCAmelCase									=			2
          __UpperCAmelCase									=			99
          __UpperCAmelCase									=			0
          __UpperCAmelCase									=			32
          __UpperCAmelCase									=			2
          __UpperCAmelCase									=			4
          __UpperCAmelCase									=			0.1
          __UpperCAmelCase									=			0.1
          __UpperCAmelCase									=			512
          __UpperCAmelCase									=			16
          __UpperCAmelCase									=			2
          __UpperCAmelCase									=			0.02
          __UpperCAmelCase									=			3
          __UpperCAmelCase									=			4
          __UpperCAmelCase									=			'''last'''
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			0
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )
          __UpperCAmelCase									=			random_attention_mask([self.batch_size, self.seq_length]     ,      dtype=tf.floataa  )
  
          __UpperCAmelCase									=			None
          if self.use_input_lengths:
              __UpperCAmelCase									=			(
                  ids_tensor([self.batch_size]     ,      vocab_size=2  ) + self.seq_length - 2
              )  # small variation of seq_length
  
          __UpperCAmelCase									=			None
          if self.use_token_type_ids:
              __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.n_langs  )
  
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			None
          if self.use_labels:
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      2     ,      dtype=tf.floataa  )
              __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.num_choices  )
  
          __UpperCAmelCase									=			XLMConfig(
              vocab_size=self.vocab_size     ,      n_special=self.n_special     ,      emb_dim=self.hidden_size     ,      n_layers=self.num_hidden_layers     ,      n_heads=self.num_attention_heads     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      gelu_activation=self.gelu_activation     ,      sinusoidal_embeddings=self.sinusoidal_embeddings     ,      asm=self.asm     ,      causal=self.causal     ,      n_langs=self.n_langs     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      summary_type=self.summary_type     ,      use_proj=self.use_proj     ,      bos_token_id=self.bos_token_id     ,      )
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      List[Any]:
          __UpperCAmelCase									=			TFXLMModel(config=lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          __UpperCAmelCase									=			model(lowercase__  )
  
          __UpperCAmelCase									=			[input_ids, input_mask]
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      List[str]:
          __UpperCAmelCase									=			TFXLMWithLMHeadModel(lowercase__  )
  
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          __UpperCAmelCase									=			model(lowercase__  )
  
          __UpperCAmelCase									=			outputs
  
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      Optional[Any]:
          __UpperCAmelCase									=			TFXLMForQuestionAnsweringSimple(lowercase__  )
  
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths}
  
          __UpperCAmelCase									=			model(lowercase__  )
  
          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     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      Dict:
          __UpperCAmelCase									=			TFXLMForSequenceClassification(lowercase__  )
  
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''lengths''': input_lengths}
  
          __UpperCAmelCase									=			model(lowercase__  )
  
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.type_sequence_label_size)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      int:
          __UpperCAmelCase									=			self.num_labels
          __UpperCAmelCase									=			TFXLMForTokenClassification(config=lowercase__  )
          __UpperCAmelCase									=			{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      )     ->      Tuple:
          __UpperCAmelCase									=			self.num_choices
          __UpperCAmelCase									=			TFXLMForMultipleChoice(config=lowercase__  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			tf.tile(tf.expand_dims(lowercase__     ,      1  )     ,      (1, self.num_choices, 1)  )
          __UpperCAmelCase									=			{
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
              '''token_type_ids''': multiple_choice_token_type_ids,
          }
          __UpperCAmelCase									=			model(lowercase__  )
          self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     (
              __UpperCAmelCase
          )					,     
          )									=			config_and_inputs
          __UpperCAmelCase									=			{
              '''input_ids''': input_ids,
              '''token_type_ids''': token_type_ids,
              '''langs''': token_type_ids,
              '''lengths''': input_lengths,
          }
          return config, inputs_dict
  
  
  
  
  
  
  @require_tf
  class 					A_   (      _a ,     _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       (
          (
              TFXLMModel,
              TFXLMWithLMHeadModel,
              TFXLMForSequenceClassification,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForTokenClassification,
              TFXLMForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      a__     							=       (
          (TFXLMWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      a__     							=       (
          {
              "feature-extraction": TFXLMModel,
              "fill-mask": TFXLMWithLMHeadModel,
              "question-answering": TFXLMForQuestionAnsweringSimple,
              "text-classification": TFXLMForSequenceClassification,
              "text-generation": TFXLMWithLMHeadModel,
              "token-classification": TFXLMForTokenClassification,
              "zero-shot": TFXLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      a__     							=       False
      a__     							=       False
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith('''Fast'''  )
          ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True
  
          return False
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			TFXLMModelTester(self  )
          __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      emb_dim=37  )
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          self.config_tester.run_common_tests()
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase__  )
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      int:
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCAmelCase									=			TFXLMModel.from_pretrained(lowercase__  )
              self.assertIsNotNone(lowercase__  )
  
  
  
  
  
  
  @require_tf
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      @slow
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''  )
          __UpperCAmelCase									=			tf.convert_to_tensor([[14, 447]]     ,      dtype=tf.intaa  )  # the president
          __UpperCAmelCase									=			[
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          __UpperCAmelCase									=			model.generate(lowercase__     ,      do_sample=lowercase__  )
          self.assertListEqual(output_ids[0].numpy().tolist()     ,      lowercase__  )
  
  
  
  
  