

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


lowerCamelCase_    =					logging.get_logger(__name__)  # pylint: disable=invalid-name


lowerCamelCase_    =					'''lfs-multipart-upload'''







class  __lowerCamelCase				(	__snake_case	):







      @staticmethod
      def 	lowerCAmelCase_    (     lowerCamelCase   )						->       int:
        snake_case_       						=      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=lowerCamelCase							,  help="""Local path to repository you want to configure."""   )
        enable_parser.set_defaults(func=lambda lowerCamelCase   : LfsEnableCommand(lowerCamelCase   )   )

        snake_case_       						=      parser.add_parser(
            lowerCamelCase							,  help=(
                """Deprecated: use `huggingface-cli` instead. """
                """Command will get called by git-lfs, do not call it directly."""
            )							,  )
        upload_parser.set_defaults(func=lambda lowerCamelCase   : LfsUploadCommand(lowerCamelCase   )   )







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase   )						->       Dict:
        snake_case_       						=      args







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        warnings.warn(
            """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""   )
        snake_case_       						=      os.path.abspath(self.args.path   )
        if not os.path.isdir(lowerCamelCase   ):
          print("""This does not look like a valid git repo."""   )
          exit(1   )
        subprocess.run(
            """git config lfs.customtransfer.multipart.path transformers-cli""".split()							,  check=lowerCamelCase							,  cwd=lowerCamelCase   )
        subprocess.run(
            f'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()							,  check=lowerCamelCase							,  cwd=lowerCamelCase							,  )
        print("""Local repo set up for largefiles"""   )



def    UpperCamelCase(					lowercase_							)	->				List[Any]:





  '''simple docstring'''





  snake_case_       						=      json.dumps(lowercase_							) + """\n"""
  sys.stdout.write(lowercase_							)
  sys.stdout.flush()



def    UpperCamelCase(					)	->				Optional[Dict]:





  '''simple docstring'''





  snake_case_       						=      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  __lowerCamelCase				(	__snake_case	):







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[int]:
        snake_case_       						=      filepath
        snake_case_       						=      seek_from
        snake_case_       						=      read_limit
        snake_case_       						=      0







      def __enter__(     self   )						->       Tuple:
        snake_case_       						=      open(self.filepath							,  """rb"""   )
        self.f.seek(self.seek_from   )
        return self







      def __len__(     self   )						->       Optional[int]:
        snake_case_       						=      os.fstat(self.f.fileno()   ).st_size
        return min(self.read_limit							,  total_length - self.seek_from   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase=-1   )						->       str:
        if self.n_seen >= self.read_limit:
          return b""
        snake_case_       						=      self.read_limit - self.n_seen
        snake_case_       						=      self.f.read(remaining_amount if n < 0 else min(lowerCamelCase							,  lowerCamelCase   )   )
        self.n_seen += len(lowerCamelCase   )
        return data







      def __iter__(     self   )						->       Dict:
        yield self.read(n=4 * 1024 * 1024   )







      def __exit__(     self							,  *lowerCamelCase   )						->       Tuple:
        self.f.close()







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      args







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # 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.
        snake_case_       						=      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:
          snake_case_       						=      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   )

          snake_case_       						=      msg["""oid"""]
          snake_case_       						=      msg["""path"""]
          snake_case_       						=      msg["""action"""]["""href"""]
          snake_case_       						=      msg["""action"""]["""header"""]
          snake_case_       						=      int(header.pop("""chunk_size"""   )   )
          snake_case_       						=      list(header.values()   )

          snake_case_       						=      []
          for i, presigned_url in enumerate(lowerCamelCase   ):
            with FileSlice(lowerCamelCase							,  seek_from=i * chunk_size							,  read_limit=lowerCamelCase   ) as data:
              snake_case_       						=      requests.put(lowerCamelCase							,  data=lowerCamelCase   )
              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.

          snake_case_       						=      requests.post(
              lowerCamelCase							,  json={
                  """oid""": oid,
                  """parts""": parts,
              }							,  )
          r.raise_for_status()

          write_msg({"""event""": """complete""", """oid""": oid}   )


import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import 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 (
        ErnieMForInformationExtraction,
        ErnieMForMultipleChoice,
        ErnieMForQuestionAnswering,
        ErnieMForSequenceClassification,
        ErnieMForTokenClassification,
        ErnieMModel,
    )
    from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST







class  __lowerCamelCase				:







      def __init__(     self							,  lowerCamelCase							,  lowerCamelCase=13							,  lowerCamelCase=7							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase=True							,  lowerCamelCase=99							,  lowerCamelCase=32							,  lowerCamelCase=5							,  lowerCamelCase=4							,  lowerCamelCase=37							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.1							,  lowerCamelCase=0.1							,  lowerCamelCase=512							,  lowerCamelCase=16							,  lowerCamelCase=2							,  lowerCamelCase=0.02							,  lowerCamelCase=3							,  lowerCamelCase=4							,  lowerCamelCase=None							,  )						->       Optional[int]:
        snake_case_       						=      parent
        snake_case_       						=      batch_size
        snake_case_       						=      seq_length
        snake_case_       						=      is_training
        snake_case_       						=      use_input_mask
        snake_case_       						=      use_labels
        snake_case_       						=      vocab_size
        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      type_vocab_size
        snake_case_       						=      type_sequence_label_size
        snake_case_       						=      initializer_range
        snake_case_       						=      num_labels
        snake_case_       						=      num_choices
        snake_case_       						=      scope







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size   )

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

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

        snake_case_       						=      self.get_config()

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







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size   )

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

        return config, input_ids, input_mask







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        return ErnieMConfig(
            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							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  max_position_embeddings=self.max_position_embeddings							,  type_vocab_size=self.type_vocab_size							,  initializer_range=self.initializer_range							,  )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Union[str, Any]:
        snake_case_       						=      ErnieMModel(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase							,  return_dict=lowerCamelCase   )
        self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        snake_case_       						=      ErnieMForQuestionAnswering(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(
            lowerCamelCase							,  attention_mask=lowerCamelCase							,  start_positions=lowerCamelCase							,  end_positions=lowerCamelCase							,  )
        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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        snake_case_       						=      ErnieMForInformationExtraction(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      torch.ones_like(lowerCamelCase							,  dtype=torch.floataa   )
        snake_case_       						=      model(
            lowerCamelCase							,  attention_mask=lowerCamelCase							,  start_positions=lowerCamelCase							,  end_positions=lowerCamelCase							,  )
        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							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      self.num_labels
        snake_case_       						=      ErnieMForSequenceClassification(lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      model(lowerCamelCase							,  attention_mask=lowerCamelCase							,  labels=lowerCamelCase   )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Union[str, Any]:
        snake_case_       						=      self.num_labels
        snake_case_       						=      ErnieMForTokenClassification(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        input_ids.to(lowerCamelCase   )
        input_mask.to(lowerCamelCase   )
        token_labels.to(lowerCamelCase   )

        snake_case_       						=      model(lowerCamelCase							,  attention_mask=lowerCamelCase							,  labels=lowerCamelCase   )

        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels)   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        snake_case_       						=      self.num_choices
        snake_case_       						=      ErnieMForMultipleChoice(config=lowerCamelCase   )
        model.to(lowerCamelCase   )
        model.eval()
        snake_case_       						=      input_ids.unsqueeze(1   ).expand(-1							,  self.num_choices							,  -1   ).contiguous()
        snake_case_       						=      input_mask.unsqueeze(1   ).expand(-1							,  self.num_choices							,  -1   ).contiguous()
        snake_case_       						=      model(
            lowerCamelCase							,  attention_mask=lowerCamelCase							,  labels=lowerCamelCase							,  )
        self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_choices)   )







      def 	lowerCAmelCase_    (     self   )						->       str:
        snake_case_       						=      self.prepare_config_and_inputs()
        (
            (
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				(
            snake_case_
        )						,				
        )       						=      config_and_inputs
        snake_case_       						=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class  __lowerCamelCase				(	__snake_case  ,							__snake_case  ,							unittest.TestCase	):
      lowerCamelCase_	:      Optional[int]          =					(
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase_	:      List[str]          =					()
      lowerCamelCase_	:      Optional[int]          =					(
          {
              'feature-extraction': ErnieMModel,
              'question-answering': ErnieMForQuestionAnswering,
              'text-classification': ErnieMForSequenceClassification,
              'token-classification': ErnieMForTokenClassification,
              'zero-shot': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase_	:      List[Any]          =					False







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Tuple:
        if pipeline_test_casse_name == "QAPipelineTests":
          return True

        return False







      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        snake_case_       						=      ErnieMModelTester(self   )
        snake_case_       						=      ConfigTester(self							,  config_class=lowerCamelCase							,  hidden_size=37   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        self.config_tester.run_common_tests()







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          snake_case_       						=      type
          self.model_tester.create_and_check_model(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase   )







      @slow
      def 	lowerCAmelCase_    (     self   )						->       int:
        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          snake_case_       						=      ErnieMModel.from_pretrained(lowerCamelCase   )
          self.assertIsNotNone(lowerCamelCase   )







@require_torch
class  __lowerCamelCase				(	unittest.TestCase	):







      @slow
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""   )
        model.eval()
        snake_case_       						=      torch.tensor([[0, 1, 2, 3, 4, 5]]   )
        snake_case_       						=      model(lowerCamelCase   )[0]

        # TODO Replace vocab size
        snake_case_       						=      768

        snake_case_       						=      torch.Size((1, 6, hidden_size)   )
        self.assertEqual(output.shape							,  lowerCamelCase   )

        snake_case_       						=      torch.tensor(
            [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]   )

        self.assertTrue(torch.allclose(output[:, :3, :3]							,  lowerCamelCase							,  atol=1e-3   )   )