

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}  )







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__  )




