"""simple docstring"""






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
       from cookiecutter.main import cookiecutter

       __A			   = True
except ImportError:
       __A			   = False

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Tuple:
   return AddNewModelCommand(args.testing       ,    args.testing_file       ,    path=args.path )




class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    @staticmethod
    def _snake_case			(   _UpperCAmelCase							):
       lowercase__:   List[Any]					=      parser.add_parser('''add-new-model'''							)
       add_new_model_parser.add_argument('''--testing'''    ,			action='''store_true'''    ,			help='''If in testing mode.'''							)
       add_new_model_parser.add_argument('''--testing_file'''    ,			type=_UpperCAmelCase    ,			help='''Configuration file on which to run.'''							)
       add_new_model_parser.add_argument(
           '''--path'''    ,			type=_UpperCAmelCase    ,			help='''Path to cookiecutter. Should only be used for testing purposes.'''							)
       add_new_model_parser.set_defaults(func=_UpperCAmelCase							)






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None    ,			*_UpperCAmelCase							):
       lowercase__:   str					=      testing
       lowercase__:   Tuple					=      testing_file
       lowercase__:   Optional[int]					=      path






    def _snake_case			(   self							):
       warnings.warn(
           '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '''
           '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '''
           '''checks, you should use `transformers-cli add-new-model-like` instead.'''							)
       if not _has_cookiecutter:
          raise ImportError(
              '''Model creation dependencies are required to use the `add_new_model` command. Install them by running '''
              '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'''							)
       # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
       lowercase__:   str					=      [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]]
       if len(_UpperCAmelCase							) > 0:
          raise ValueError(
              '''Several directories starting with `cookiecutter-template-` in current working directory. '''
              '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or '''
              '''change your working directory.'''							)

       lowercase__:   Optional[Any]					=      (
           Path(_UpperCAmelCase							).parent.parent.parent.parent if self._path is None else Path(self._path							).parent.parent
       )
       lowercase__:   str					=      path_to_transformer_root / '''templates''' / '''adding_a_new_model'''

       # Execute cookiecutter
       if not self._testing:
          cookiecutter(str(_UpperCAmelCase							)							)
       else:
          with open(self._testing_file    ,			'''r'''							) as configuration_file:
             lowercase__:   List[Any]					=      json.load(_UpperCAmelCase							)

          cookiecutter(
              str(path_to_cookiecutter if self._path is None else self._path							)    ,			no_input=_UpperCAmelCase    ,			extra_context=_UpperCAmelCase    ,			)

       lowercase__:   List[str]					=      [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0]

       # Retrieve configuration
       with open(directory + '''/configuration.json'''    ,			'''r'''							) as configuration_file:
          lowercase__:   Optional[Any]					=      json.load(_UpperCAmelCase							)

       lowercase__:   str					=      configuration['''lowercase_modelname''']
       lowercase__:   Dict					=      configuration['''generate_tensorflow_pytorch_and_flax''']
       os.remove(F"""{directory}/configuration.json"""							)

       lowercase__:   List[Any]					=      '''PyTorch''' in generate_tensorflow_pytorch_and_flax
       lowercase__:   Dict					=      '''TensorFlow''' in generate_tensorflow_pytorch_and_flax
       lowercase__:   List[str]					=      '''Flax''' in generate_tensorflow_pytorch_and_flax

       lowercase__:   int					=      F"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
       os.makedirs(_UpperCAmelCase    ,			exist_ok=_UpperCAmelCase							)
       os.makedirs(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}"""    ,			exist_ok=_UpperCAmelCase							)

       # Tests require submodules as they have parent imports
       with open(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"""    ,			'''w'''							):
          pass

       shutil.move(
           F"""{directory}/__init__.py"""    ,			F"""{model_dir}/__init__.py"""    ,			)
       shutil.move(
           F"""{directory}/configuration_{lowercase_model_name}.py"""    ,			F"""{model_dir}/configuration_{lowercase_model_name}.py"""    ,			)

       def remove_copy_lines(_UpperCAmelCase							):
          with open(_UpperCAmelCase    ,			'''r'''							) as f:
             lowercase__:   List[str]					=      f.readlines()
          with open(_UpperCAmelCase    ,			'''w'''							) as f:
             for line in lines:
                if "# Copied from transformers." not in line:
                   f.write(_UpperCAmelCase							)

       if output_pytorch:
          if not self._testing:
             remove_copy_lines(F"""{directory}/modeling_{lowercase_model_name}.py"""							)

          shutil.move(
              F"""{directory}/modeling_{lowercase_model_name}.py"""    ,			F"""{model_dir}/modeling_{lowercase_model_name}.py"""    ,			)

          shutil.move(
              F"""{directory}/test_modeling_{lowercase_model_name}.py"""    ,			F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"""    ,			)
       else:
          os.remove(F"""{directory}/modeling_{lowercase_model_name}.py"""							)
          os.remove(F"""{directory}/test_modeling_{lowercase_model_name}.py"""							)

       if output_tensorflow:
          if not self._testing:
             remove_copy_lines(F"""{directory}/modeling_tf_{lowercase_model_name}.py"""							)

          shutil.move(
              F"""{directory}/modeling_tf_{lowercase_model_name}.py"""    ,			F"""{model_dir}/modeling_tf_{lowercase_model_name}.py"""    ,			)

          shutil.move(
              F"""{directory}/test_modeling_tf_{lowercase_model_name}.py"""    ,			F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"""    ,			)
       else:
          os.remove(F"""{directory}/modeling_tf_{lowercase_model_name}.py"""							)
          os.remove(F"""{directory}/test_modeling_tf_{lowercase_model_name}.py"""							)

       if output_flax:
          if not self._testing:
             remove_copy_lines(F"""{directory}/modeling_flax_{lowercase_model_name}.py"""							)

          shutil.move(
              F"""{directory}/modeling_flax_{lowercase_model_name}.py"""    ,			F"""{model_dir}/modeling_flax_{lowercase_model_name}.py"""    ,			)

          shutil.move(
              F"""{directory}/test_modeling_flax_{lowercase_model_name}.py"""    ,			F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"""    ,			)
       else:
          os.remove(F"""{directory}/modeling_flax_{lowercase_model_name}.py"""							)
          os.remove(F"""{directory}/test_modeling_flax_{lowercase_model_name}.py"""							)

       shutil.move(
           F"""{directory}/{lowercase_model_name}.md"""    ,			F"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"""    ,			)

       shutil.move(
           F"""{directory}/tokenization_{lowercase_model_name}.py"""    ,			F"""{model_dir}/tokenization_{lowercase_model_name}.py"""    ,			)

       shutil.move(
           F"""{directory}/tokenization_fast_{lowercase_model_name}.py"""    ,			F"""{model_dir}/tokenization_{lowercase_model_name}_fast.py"""    ,			)

       from os import fdopen, remove
       from shutil import copymode, move
       from tempfile import mkstemp

       def replace(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
          # Create temp file
          lowercase__, lowercase__:   Any					=      mkstemp()
          lowercase__:   List[Any]					=      False
          with fdopen(_UpperCAmelCase    ,			'''w'''							) as new_file:
             with open(_UpperCAmelCase							) as old_file:
                for line in old_file:
                   new_file.write(_UpperCAmelCase							)
                   if line_to_copy_below in line:
                      lowercase__:   Optional[Any]					=      True
                      for line_to_copy in lines_to_copy:
                         new_file.write(_UpperCAmelCase							)

          if not line_found:
             raise ValueError(F"""Line {line_to_copy_below} was not found in file."""							)

          # Copy the file permissions from the old file to the new file
          copymode(_UpperCAmelCase    ,			_UpperCAmelCase							)
          # Remove original file
          remove(_UpperCAmelCase							)
          # Move new file
          move(_UpperCAmelCase    ,			_UpperCAmelCase							)

       def skip_units(_UpperCAmelCase							):
          return (
              ("generating PyTorch" in line and not output_pytorch)
              or ("generating TensorFlow" in line and not output_tensorflow)
              or ("generating Flax" in line and not output_flax)
          )

       def replace_in_files(_UpperCAmelCase							):
          with open(_UpperCAmelCase							) as datafile:
             lowercase__:   List[str]					=      []
             lowercase__:   Union[str, Any]					=      False
             lowercase__:   List[str]					=      False
             for line in datafile:
                if "# To replace in: " in line and "##" not in line:
                   lowercase__:   str					=      line.split('''"'''							)[1]
                   lowercase__:   List[Any]					=      skip_units(_UpperCAmelCase							)
                elif "# Below: " in line and "##" not in line:
                   lowercase__:   int					=      line.split('''"'''							)[1]
                   lowercase__:   int					=      skip_units(_UpperCAmelCase							)
                elif "# End." in line and "##" not in line:
                   if not skip_file and not skip_snippet:
                      replace(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

                   lowercase__:   int					=      []
                elif "# Replace with" in line and "##" not in line:
                   lowercase__:   Dict					=      []
                elif "##" not in line:
                   lines_to_copy.append(_UpperCAmelCase							)

          remove(_UpperCAmelCase							)

       replace_in_files(F"""{directory}/to_replace_{lowercase_model_name}.py"""							)
       os.rmdir(_UpperCAmelCase							)

"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = MBartConfig
    _UpperCAmelCase	:str						    = {}
    _UpperCAmelCase	:Union[str, Any]						    = "gelu"






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=20    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=0    ,			):
       lowercase__:   List[str]					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   List[Any]					=      seq_length
       lowercase__:   str					=      is_training
       lowercase__:   List[str]					=      use_labels
       lowercase__:   Optional[int]					=      vocab_size
       lowercase__:   int					=      hidden_size
       lowercase__:   Optional[int]					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Tuple					=      intermediate_size
       lowercase__:   Optional[Any]					=      hidden_dropout_prob
       lowercase__:   Optional[int]					=      attention_probs_dropout_prob
       lowercase__:   str					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      eos_token_id
       lowercase__:   int					=      pad_token_id
       lowercase__:   List[str]					=      bos_token_id






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size							)
       lowercase__:   str					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)    ,			1							)
       lowercase__:   List[Any]					=      tf.concat([input_ids, eos_tensor]    ,			axis=1							)

       lowercase__:   List[Any]					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   Optional[Any]					=      self.config_cls(
           vocab_size=self.vocab_size    ,			d_model=self.hidden_size    ,			encoder_layers=self.num_hidden_layers    ,			decoder_layers=self.num_hidden_layers    ,			encoder_attention_heads=self.num_attention_heads    ,			decoder_attention_heads=self.num_attention_heads    ,			encoder_ffn_dim=self.intermediate_size    ,			decoder_ffn_dim=self.intermediate_size    ,			dropout=self.hidden_dropout_prob    ,			attention_dropout=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			eos_token_ids=[2]    ,			bos_token_id=self.bos_token_id    ,			pad_token_id=self.pad_token_id    ,			decoder_start_token_id=self.pad_token_id    ,			**self.config_updates    ,			)
       lowercase__:   Optional[int]					=      prepare_mbart_inputs_dict(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       return config, inputs_dict






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      TFMBartModel(config=_UpperCAmelCase							).get_decoder()
       lowercase__:   Tuple					=      inputs_dict['''input_ids''']

       lowercase__:   Optional[Any]					=      input_ids[:1, :]
       lowercase__:   Optional[int]					=      inputs_dict['''attention_mask'''][:1, :]
       lowercase__:   List[str]					=      inputs_dict['''head_mask''']
       lowercase__:   Optional[int]					=      1

       # first forward pass
       lowercase__:   List[Any]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			use_cache=_UpperCAmelCase							)

       lowercase__, lowercase__:   Any					=      outputs.to_tuple()
       lowercase__:   List[str]					=      past_key_values[1]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    )  ->  int:
   if attention_mask is None:
      lowercase__:   Union[str, Any]					=      tf.cast(tf.math.not_equal(__UpperCAmelCase       ,    config.pad_token_id )       ,    tf.inta )
   if decoder_attention_mask is None:
      lowercase__:   List[str]					=      tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape       ,    dtype=tf.inta ),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,    config.pad_token_id )       ,    tf.inta ),
          ]       ,    axis=-1       ,    )
   if head_mask is None:
      lowercase__:   str					=      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
   if decoder_head_mask is None:
      lowercase__:   List[str]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   if cross_attn_head_mask is None:
      lowercase__:   Union[str, Any]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    _UpperCAmelCase	:List[Any]						    = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    _UpperCAmelCase	:List[str]						    = (
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    _UpperCAmelCase	:str						    = True
    _UpperCAmelCase	:List[Any]						    = False
    _UpperCAmelCase	:str						    = False






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
          # Exception encountered when calling layer '...'
          return True

       return False






    def _snake_case			(   self							):
       lowercase__:   Tuple					=      TFMBartModelTester(self							)
       lowercase__:   Optional[Any]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase							)






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






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase							)



@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = [
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    _UpperCAmelCase	:Any						    = [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    _UpperCAmelCase	:Tuple						    = "facebook/mbart-large-en-ro"






    @cached_property
    def _snake_case			(   self							):
       return AutoTokenizer.from_pretrained(self.model_name							)






    @cached_property
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       return model






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      self.translate_src_text(**_UpperCAmelCase							)
       self.assertListEqual(self.expected_text    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   str					=      self.tokenizer(self.src_text    ,			**_UpperCAmelCase    ,			return_tensors='''tf'''							)
       lowercase__:   Optional[int]					=      self.model.generate(
           model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2							)
       lowercase__:   Tuple					=      self.tokenizer.batch_decode(_UpperCAmelCase    ,			skip_special_tokens=_UpperCAmelCase							)
       return generated_words






    @slow
    def _snake_case			(   self							):
       self._assert_generated_batch_equal_expected()
