



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowercase:					List[str]      =						logging.get_logger(__name__)

_lowercase:					str      =						{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				"m2m_100"
	__A		      =				["past_key_values"]
	__A		      =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




	def __init__(self						,			lowerCamelCase_=128112						,			lowerCamelCase_=1024						,			lowerCamelCase_=12						,			lowerCamelCase_=4096						,			lowerCamelCase_=16						,			lowerCamelCase_=12						,			lowerCamelCase_=4096						,			lowerCamelCase_=16						,			lowerCamelCase_=0.05						,			lowerCamelCase_=0.05						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_="relu"						,			lowerCamelCase_=1024						,			lowerCamelCase_=0.1						,			lowerCamelCase_=0.1						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.02						,			lowerCamelCase_=2						,			lowerCamelCase_=True						,			lowerCamelCase_=1						,			lowerCamelCase_=0						,			lowerCamelCase_=2						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						a   	=  vocab_size
						a   	=  max_position_embeddings
						a   	=  d_model
						a   	=  encoder_ffn_dim
						a   	=  encoder_layers
						a   	=  encoder_attention_heads
						a   	=  decoder_ffn_dim
						a   	=  decoder_layers
						a   	=  decoder_attention_heads
						a   	=  dropout
						a   	=  attention_dropout
						a   	=  activation_dropout
						a   	=  activation_function
						a   	=  init_std
						a   	=  encoder_layerdrop
						a   	=  decoder_layerdrop
						a   	=  use_cache
						a   	=  encoder_layers
						a   	=  scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=lowerCamelCase_						,			bos_token_id=lowerCamelCase_						,			eos_token_id=lowerCamelCase_						,			is_encoder_decoder=lowerCamelCase_						,			decoder_start_token_id=lowerCamelCase_						,			**lowerCamelCase_						,			)




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  OrderedDict(
						    [
						        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
						        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
						    ]						)

						if self.use_past:
											a   	=  {0: 'batch'}
											a   	=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											a   	=  {0: 'batch', 1: 'decoder_sequence'}
											a   	=  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase_						,			direction="inputs"						)
						return common_inputs




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = -1						,			lowerCamelCase_ = -1						,			lowerCamelCase_ = False						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  compute_effective_axis_dimension(
						    lowerCamelCase_						,			fixed_dimension=OnnxConfig.default_fixed_batch						,			num_token_to_add=0						)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						a   	=  tokenizer.num_special_tokens_to_add(lowerCamelCase_						)
						a   	=  compute_effective_axis_dimension(
						    lowerCamelCase_						,			fixed_dimension=OnnxConfig.default_fixed_sequence						,			num_token_to_add=lowerCamelCase_						)

						# Generate dummy inputs according to compute batch and sequence
						a   	=  [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
						a   	=  dict(tokenizer(lowerCamelCase_						,			return_tensors=lowerCamelCase_						)						)
						return common_inputs




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = -1						,			lowerCamelCase_ = -1						,			lowerCamelCase_ = False						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						)

						# Generate decoder inputs
						a   	=  seq_length if not self.use_past else 1
						a   	=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						)
						a   	=  {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						a   	=  dict(**lowerCamelCase_						,			**lowerCamelCase_						)

						if self.use_past:
											if not is_torch_available():
																raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
											else:
																import torch
											a   	=  common_inputs['input_ids'].shape
											a   	=  common_inputs['decoder_input_ids'].shape[1]
											a   	=  self.num_attention_heads
											a   	=  (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											a   	=  decoder_seq_length + 3
											a   	=  (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											a   	=  torch.cat(
											    [common_inputs["decoder_attention_mask"], torch.ones(lowerCamelCase_						,			lowerCamelCase_						)]						,			dim=1						)

											a   	=  []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											a   	=  self.num_layers
											a   	=  min(lowerCamelCase_						,			lowerCamelCase_						)
											a   	=  max(lowerCamelCase_						,			lowerCamelCase_						) - min_num_layers
											a   	=  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

											for _ in range(lowerCamelCase_						):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(lowerCamelCase_						),
																        torch.zeros(lowerCamelCase_						),
																        torch.zeros(lowerCamelCase_						),
																        torch.zeros(lowerCamelCase_						),
																    )						)
											# TODO: test this.
											a   	=  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
											for _ in range(lowerCamelCase_						,			lowerCamelCase_						):
																common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_						), torch.zeros(lowerCamelCase_						))						)
						return common_inputs
	__A		      =				_generate_dummy_inputs_for_default_and_seqaseq_lm








"""simple docstring"""


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 		snake_case    :





    def __init__(				self      :   Union[str, Any]							,						A      :   Optional[Any]							,						):


      '''simple docstring'''




      a	:     Union[str, Any]							=  parent
      a	:     str							=  1_3
      a	:     Optional[int]							=  7
      a	:     List[str]							=  True
      a	:     int							=  True
      a	:     Optional[Any]							=  True
      a	:     Tuple							=  True
      a	:     List[str]							=  True
      a	:     Union[str, Any]							=  False
      a	:     Optional[Any]							=  False
      a	:     List[str]							=  False
      a	:     Union[str, Any]							=  2
      a	:     Optional[int]							=  9_9
      a	:     Optional[Any]							=  0
      a	:     Tuple							=  3_2
      a	:     Union[str, Any]							=  2
      a	:     Optional[Any]							=  4
      a	:     Optional[int]							=  0.1
      a	:     List[str]							=  0.1
      a	:     int							=  5_1_2
      a	:     List[Any]							=  1_6
      a	:     Dict							=  2
      a	:     List[str]							=  0.02
      a	:     str							=  3
      a	:     Optional[Any]							=  4
      a	:     Optional[Any]							=  'last'
      a	:     Any							=  True
      a	:     Union[str, Any]							=  None
      a	:     int							=  0





    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      a	:     Dict							=  ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size     )
      a	:     Dict							=  random_attention_mask([self.batch_size, self.seq_length]							,						dtype=tf.floataa     )

      a	:     Union[str, Any]							=  None
      if self.use_input_lengths:
        a	:     List[str]							=  (
            ids_tensor([self.batch_size]							,						vocab_size=2     ) + self.seq_length - 2
        )  # small variation of seq_length

      a	:     Optional[Any]							=  None
      if self.use_token_type_ids:
        a	:     str							=  ids_tensor([self.batch_size, self.seq_length]							,						self.n_langs     )

      a	:     Optional[int]							=  None
      a	:     Optional[Any]							=  None
      a	:     Tuple							=  None
      if self.use_labels:
        a	:     Union[str, Any]							=  ids_tensor([self.batch_size]							,						self.type_sequence_label_size     )
        a	:     Any							=  ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels     )
        a	:     Dict							=  ids_tensor([self.batch_size]							,						2							,						dtype=tf.floataa     )
        a	:     Tuple							=  ids_tensor([self.batch_size]							,						self.num_choices     )

      a	:     str							=  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      :   List[str]							,						A      :   Union[str, Any]							,						A      :   Optional[Any]							,						A      :   List[str]							,						A      :   List[Any]							,						A      :   Union[str, Any]							,						A      :   Dict							,						A      :   Optional[int]							,						A      :   List[Any]							,						A      :   Optional[int]							,						):


      '''simple docstring'''




      a	:     int							=  TFXLMModel(config=A     )
      a	:     Optional[Any]							=  {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
      a	:     Dict							=  model(A     )

      a	:     List[str]							=  [input_ids, input_mask]
      a	:     Union[str, Any]							=  model(A     )
      self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)     )





    def 							lowerCamelCase__ (				self      :   str							,						A      :   List[Any]							,						A      :   List[Any]							,						A      :   int							,						A      :   int							,						A      :   Any							,						A      :   List[Any]							,						A      :   Tuple							,						A      :   str							,						A      :   List[str]							,						):


      '''simple docstring'''




      a	:     Union[str, Any]							=  TFXLMWithLMHeadModel(A     )

      a	:     str							=  {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
      a	:     List[str]							=  model(A     )

      a	:     Union[str, Any]							=  outputs

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





    def 							lowerCamelCase__ (				self      :   List[str]							,						A      :   int							,						A      :   List[str]							,						A      :   Tuple							,						A      :   Dict							,						A      :   Dict							,						A      :   str							,						A      :   Union[str, Any]							,						A      :   Optional[Any]							,						A      :   Union[str, Any]							,						):


      '''simple docstring'''




      a	:     Any							=  TFXLMForQuestionAnsweringSimple(A     )

      a	:     Optional[Any]							=  {'input_ids': input_ids, 'lengths': input_lengths}

      a	:     Any							=  model(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      :   Optional[int]							,						A      :   Optional[int]							,						A      :   Dict							,						A      :   int							,						A      :   Any							,						A      :   List[Any]							,						A      :   str							,						A      :   List[Any]							,						A      :   Dict							,						A      :   List[str]							,						):


      '''simple docstring'''




      a	:     Dict							=  TFXLMForSequenceClassification(A     )

      a	:     Optional[int]							=  {'input_ids': input_ids, 'lengths': input_lengths}

      a	:     Tuple							=  model(A     )

      self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size)     )





    def 							lowerCamelCase__ (				self      :   int							,						A      :   Optional[Any]							,						A      :   str							,						A      :   Dict							,						A      :   Optional[Any]							,						A      :   List[str]							,						A      :   List[str]							,						A      :   List[Any]							,						A      :   List[Any]							,						A      :   str							,						):


      '''simple docstring'''




      a	:     Any							=  self.num_labels
      a	:     int							=  TFXLMForTokenClassification(config=A     )
      a	:     Union[str, Any]							=  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
      a	:     int							=  model(A     )
      self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)     )





    def 							lowerCamelCase__ (				self      :   Optional[Any]							,						A      :   Any							,						A      :   Optional[Any]							,						A      :   List[Any]							,						A      :   Dict							,						A      :   List[Any]							,						A      :   Any							,						A      :   List[Any]							,						A      :   List[str]							,						A      :   str							,						):


      '''simple docstring'''




      a	:     Tuple							=  self.num_choices
      a	:     List[str]							=  TFXLMForMultipleChoice(config=A     )
      a	:     List[str]							=  tf.tile(tf.expand_dims(A							,						1     )							,						(1, self.num_choices, 1)     )
      a	:     str							=  tf.tile(tf.expand_dims(A							,						1     )							,						(1, self.num_choices, 1)     )
      a	:     Union[str, Any]							=  tf.tile(tf.expand_dims(A							,						1     )							,						(1, self.num_choices, 1)     )
      a	:     str							=  {
          'input_ids': multiple_choice_inputs_ids,
          'attention_mask': multiple_choice_input_mask,
          'token_type_ids': multiple_choice_token_type_ids,
      }
      a	:     str							=  model(A     )
      self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices)     )





    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      a	:     Union[str, Any]							=  self.prepare_config_and_inputs()
      (
          (
          a
      ),	(
          a
      ),	(
          a
      ),	(
          a
      ),	(
          a
      ),	(
          a
      ),	(
          a
      ),	(
          a
      ),	(
          a
      ),	
      )	:     Dict							=  config_and_inputs
      a	:     Tuple							=  {
          'input_ids': input_ids,
          'token_type_ids': token_type_ids,
          'langs': token_type_ids,
          'lengths': input_lengths,
      }
      return config, inputs_dict






@require_tf
class 		snake_case    (    UpperCAmelCase  ,					UpperCAmelCase  ,					unittest.TestCase			):
    __magic_name__       	=	(
        (
            TFXLMModel,
            TFXLMWithLMHeadModel,
            TFXLMForSequenceClassification,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForTokenClassification,
            TFXLMForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __magic_name__       	=	(
        (TFXLMWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    __magic_name__       	=	(
        {
            '''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 {}
    )
    __magic_name__       	=	False
    __magic_name__       	=	False





    def 							lowerCamelCase__ (				self      :   Any							,						A      :   List[Any]							,						A      :   Optional[Any]							,						A      :   str							,						A      :   Optional[Any]							,						A      :   List[str]     ):


      '''simple docstring'''




      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      :   int     ):


      '''simple docstring'''




      a	:     str							=  TFXLMModelTester(self     )
      a	:     int							=  ConfigTester(self							,						config_class=A							,						emb_dim=3_7     )





    def 							lowerCamelCase__ (				self      :   str     ):


      '''simple docstring'''




      self.config_tester.run_common_tests()





    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      a	:     Optional[int]							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_model(*A     )





    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_lm_head(*A     )





    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




      a	:     Optional[int]							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_qa(*A     )





    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      a	:     List[str]							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_sequence_classif(*A     )





    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      a	:     str							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_for_token_classification(*A     )





    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      a	:     Tuple							=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_for_multiple_choice(*A     )





    @slow
    def 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a	:     Any							=  TFXLMModel.from_pretrained(A     )
        self.assertIsNotNone(A     )






@require_tf
class 		snake_case    (    unittest.TestCase			):





    @slow
    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      a	:     List[Any]							=  TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'     )
      a	:     Optional[int]							=  tf.convert_to_tensor([[1_4, 4_4_7]]							,						dtype=tf.intaa     )  # the president
      a	:     List[Any]							=  [
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
          1_4,
          4_4_7,
      ]  # 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
      a	:     Optional[Any]							=  model.generate(A							,						do_sample=A     )
      self.assertListEqual(output_ids[0].numpy().tolist()							,						A     )




