




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 a_ 		=  "platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


    if attention_mask is None:
        __UpperCamelCase			=    np.where(input_ids != config.pad_token_id       ,  1       ,  0   )
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }

@require_flax
class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Tuple			,							snake_case       :			Optional[Any]			,							snake_case       :			Union[str, Any]=13			,							snake_case       :			int=7			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			Any=False			,							snake_case       :			List[str]=99			,							snake_case       :			Union[str, Any]=16			,							snake_case       :			Union[str, Any]=2			,							snake_case       :			Any=4			,							snake_case       :			int=4			,							snake_case       :			str="gelu"			,							snake_case       :			Optional[int]=0.1			,							snake_case       :			int=0.1			,							snake_case       :			Any=20			,							snake_case       :			int=2			,							snake_case       :			Optional[Any]=1			,							snake_case       :			int=0			,							snake_case       :			str=16			,							snake_case       :			Any=16			,							snake_case       :			Optional[Any]=0.02			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    seq_length
          __UpperCamelCase			=    is_training
          __UpperCamelCase			=    use_labels
          __UpperCamelCase			=    vocab_size
          __UpperCamelCase			=    hidden_size
          __UpperCamelCase			=    num_hidden_layers
          __UpperCamelCase			=    num_attention_heads
          __UpperCamelCase			=    intermediate_size
          __UpperCamelCase			=    hidden_act
          __UpperCamelCase			=    hidden_dropout_prob
          __UpperCamelCase			=    attention_probs_dropout_prob
          __UpperCamelCase			=    max_position_embeddings
          __UpperCamelCase			=    eos_token_id
          __UpperCamelCase			=    pad_token_id
          __UpperCamelCase			=    bos_token_id
          __UpperCamelCase			=    embed_dim
          __UpperCamelCase			=    word_embed_proj_dim
          __UpperCamelCase			=    initializer_range
          __UpperCamelCase			=    False







      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,							self.vocab_size					)			,							3			,							self.vocab_size					)
          __UpperCamelCase			=    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,							dtype=np.intaa					))			,							-1					)

          __UpperCamelCase			=    OPTConfig(
              vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							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_id=self.eos_token_id			,							bos_token_id=self.bos_token_id			,							pad_token_id=self.pad_token_id			,							embed_dim=self.embed_dim			,							is_encoder_decoder=snake_case			,							word_embed_proj_dim=self.word_embed_proj_dim			,							initializer_range=self.initializer_range			,							use_cache=snake_case			,							)
          __UpperCamelCase			=    prepare_opt_inputs_dict(snake_case			,							snake_case					)
          return config, inputs_dict







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.prepare_config_and_inputs()
          return config, inputs_dict







      def    snake_case						(  self       :			Tuple			,							snake_case       :			int			,							snake_case       :			int			,							snake_case       :			Any					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase			=    inputs_dict['''input_ids''']
          __UpperCamelCase			=    inputs_dict['''attention_mask''']

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.ones((input_ids.shape[0], max_length)			,							dtype='''i4'''					)

          __UpperCamelCase			=    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,							(input_ids.shape[0], input_ids.shape[-1] - 1)			,							)
          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							attention_mask=snake_case			,							past_key_values=outputs_cache.past_key_values			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case					)

          __UpperCamelCase			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
          self.parent.assertTrue(diff < 1E-3			,							msg=F"Max diff is {diff}"					)







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			Optional[Any]			,							snake_case       :			Any			,							snake_case       :			int					):
          __UpperCamelCase			=    20
          __UpperCamelCase			=    model_class_name(snake_case					)

          __UpperCamelCase		,   __UpperCamelCase			=    (
              inputs_dict['''input_ids'''],
              inputs_dict['''attention_mask'''],
          )

          __UpperCamelCase			=    jnp.concatenate(
              [
                  attention_mask,
                  jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])					),
              ]			,							axis=-1			,							)

          __UpperCamelCase			=    model.init_cache(input_ids.shape[0]			,							snake_case					)
          __UpperCamelCase			=    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1					)[None, :]			,							(input_ids.shape[0], input_ids.shape[-1] - 1)			,							)

          __UpperCamelCase			=    model(
              input_ids[:, :-1]			,							attention_mask=snake_case			,							past_key_values=snake_case			,							position_ids=snake_case			,							)
          __UpperCamelCase			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,							dtype='''i4'''					)
          __UpperCamelCase			=    model(
              input_ids[:, -1:]			,							past_key_values=outputs_cache.past_key_values			,							attention_mask=snake_case			,							position_ids=snake_case			,							)

          __UpperCamelCase			=    model(snake_case			,							attention_mask=snake_case					)

          __UpperCamelCase			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
          self.parent.assertTrue(diff < 1E-3			,							msg=F"Max diff is {diff}"					)







@require_flax
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Any     =       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__      :			Optional[int]     =       () if is_flax_available() else ()







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTModelTester(self					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(snake_case			,							snake_case			,							snake_case					)







      def    snake_case						(  self       :			int					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(snake_case			,							snake_case			,							snake_case					)







      @slow
      def    snake_case						(  self       :			Union[str, Any]					):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase			=    model_class_name.from_pretrained('''facebook/opt-125m'''					)
              __UpperCamelCase			=    np.ones((1, 1)					) * model.config.eos_token_id
              __UpperCamelCase			=    model(snake_case					)
              self.assertIsNotNone(snake_case					)







@require_sentencepiece
@require_flax
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @slow
      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    FlaxOPTModel.from_pretrained('''facebook/opt-350m'''					)
          __UpperCamelCase			=    jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
          __UpperCamelCase			=    model(input_ids=snake_case					).last_hidden_state
          __UpperCamelCase			=    (1, 11, 512)
          self.assertEqual(output.shape			,							snake_case					)
          __UpperCamelCase			=    jnp.array(
              [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]					)
          self.assertTrue(jnp.allclose(output[:, :3, :3]			,							snake_case			,							atol=4E-2					)					)







@require_flax
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Optional[Any]					):
          super().setUp()
          __UpperCamelCase			=    '''facebook/opt-350m'''







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(self.path_model					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(self.path_model					)

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of''',
              '''Paris is the capital of France and''',
              '''Computers and mobile phones have taken''',
          ]
          # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case			,							add_special_tokens=snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          __UpperCamelCase			=    jnp.array(
              [
                  [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                  [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                  [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                  [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
              ]					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)

          __UpperCamelCase			=    jax.jit(snake_case					)
          __UpperCamelCase			=    model(inputs.input_ids			,							attention_mask=inputs.attention_mask					)[0].mean(axis=-1					)
          self.assertTrue(jnp.allclose(snake_case			,							snake_case			,							atol=4E-2					)					)







@require_flax
@slow
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      @property
      def    snake_case						(  self       :			Any					):
          return [
              "Today is a beautiful day and I want",
              "In the city of",
              "Paris is the capital of France and",
              "Computers and mobile phones have taken",
          ]







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of New York, the city''',
              '''Paris is the capital of France and the capital''',
              '''Computers and mobile phones have taken over the''',
          ]

          __UpperCamelCase			=    []

          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to''',
              '''In the city of San Francisco, the city''',
              '''Paris is the capital of France and the capital''',
              '''Computers and mobile phones have taken over the''',
          ]

          __UpperCamelCase			=    []
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)

          for prompt in self.prompts:
              __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''					).input_ids

              __UpperCamelCase			=    model.generate(snake_case			,							max_length=10					)
              __UpperCamelCase			=    generated_ids[0]

              __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)
              predicted_outputs += generated_string

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    '''facebook/opt-125m'''
          __UpperCamelCase			=    [
              '''Today is a beautiful day and I want to thank''',
              '''In the city of Rome Canaver Canaver Canaver Canaver''',
          ]
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)
          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    tokenizer(
              [
                  '''Today is a beautiful day and I want to''',
                  '''In the city of''',
              ]			,							return_tensors='''jax'''			,							padding=snake_case			,							)

          __UpperCamelCase			=    jax.jit(model.generate					)

          __UpperCamelCase			=    jit_generate(inputs['''input_ids''']			,							attention_mask=inputs['''attention_mask''']					).sequences

          __UpperCamelCase			=    tokenizer.batch_decode(snake_case			,							skip_special_tokens=snake_case					)

          self.assertIsNotNone(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    '''facebook/opt-350m'''

          __UpperCamelCase			=    GPTaTokenizer.from_pretrained(snake_case					)
          __UpperCamelCase			=    FlaxOPTForCausalLM.from_pretrained(snake_case					)

          __UpperCamelCase			=    '''left'''

          # use different length sentences to test batching
          __UpperCamelCase			=    [
              '''Hello, my dog is a little''',
              '''Today, I''',
          ]

          __UpperCamelCase			=    tokenizer(snake_case			,							return_tensors='''jax'''			,							padding=snake_case					)
          __UpperCamelCase			=    inputs['''input_ids''']

          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							attention_mask=inputs['''attention_mask''']			,							trace=snake_case					)

          __UpperCamelCase			=    tokenizer(sentences[0]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case					)

          __UpperCamelCase			=    inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
          __UpperCamelCase			=    tokenizer(sentences[1]			,							return_tensors='''jax'''					).input_ids
          __UpperCamelCase			=    model.generate(input_ids=snake_case			,							max_length=model.config.max_length - num_paddings					)

          __UpperCamelCase			=    tokenizer.batch_decode(outputs[0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_non_padded[0][0]			,							skip_special_tokens=snake_case					)
          __UpperCamelCase			=    tokenizer.decode(output_padded[0][0]			,							skip_special_tokens=snake_case					)

          __UpperCamelCase			=    [
              '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
              '''Today, I was in the middle of a conversation with a friend about the''',
          ]
          self.assertListEqual(snake_case			,							snake_case					)
          self.assertListEqual(snake_case			,							[non_padded_sentence, padded_sentence]					)











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       _lowerCAmelCase :






			"""simple docstring"""





			def __init__(					self						:							Tuple      ,  lowercase						:							str      ,  )    ->       str:
									"""simple docstring"""







									__lowercase											=					parent
									__lowercase											=					13
									__lowercase											=					7
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					False
									__lowercase											=					False
									__lowercase											=					False
									__lowercase											=					2
									__lowercase											=					99
									__lowercase											=					0
									__lowercase											=					32
									__lowercase											=					2
									__lowercase											=					4
									__lowercase											=					0.1
									__lowercase											=					0.1
									__lowercase											=					512
									__lowercase											=					16
									__lowercase											=					2
									__lowercase											=					0.02
									__lowercase											=					3
									__lowercase											=					4
									__lowercase											=					"""last"""
									__lowercase											=					True
									__lowercase											=					None
									__lowercase											=					0





			def 		snake_case__      (					self						:							Dict )    ->       Tuple:
									"""simple docstring"""







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

									__lowercase											=					None
									if self.use_input_lengths:
															__lowercase											=					(
															    ids_tensor([self.batch_size]      ,  vocab_size=2 ) + self.seq_length - 2
															)  # small variation of seq_length

									__lowercase											=					None
									if self.use_token_type_ids:
															__lowercase											=					ids_tensor([self.batch_size, self.seq_length]      ,  self.n_langs )

									__lowercase											=					None
									__lowercase											=					None
									__lowercase											=					None
									if self.use_labels:
															__lowercase											=					ids_tensor([self.batch_size]      ,  self.type_sequence_label_size )
															__lowercase											=					ids_tensor([self.batch_size, self.seq_length]      ,  self.num_labels )
															__lowercase											=					ids_tensor([self.batch_size]      ,  2      ,  dtype=tf.floataa )
															__lowercase											=					ids_tensor([self.batch_size]      ,  self.num_choices )

									__lowercase											=					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 		snake_case__      (					self						:							List[str]      ,  lowercase						:							Tuple      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							int      ,  lowercase						:							List[str]      ,  )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					TFXLMModel(config=lowercase )
									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase											=					model(lowercase )

									__lowercase											=					[input_ids, input_mask]
									__lowercase											=					model(lowercase )
									self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size) )





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Dict      ,  lowercase						:							Dict      ,  lowercase						:							int      ,  lowercase						:							Union[str, Any]      ,  )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					TFXLMWithLMHeadModel(lowercase )

									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase											=					model(lowercase )

									__lowercase											=					outputs

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





			def 		snake_case__      (					self						:							Optional[Any]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Any      ,  lowercase						:							int      ,  lowercase						:							List[Any]      ,  lowercase						:							Any      ,  lowercase						:							int      ,  lowercase						:							List[Any]      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[Any]      ,  )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					TFXLMForQuestionAnsweringSimple(lowercase )

									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths}

									__lowercase											=					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 		snake_case__      (					self						:							Optional[int]      ,  lowercase						:							Tuple      ,  lowercase						:							List[str]      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Any      ,  lowercase						:							Tuple      ,  lowercase						:							Any      ,  lowercase						:							Dict      ,  )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					TFXLMForSequenceClassification(lowercase )

									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths}

									__lowercase											=					model(lowercase )

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





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[int]      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Tuple      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Tuple      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[Any]      ,  )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.num_labels
									__lowercase											=					TFXLMForTokenClassification(config=lowercase )
									__lowercase											=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									__lowercase											=					model(lowercase )
									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.num_labels) )





			def 		snake_case__      (					self						:							int      ,  lowercase						:							Tuple      ,  lowercase						:							Optional[Any]      ,  lowercase						:							str      ,  lowercase						:							str      ,  lowercase						:							Dict      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							List[str]      ,  lowercase						:							List[str]      ,  )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					self.num_choices
									__lowercase											=					TFXLMForMultipleChoice(config=lowercase )
									__lowercase											=					tf.tile(tf.expand_dims(lowercase      ,  1 )      ,  (1, self.num_choices, 1) )
									__lowercase											=					tf.tile(tf.expand_dims(lowercase      ,  1 )      ,  (1, self.num_choices, 1) )
									__lowercase											=					tf.tile(tf.expand_dims(lowercase      ,  1 )      ,  (1, self.num_choices, 1) )
									__lowercase											=					{
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									    """token_type_ids""": multiple_choice_token_type_ids,
									}
									__lowercase											=					model(lowercase )
									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_choices) )







			def 		snake_case__      (					self						:							str )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					self.prepare_config_and_inputs()
									(
									    (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      
									)											=					config_and_inputs
									__lowercase											=					{
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """langs""": token_type_ids,
									    """lengths""": input_lengths,
									}
									return config, inputs_dict





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






			"""simple docstring"""





			lowercase__   :	Tuple  			=      (
			    (
			        TFXLMModel,
			        TFXLMWithLMHeadModel,
			        TFXLMForSequenceClassification,
			        TFXLMForQuestionAnsweringSimple,
			        TFXLMForTokenClassification,
			        TFXLMForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowercase__   :	List[str]  			=      (
			    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Check other models whether language generation is also applicable
			lowercase__   :	Tuple  			=      (
			    {
			        """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 {}
			)
			lowercase__   :	int  			=      False
			lowercase__   :	int  			=      False





			def 		snake_case__      (					self						:							Optional[int]      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							List[str]      ,  lowercase						:							Dict )    ->       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 		snake_case__      (					self						:							Tuple )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					TFXLMModelTester(self )
									__lowercase											=					ConfigTester(self      ,  config_class=lowercase      ,  emb_dim=37 )





			def 		snake_case__      (					self						:							List[str] )    ->       List[str]:
									"""simple docstring"""







									self.config_tester.run_common_tests()





			def 		snake_case__      (					self						:							List[Any] )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_model(*lowercase )





			def 		snake_case__      (					self						:							str )    ->       str:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_lm_head(*lowercase )





			def 		snake_case__      (					self						:							Optional[int] )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_qa(*lowercase )





			def 		snake_case__      (					self						:							Optional[Any] )    ->       str:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_sequence_classif(*lowercase )





			def 		snake_case__      (					self						:							Union[str, Any] )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_token_classification(*lowercase )





			def 		snake_case__      (					self						:							Optional[Any] )    ->       Tuple:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase )







			@slow
			def 		snake_case__      (					self						:							Optional[int] )    ->       Optional[int]:
									"""simple docstring"""







									for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowercase											=					TFXLMModel.from_pretrained(lowercase )
															self.assertIsNotNone(lowercase )





@require_tf
class       _lowerCAmelCase (			unittest.TestCase      ):






			"""simple docstring"""





			@slow
			def 		snake_case__      (					self						:							int )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
									__lowercase											=					tf.convert_to_tensor([[14, 447]]      ,  dtype=tf.intaa )  # the president
									__lowercase											=					[
									    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
									__lowercase											=					model.generate(lowercase      ,  do_sample=lowercase )
									self.assertListEqual(output_ids[0].numpy().tolist()      ,  lowercase )





