

from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers.models.xglm.modeling_tf_xglm import (
					    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFXGLMForCausalLM,
					    TFXGLMModel,
					)







@require_tf
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      XGLMConfig
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      {}
				__SCREAMING_SNAKE_CASE :			Tuple	      =      '''gelu'''




				def __init__( self				,       snake_case				,       snake_case=14				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=99				,       snake_case=32				,       snake_case=2				,       snake_case=4				,       snake_case=37				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=0.02				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   seq_length
											snake_case_          =   is_training
											snake_case_          =   use_input_mask
											snake_case_          =   use_labels
											snake_case_          =   vocab_size
											snake_case_          =   d_model
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   ffn_dim
											snake_case_          =   activation_function
											snake_case_          =   activation_dropout
											snake_case_          =   attention_dropout
											snake_case_          =   max_position_embeddings
											snake_case_          =   initializer_range
											snake_case_          =   None
											snake_case_          =   0
											snake_case_          =   2
											snake_case_          =   1




				def    a							( self	):
											return XGLMConfig.from_pretrained('facebook/xglm-564M'	)




				def    a							( self	):
											snake_case_          =   tf.clip_by_value(
											    ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size	)				,       clip_value_min=0				,       clip_value_max=3	)

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

											snake_case_          =   self.get_config()

											snake_case_          =   floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,       2	)

											return (
											    config,
											    input_ids,
											    input_mask,
											    head_mask,
											)




				def    a							( self	):
											return XGLMConfig(
											    vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       num_layers=self.num_hidden_layers				,       attention_heads=self.num_attention_heads				,       ffn_dim=self.ffn_dim				,       activation_function=self.activation_function				,       activation_dropout=self.activation_dropout				,       attention_dropout=self.attention_dropout				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       use_cache=snake_case				,       bos_token_id=self.bos_token_id				,       eos_token_id=self.eos_token_id				,       pad_token_id=self.pad_token_id				,       return_dict=snake_case				,       )




				def    a							( self	):
											snake_case_          =   self.prepare_config_and_inputs()

											(
											    (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   
											)          =   config_and_inputs

											snake_case_          =   {
											    'input_ids': input_ids,
											    'head_mask': head_mask,
											}

											return config, inputs_dict







@require_tf
class 			lowercase    (	lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (TFXGLMForCausalLM,) if is_tf_available() else ()
				__SCREAMING_SNAKE_CASE :			List[str]	      =      (
				    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
				)
				__SCREAMING_SNAKE_CASE :			Any	      =      False
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      False
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      False




				def    a							( self	):
											snake_case_          =   TFXGLMModelTester(self	)
											snake_case_          =   ConfigTester(self				,       config_class=snake_case				,       n_embd=37	)




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




				@slow
				def    a							( self	):
											for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_          =   TFXGLMModel.from_pretrained(snake_case	)
																		self.assertIsNotNone(snake_case	)




				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'	)
				def    a							( self	):
											super().test_resize_token_embeddings()







@require_tf
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self				,       snake_case=True	):
											snake_case_          =   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
											snake_case_          =   tf.convert_to_tensor([[2, 268, 9865]]				,       dtype=tf.intaa	)  # The dog
											# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
											# fmt: off
											snake_case_          =   [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
											# fmt: on
											snake_case_          =   model.generate(snake_case				,       do_sample=snake_case				,       num_beams=1	)
											if verify_outputs:
																		self.assertListEqual(output_ids[0].numpy().tolist()				,       snake_case	)




				@slow
				def    a							( self	):
											snake_case_          =   XGLMTokenizer.from_pretrained('facebook/xglm-564M'	)
											snake_case_          =   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)

											tf.random.set_seed(0	)
											snake_case_          =   tokenizer('Today is a nice day and'				,       return_tensors='tf'	)
											snake_case_          =   tokenized.input_ids
											# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
											with tf.device(':/CPU:0'	):
																		snake_case_          =   model.generate(snake_case				,       do_sample=snake_case				,       seed=[7, 0]	)
											snake_case_          =   tokenizer.decode(output_ids[0]				,       skip_special_tokens=snake_case	)

											snake_case_          =   (
											    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
											)
											self.assertEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											snake_case_          =   TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
											snake_case_          =   XGLMTokenizer.from_pretrained('facebook/xglm-564M'	)

											snake_case_          =   'left'

											# use different length sentences to test batching
											snake_case_          =   [
											    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
											    'left-padding, such as in batched generation. The output for the sequence below should be the same '
											    'regardless of whether left padding is applied or not. When',
											    'Hello, my dog is a little',
											]

											snake_case_          =   tokenizer(snake_case				,       return_tensors='tf'				,       padding=snake_case	)
											snake_case_          =   inputs['input_ids']

											snake_case_          =   model.generate(input_ids=snake_case				,       attention_mask=inputs['attention_mask']				,       max_new_tokens=12	)

											snake_case_          =   tokenizer(sentences[0]				,       return_tensors='tf'	).input_ids
											snake_case_          =   model.generate(input_ids=snake_case				,       max_new_tokens=12	)

											snake_case_          =   tokenizer(sentences[1]				,       return_tensors='tf'	).input_ids
											snake_case_          =   model.generate(input_ids=snake_case				,       max_new_tokens=12	)

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

											snake_case_          =   [
											    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
											    'left-padding, such as in batched generation. The output for the sequence below should be the same '
											    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
											    'a single',
											    'Hello, my dog is a little bit of a shy one, but he is very friendly',
											]
											self.assertListEqual(snake_case				,       snake_case	)
											self.assertListEqual(snake_case				,       [non_padded_sentence, padded_sentence]	)







import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			str	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      True




				def    a							( self	):
											super().setUp()

											snake_case_          =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=snake_case	)
											snake_case_          =   tokenizer




				def    a							( self	):
											snake_case_          =   '<pad>'
											snake_case_          =   1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case	)				,       snake_case	)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case	)				,       snake_case	)




				def    a							( self	):
											snake_case_          =   list(self.get_tokenizer().get_vocab().keys()	)

											self.assertEqual(vocab_keys[0]				,       '<s>'	)
											self.assertEqual(vocab_keys[1]				,       '<pad>'	)
											self.assertEqual(vocab_keys[-1]				,       '<mask>'	)
											self.assertEqual(len(snake_case	)				,       10_1122	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       10_1122	)




				@require_torch
				def    a							( self	):
											snake_case_          =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   self.tokenizer(
											    snake_case				,       max_length=len(snake_case	)				,       padding=snake_case				,       truncation=snake_case				,       return_tensors='pt'	)
											self.assertIsInstance(snake_case				,       snake_case	)

											self.assertEqual((2, 6)				,       batch.input_ids.shape	)
											self.assertEqual((2, 6)				,       batch.attention_mask.shape	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




				def    a							( self	):
											if not self.test_rust_tokenizer:
																		return

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

											snake_case_          =   'I was born in 92000, and this is falsé.'

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											snake_case_          =   [
											    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
											    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
											    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
											    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
											    'telles que la traduction et la synthèse de texte.',
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=snake_case				,       model_name='moussaKam/mbarthez'				,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'				,       sequences=snake_case				,       )




