
'''simple docstring'''
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 			SCREAMING_SNAKE_CASE__      :
				lowerCAmelCase_     			=							XGLMConfig
				lowerCAmelCase_     			=							{}
				lowerCAmelCase_     			=							"""gelu"""







				def __init__(       self						,				A_						,				A_=14						,				A_=7						,				A_=True						,				A_=True						,				A_=True						,				A_=99						,				A_=32						,				A_=2						,				A_=4						,				A_=37						,				A_="gelu"						,				A_=0.1						,				A_=0.1						,				A_=512						,				A_=0.02						,				)->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     seq_length
											UpperCamelCase   			=     is_training
											UpperCamelCase   			=     use_input_mask
											UpperCamelCase   			=     use_labels
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     ffn_dim
											UpperCamelCase   			=     activation_function
											UpperCamelCase   			=     activation_dropout
											UpperCamelCase   			=     attention_dropout
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     None
											UpperCamelCase   			=     0
											UpperCamelCase   			=     2
											UpperCamelCase   			=     1







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											return XGLMConfig.from_pretrained('facebook/xglm-564M'     )







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     tf.clip_by_value(
											    ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size     )						,				clip_value_min=0						,				clip_value_max=3     )

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

											UpperCamelCase   			=     self.get_config()

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

											return (
											    config,
											    input_ids,
											    input_mask,
											    head_mask,
											)







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											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=A_						,				bos_token_id=self.bos_token_id						,				eos_token_id=self.eos_token_id						,				pad_token_id=self.pad_token_id						,				return_dict=A_						,				)







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     self.prepare_config_and_inputs()

											(
											    (
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	(
											    UpperCamelCase
											)			,	
											)   			=     config_and_inputs

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

											return config, inputs_dict


@require_tf
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				lowerCAmelCase_     			=							(TFXGLMForCausalLM,) if is_tf_available() else ()
				lowerCAmelCase_     			=							(
				    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							False







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     TFXGLMModelTester(self     )
											UpperCamelCase   			=     ConfigTester(self						,				config_class=A_						,				n_embd=37     )







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.config_tester.run_common_tests()







				@slow
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCamelCase   			=     TFXGLMModel.from_pretrained(A_     )
																		self.assertIsNotNone(A_     )







				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											super().test_resize_token_embeddings()


@require_tf
class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				@slow
				def 						UpperCAmelCase_    (       self						,				A_=True     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )
											UpperCamelCase   			=     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
											UpperCamelCase   			=     [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
											# fmt: on
											UpperCamelCase   			=     model.generate(A_						,				do_sample=A_						,				num_beams=1     )
											if verify_outputs:
																		self.assertListEqual(output_ids[0].numpy().tolist()						,				A_     )







				@slow
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     XGLMTokenizer.from_pretrained('facebook/xglm-564M'     )
											UpperCamelCase   			=     TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )

											tf.random.set_seed(0     )
											UpperCamelCase   			=     tokenizer('Today is a nice day and'						,				return_tensors='tf'     )
											UpperCamelCase   			=     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'     ):
																		UpperCamelCase   			=     model.generate(A_						,				do_sample=A_						,				seed=[7, 0]     )
											UpperCamelCase   			=     tokenizer.decode(output_ids[0]						,				skip_special_tokens=A_     )

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







				@slow
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )
											UpperCamelCase   			=     XGLMTokenizer.from_pretrained('facebook/xglm-564M'     )

											UpperCamelCase   			=     'left'

											# use different length sentences to test batching
											UpperCamelCase   			=     [
											    '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',
											]

											UpperCamelCase   			=     tokenizer(A_						,				return_tensors='tf'						,				padding=A_     )
											UpperCamelCase   			=     inputs['input_ids']

											UpperCamelCase   			=     model.generate(input_ids=A_						,				attention_mask=inputs['attention_mask']						,				max_new_tokens=12     )

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

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

											UpperCamelCase   			=     tokenizer.batch_decode(A_						,				skip_special_tokens=A_     )
											UpperCamelCase   			=     tokenizer.decode(output_non_padded[0]						,				skip_special_tokens=A_     )
											UpperCamelCase   			=     tokenizer.decode(output_padded[0]						,				skip_special_tokens=A_     )

											UpperCamelCase   			=     [
											    '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(A_						,				A_     )
											self.assertListEqual(A_						,				[non_padded_sentence, padded_sentence]     )









"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    'laion/clap-htsat-unfused'
						_lowerCamelCase					:					Optional[Any]	       =    tempfile.mkdtemp()
			def    _lowerCAmelCase		(	self				,   **A							):
						return RobertaTokenizer.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self				,   **A							):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self							):
						shutil.rmtree(self.tmpdirname							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()

						_lowerCamelCase					:					List[str]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						processor.save_pretrained(self.tmpdirname							)
						_lowerCamelCase					:					str	       =    ClapProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer				,   A							)

						self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						_lowerCamelCase					:					Tuple	       =    self.get_tokenizer(bos_token='(BOS)'				,   eos_token='(EOS)'							)
						_lowerCamelCase					:					str	       =    self.get_feature_extractor(do_normalize=A				,   padding_value=1.0							)

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor.from_pretrained(
						    self.tmpdirname				,   bos_token='(BOS)'				,   eos_token='(EOS)'				,   do_normalize=A				,   padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer				,   A							)

						self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[int]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Any	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					int	       =    floats_list((3, 1000)							)

						_lowerCamelCase					:					str	       =    feature_extractor(A				,   return_tensors='np'							)
						_lowerCamelCase					:					List[str]	       =    processor(audios=A				,   return_tensors='np'							)

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()				,   input_processor[key].sum()				,   delta=1E-2							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    self.get_feature_extractor()
						_lowerCamelCase					:					str	       =    self.get_tokenizer()

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					List[str]	       =    'This is a test string'

						_lowerCamelCase					:					Any	       =    processor(text=A							)

						_lowerCamelCase					:					Optional[int]	       =    tokenizer(A							)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Optional[int]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					Dict	       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_lowerCamelCase					:					int	       =    processor.batch_decode(A							)
						_lowerCamelCase					:					int	       =    tokenizer.batch_decode(A							)

						self.assertListEqual(A				,   A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						self.assertListEqual(
						    processor.model_input_names[2:]				,   feature_extractor.model_input_names				,   msg='`processor` and `feature_extractor` model input names do not match'				,   )





