




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class 							__SCREAMING_SNAKE_CASE				(						unittest.TestCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				tempfile.mkdtemp()

						lowerCamelCase_				:						List[Any]				  =				CLIPImageProcessor()
						lowerCamelCase_				:						Union[str, Any]				  =				BertTokenizer.from_pretrained(
						    'hf-internal-testing/tiny-random-BertModel'					,			model_input_names=['input_ids', 'attention_mask']   )

						lowerCamelCase_				:						Tuple				  =				GitProcessor(__lowercase					,			__lowercase   )

						processor.save_pretrained(self.tmpdirname   )

					def       lowercase_				(							self					,			**__lowercase   ):







						'''simple docstring'''




						return AutoProcessor.from_pretrained(self.tmpdirname					,			**__lowercase   ).tokenizer

					def       lowercase_				(							self					,			**__lowercase   ):







						'''simple docstring'''




						return AutoProcessor.from_pretrained(self.tmpdirname					,			**__lowercase   ).image_processor

					def       lowercase_				(							self   ):







						'''simple docstring'''




						shutil.rmtree(self.tmpdirname   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				[np.random.randint(255					,			size=(3, 30, 400)					,			dtype=np.uinta   )]

						lowerCamelCase_				:						Dict				  =				[Image.fromarray(np.moveaxis(__lowercase					,			0					,			-1   )   ) for x in image_inputs]

						return image_inputs

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				GitProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()   )
						processor.save_pretrained(self.tmpdirname   )

						lowerCamelCase_				:						str				  =				self.get_tokenizer(bos_token='(BOS)'					,			eos_token='(EOS)'   )
						lowerCamelCase_				:						Any				  =				self.get_image_processor(do_normalize=__lowercase					,			padding_value=1.0   )

						lowerCamelCase_				:						Optional[Any]				  =				GitProcessor.from_pretrained(
						    self.tmpdirname					,			bos_token='(BOS)'					,			eos_token='(EOS)'					,			do_normalize=__lowercase					,			padding_value=1.0   )

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

						self.assertEqual(processor.image_processor.to_json_string()					,			image_processor_add_kwargs.to_json_string()   )
						self.assertIsInstance(processor.image_processor					,			__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				self.get_image_processor()
						lowerCamelCase_				:						Any				  =				self.get_tokenizer()

						lowerCamelCase_				:						str				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Tuple				  =				self.prepare_image_inputs()

						lowerCamelCase_				:						Union[str, Any]				  =				image_processor(__lowercase					,			return_tensors='np'   )
						lowerCamelCase_				:						Optional[int]				  =				processor(images=__lowercase					,			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       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				self.get_image_processor()
						lowerCamelCase_				:						List[Any]				  =				self.get_tokenizer()

						lowerCamelCase_				:						Optional[int]				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Optional[int]				  =				'lower newer'

						lowerCamelCase_				:						Optional[Any]				  =				processor(text=__lowercase   )

						lowerCamelCase_				:						int				  =				tokenizer(__lowercase					,			return_token_type_ids=__lowercase   )

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]					,			encoded_processor[key]   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.get_image_processor()
						lowerCamelCase_				:						str				  =				self.get_tokenizer()

						lowerCamelCase_				:						str				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				'lower newer'
						lowerCamelCase_				:						Tuple				  =				self.prepare_image_inputs()

						lowerCamelCase_				:						List[str]				  =				processor(text=__lowercase					,			images=__lowercase   )

						self.assertListEqual(list(inputs.keys()   )					,			['input_ids', 'attention_mask', 'pixel_values']   )

						# test if it raises when no input is passed
						with pytest.raises(__lowercase   ):
							processor()

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				self.get_image_processor()
						lowerCamelCase_				:						str				  =				self.get_tokenizer()

						lowerCamelCase_				:						Dict				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						str				  =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCamelCase_				:						Any				  =				processor.batch_decode(__lowercase   )
						lowerCamelCase_				:						Optional[int]				  =				tokenizer.batch_decode(__lowercase   )

						self.assertListEqual(__lowercase					,			__lowercase   )





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				self.get_image_processor()
						lowerCamelCase_				:						List[str]				  =				self.get_tokenizer()

						lowerCamelCase_				:						Optional[Any]				  =				GitProcessor(tokenizer=__lowercase					,			image_processor=__lowercase   )

						lowerCamelCase_				:						Optional[Any]				  =				'lower newer'
						lowerCamelCase_				:						Dict				  =				self.prepare_image_inputs()

						lowerCamelCase_				:						List[str]				  =				processor(text=__lowercase					,			images=__lowercase   )

						# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
						self.assertListEqual(list(inputs.keys()   )					,			['input_ids', 'attention_mask', 'pixel_values']   )


'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     tie_word_embeddings=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case							:  Any			=     kwargs.pop('''text_encoder'''   )
										snake_case							:  List[Any]			=     text_encoder_config.pop('''model_type'''   )

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										return cls(
										    text_encoder=text_encoder_config.to_dict(),     audio_encoder=audio_encoder_config.to_dict(),     decoder=decoder_config.to_dict(),     **SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     copy.deepcopy(self.__dict__   )
										snake_case							:  Tuple			=     self.text_encoder.to_dict()
										snake_case							:  str			=     self.audio_encoder.to_dict()
										snake_case							:  Dict			=     self.decoder.to_dict()
										snake_case							:  Optional[int]			=     self.__class__.model_type
										return output

