






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.gptj.modeling_tf_gptj import (
	    TFGPTJForCausalLM,
	    TFGPTJForQuestionAnswering,
	    TFGPTJForSequenceClassification,
	    TFGPTJModel,
	    shape_list,
	)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  parent
							lowerCAmelCase__	 =  13
							lowerCAmelCase__	 =  7
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  99
							lowerCAmelCase__	 =  32
							lowerCAmelCase__	 =  4
							lowerCAmelCase__	 =  2
							lowerCAmelCase__	 =  4
							lowerCAmelCase__	 =  37
							lowerCAmelCase__	 =  """gelu"""
							lowerCAmelCase__	 =  0.1
							lowerCAmelCase__	 =  0.1
							lowerCAmelCase__	 =  512
							lowerCAmelCase__	 =  16
							lowerCAmelCase__	 =  2
							lowerCAmelCase__	 =  0.02
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  4
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  self.vocab_size - 1
							lowerCAmelCase__	 =  self.vocab_size - 1
							lowerCAmelCase__	 =  self.vocab_size - 1

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size    )

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

							lowerCAmelCase__	 =  None
							if self.use_token_type_ids:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size    )

							lowerCAmelCase__	 =  None
							if self.use_mc_token_ids:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.num_choices]						,      self.seq_length    )

							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  None
							if self.use_labels:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size]						,      self.type_sequence_label_size    )
													lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels    )
													lowerCAmelCase__	 =  ids_tensor([self.batch_size]						,      self.num_choices    )

							lowerCAmelCase__	 =  GPTJConfig(
							    vocab_size=self.vocab_size						,      n_embd=self.hidden_size						,      n_layer=self.num_hidden_layers						,      n_head=self.num_attention_heads						,      intermediate_size=self.intermediate_size						,      hidden_act=self.hidden_act						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      n_positions=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      initializer_range=self.initializer_range						,      bos_token_id=self.bos_token_id						,      eos_token_id=self.eos_token_id						,      pad_token_id=self.pad_token_id						,      rotary_dim=self.rotary_dim						,      return_dict=snake_case_						,      )

							lowerCAmelCase__	 =  ids_tensor([self.num_hidden_layers, self.num_attention_heads]						,      2    )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=snake_case_    )
							lowerCAmelCase__	 =  {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase__	 =  model(snake_case_    )

							lowerCAmelCase__	 =  [input_ids, None, input_mask]  # None is the input for 'past'
							lowerCAmelCase__	 =  model(snake_case_    )

							lowerCAmelCase__	 =  model(snake_case_    )

							self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=snake_case_    )

							# first forward pass
							lowerCAmelCase__	 =  model(snake_case_						,      token_type_ids=snake_case_						,      use_cache=snake_case_    )
							lowerCAmelCase__	 =  model(snake_case_						,      token_type_ids=snake_case_    )
							lowerCAmelCase__	 =  model(snake_case_						,      token_type_ids=snake_case_						,      use_cache=snake_case_    )

							self.parent.assertTrue(len(snake_case_    ) == len(snake_case_    )    )
							self.parent.assertTrue(len(snake_case_    ) == len(snake_case_    ) + 1    )

							lowerCAmelCase__	 =  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 1)						,      config.vocab_size    )
							lowerCAmelCase__	 =  ids_tensor([self.batch_size, 1]						,      self.type_vocab_size    )

							# append to next input_ids and token_type_ids
							lowerCAmelCase__	 =  tf.concat([input_ids, next_tokens]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([token_type_ids, next_token_types]						,      axis=-1    )

							lowerCAmelCase__	 =  model(snake_case_						,      token_type_ids=snake_case_    )["""last_hidden_state"""]
							lowerCAmelCase__	 =  model(snake_case_						,      token_type_ids=snake_case_						,      past_key_values=snake_case_    )[
							    """last_hidden_state"""
							]

							# select random slice
							lowerCAmelCase__	 =  int(ids_tensor((1,)						,      shape_list(snake_case_    )[-1]    )    )
							lowerCAmelCase__	 =  output_from_no_past[:, -1, random_slice_idx]
							lowerCAmelCase__	 =  output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(snake_case_						,      snake_case_						,      rtol=1E-6    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=snake_case_    )

							# create attention mask
							lowerCAmelCase__	 =  self.seq_length // 2
							lowerCAmelCase__	 =  tf.ones((self.batch_size, half_seq_length)						,      dtype=tf.intaa    )
							lowerCAmelCase__	 =  tf.zeros((self.batch_size, self.seq_length - half_seq_length)						,      dtype=tf.intaa    )
							lowerCAmelCase__	 =  tf.concat([attn_mask_begin, attn_mask_end]						,      axis=1    )

							# first forward pass
							lowerCAmelCase__	 =  model(snake_case_						,      attention_mask=snake_case_    ).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 1)						,      config.vocab_size    )

							# change a random masked slice from input_ids
							lowerCAmelCase__	 =  ids_tensor((1,)						,      snake_case_    ).numpy() + 1
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, self.seq_length)						,      config.vocab_size    )
							lowerCAmelCase__	 =  tf.range(self.seq_length    ) == (self.seq_length - random_seq_idx_to_change)
							lowerCAmelCase__	 =  tf.transpose(
							    tf.broadcast_to(tf.expand_dims(snake_case_						,      -1    )						,      (self.seq_length, self.batch_size)    )    )
							lowerCAmelCase__	 =  tf.where(snake_case_						,      snake_case_						,      snake_case_    )

							# append to next input_ids and attn_mask
							lowerCAmelCase__	 =  tf.concat([input_ids, next_tokens]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([attn_mask, tf.ones((shape_list(snake_case_    )[0], 1)						,      dtype=tf.intaa    )]						,      axis=1    )

							# get two different outputs
							lowerCAmelCase__	 =  model(snake_case_						,      attention_mask=snake_case_    )["""last_hidden_state"""]
							lowerCAmelCase__	 =  model(snake_case_						,      past_key_values=snake_case_						,      attention_mask=snake_case_    )[
							    """last_hidden_state"""
							]

							# select random slice
							lowerCAmelCase__	 =  int(ids_tensor((1,)						,      shape_list(snake_case_    )[-1]    )    )
							lowerCAmelCase__	 =  output_from_no_past[:, -1, random_slice_idx]
							lowerCAmelCase__	 =  output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(snake_case_						,      snake_case_						,      rtol=1E-1_2    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=snake_case_    )

							lowerCAmelCase__	 =  input_ids[:1, :]
							lowerCAmelCase__	 =  input_mask[:1, :]
							lowerCAmelCase__	 =  token_type_ids[:1, :]
							lowerCAmelCase__	 =  1

							# first forward pass
							lowerCAmelCase__	 =  model(snake_case_						,      attention_mask=snake_case_						,      token_type_ids=snake_case_						,      use_cache=snake_case_    )

							lowerCAmelCase__	 =  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 3)						,      config.vocab_size    )
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 3)						,      2    )
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 3)						,      self.type_vocab_size    )

							# append to next input_ids and token_type_ids
							lowerCAmelCase__	 =  tf.concat([input_ids, next_tokens]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([input_mask, next_attn_mask]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([token_type_ids, next_token_types]						,      axis=-1    )

							lowerCAmelCase__	 =  model(
							    snake_case_						,      token_type_ids=snake_case_						,      attention_mask=snake_case_    )["""last_hidden_state"""]
							lowerCAmelCase__	 =  model(
							    snake_case_						,      token_type_ids=snake_case_						,      attention_mask=snake_case_						,      past_key_values=snake_case_						,      )["""last_hidden_state"""]
							self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]    )

							# select random slice
							lowerCAmelCase__	 =  int(ids_tensor((1,)						,      shape_list(snake_case_    )[-1]    )    )
							lowerCAmelCase__	 =  output_from_no_past[:, -3:, random_slice_idx]
							lowerCAmelCase__	 =  output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(snake_case_						,      snake_case_						,      rtol=1E-3    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJForCausalLM(config=snake_case_    )
							lowerCAmelCase__	 =  {
							    """input_ids""": input_ids,
							    """attention_mask""": input_mask,
							    """token_type_ids""": token_type_ids,
							}
							lowerCAmelCase__	 =  model(snake_case_    )
							self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)    )


	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.prepare_config_and_inputs()

							(
							    lowerCAmelCase__   
							)	 =  config_and_inputs

							lowerCAmelCase__	 =  {
							    """input_ids""": input_ids,
							    """token_type_ids""": token_type_ids,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict





@require_tf
class        lowercase__       (     _a,   _a,   _a,   unittest.TestCase		):
	a_             =(
	    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
	    if is_tf_available()
	    else ()
	)

	a_             =(TFGPTJForCausalLM,) if is_tf_available() else ()
	a_             =(
	    {
	        """feature-extraction""": TFGPTJModel,
	        """question-answering""": TFGPTJForQuestionAnswering,
	        """text-classification""": TFGPTJForSequenceClassification,
	        """text-generation""": TFGPTJForCausalLM,
	        """zero-shot""": TFGPTJForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	a_             =False
	a_             =False
	a_             =False
	a_             =False

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Tuple:







							'''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 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModelTester(self    )
							lowerCAmelCase__	 =  ConfigTester(self						,      config_class=snake_case_						,      n_embd=37    )

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							self.config_tester.run_common_tests()

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







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model(*snake_case_    )

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past(*snake_case_    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_attention_mask_past(*snake_case_    )

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past_large_inputs(*snake_case_    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_lm_head_model(*snake_case_    )

	@slow
	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) > 0						,      "skip testing on GPU for now to avoid GPU OOM."						,      )
	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"						,      from_pt=snake_case_    )
							self.assertIsNotNone(snake_case_    )


	@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."    )
	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							super().test_resize_token_embeddings()





@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class        lowercase__       (     unittest.TestCase		):

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						,      from_pt=snake_case_    )
							lowerCAmelCase__	 =  tf.convert_to_tensor([[464, 3290]]						,      dtype=tf.intaa    )  # The dog
							# fmt: off
							# The dog is a man's best friend. It is a loyal companion, and it is a friend
							lowerCAmelCase__	 =  [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
							# fmt: on
							lowerCAmelCase__	 =  model.generate(snake_case_						,      do_sample=snake_case_    )
							self.assertListEqual(output_ids[0].numpy().tolist()						,      snake_case_    )

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"    )
							lowerCAmelCase__	 =  TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"						,      from_pt=snake_case_    )

							lowerCAmelCase__	 =  tokenizer("Today is a nice day and"						,      return_tensors="tf"    )
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(":/CPU:0"    ):
													lowerCAmelCase__	 =  model.generate(**snake_case_						,      do_sample=snake_case_						,      seed=[42, 0]    )
							lowerCAmelCase__	 =  tokenizer.decode(output_ids[0]						,      skip_special_tokens=snake_case_    )

							lowerCAmelCase__	 =  """Today is a nice day and I’m going to go for a walk. I’"""
							self.assertEqual(snake_case_						,      snake_case_    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"						,      from_pt=snake_case_    )
							lowerCAmelCase__	 =  AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"    )

							lowerCAmelCase__	 =  """left"""

							# Define PAD Token = EOS Token = 50256
							lowerCAmelCase__	 =  tokenizer.eos_token
							lowerCAmelCase__	 =  model.config.eos_token_id

							# use different length sentences to test batching
							lowerCAmelCase__	 =  [
							    """Hello, my dog is a little""",
							    """Today, I""",
							]
							lowerCAmelCase__	 =  [
							    """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
							    """Today, I’m going to be talking about a topic that’""",
							]
							return model, tokenizer, sentences, expected_output_sentences

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_beam_search_test_objects()

							lowerCAmelCase__	 =  tokenizer(snake_case_						,      return_tensors="tf"						,      padding=snake_case_    )
							lowerCAmelCase__	 =  model.generate(**snake_case_						,      do_sample=snake_case_						,      num_beams=2    )
							lowerCAmelCase__	 =  tokenizer.batch_decode(snake_case_						,      skip_special_tokens=snake_case_    )
							self.assertListEqual(snake_case_						,      snake_case_    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_beam_search_test_objects()

							lowerCAmelCase__	 =  tokenizer(snake_case_						,      return_tensors="tf"						,      padding=snake_case_    )
							lowerCAmelCase__	 =  tokenizer(sentences[0]						,      return_tensors="tf"    )
							lowerCAmelCase__	 =  model.generate(**snake_case_						,      do_sample=snake_case_						,      num_beams=2    )
							lowerCAmelCase__	 =  (
							    shape_list(inputs_non_padded["input_ids"]    )[-1]
							    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]						,      tf.intaa    )    ).numpy()
							)
							lowerCAmelCase__	 =  tokenizer(sentences[1]						,      return_tensors="tf"    )
							lowerCAmelCase__	 =  model.generate(
							    **snake_case_						,      do_sample=snake_case_						,      num_beams=2						,      max_length=model.config.max_length - num_paddings    )
							lowerCAmelCase__	 =  tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=snake_case_    )
							lowerCAmelCase__	 =  tokenizer.decode(output_padded[0]						,      skip_special_tokens=snake_case_    )
							self.assertListEqual(snake_case_						,      [non_padded_sentence, padded_sentence]    )


	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  self._get_beam_search_test_objects()

							lowerCAmelCase__	 =  tokenizer(snake_case_						,      return_tensors="tf"						,      padding=snake_case_    )
							lowerCAmelCase__	 =  tf.function(model.generate						,      jit_compile=snake_case_    )
							lowerCAmelCase__	 =  xla_generate(**snake_case_						,      do_sample=snake_case_						,      num_beams=2    )
							lowerCAmelCase__	 =  tokenizer.batch_decode(snake_case_						,      skip_special_tokens=snake_case_    )
							self.assertListEqual(snake_case_						,      snake_case_    )














import copy

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


a_							=      logging.get_logger(__name__)

a_							=      {
    '''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        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen_decoder"""
	a_             =["""past_key_values"""]

	def __init__(							self						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=24						,      __UpperCAmelCase=4096						,      __UpperCAmelCase=16						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=True						,      __UpperCAmelCase="gelu"						,      __UpperCAmelCase=1024						,      __UpperCAmelCase=0.1						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.02						,      __UpperCAmelCase=False						,      __UpperCAmelCase=4						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=None						,      __UpperCAmelCase=False						,      **__UpperCAmelCase						,      )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  vocab_size
							lowerCAmelCase__	 =  max_position_embeddings
							lowerCAmelCase__	 =  hidden_size
							lowerCAmelCase__	 =  ffn_dim
							lowerCAmelCase__	 =  num_hidden_layers
							lowerCAmelCase__	 =  num_attention_heads
							lowerCAmelCase__	 =  dropout
							lowerCAmelCase__	 =  attention_dropout
							lowerCAmelCase__	 =  activation_dropout
							lowerCAmelCase__	 =  activation_function
							lowerCAmelCase__	 =  initializer_factor
							lowerCAmelCase__	 =  layerdrop
							lowerCAmelCase__	 =  use_cache
							lowerCAmelCase__	 =  scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCAmelCase__	 =  num_codebooks
							super().__init__(
							    pad_token_id=__UpperCAmelCase						,      bos_token_id=__UpperCAmelCase						,      eos_token_id=__UpperCAmelCase						,      tie_word_embeddings=__UpperCAmelCase						,      **__UpperCAmelCase						,      )





class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen"""
	a_             =True

	def __init__(							self						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							super().__init__(**__UpperCAmelCase    )
							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"    )

							lowerCAmelCase__	 =  kwargs.pop("text_encoder"    )
							lowerCAmelCase__	 =  text_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("audio_encoder"    )
							lowerCAmelCase__	 =  audio_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("decoder"    )

							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  MusicgenDecoderConfig(**__UpperCAmelCase    )
							lowerCAmelCase__	 =  True

	@classmethod
	def 						UpperCAmelCase					(							cls						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   Tuple:







							'''simple docstring'''





							return cls(
							    text_encoder=text_encoder_config.to_dict()						,      audio_encoder=audio_encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__UpperCAmelCase						,      )


	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  copy.deepcopy(self.__dict__    )
							lowerCAmelCase__	 =  self.text_encoder.to_dict()
							lowerCAmelCase__	 =  self.audio_encoder.to_dict()
							lowerCAmelCase__	 =  self.decoder.to_dict()
							lowerCAmelCase__	 =  self.__class__.model_type
							return output






