





'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


snake_case_  :							str            =	"./tests/fixtures/elise_format0.mid"


@unittest.skip("The note-seq package currently throws an error on import"  )
class        __a   (lowerCamelCase							,		unittest.TestCase  ):
							__a       : List[Any]          =							SpectrogramDiffusionPipeline
							__a       : str          =							PipelineTesterMixin.required_optional_params - {
							    "callback",
							    "latents",
							    "callback_steps",
							    "output_type",
							    "num_images_per_prompt",
							}
							__a       : List[str]          =							False
							__a       : Optional[int]          =							False
							__a       : List[str]          =							TOKENS_TO_AUDIO_GENERATION_PARAMS
							__a       : Any          =							TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Dict:







													"""simple docstring"""
													torch.manual_seed(0 )
													UpperCAmelCase_      :   List[Any]      =	SpectrogramNotesEncoder(
													    max_length=20_48							,  vocab_size=15_36							,  d_model=7_68							,  dropout_rate=0.1							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  feed_forward_proj='''gated-gelu'''							,  )

													UpperCAmelCase_      :   Tuple      =	SpectrogramContEncoder(
													    input_dims=1_28							,  targets_context_length=2_56							,  d_model=7_68							,  dropout_rate=0.1							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  feed_forward_proj='''gated-gelu'''							,  )

													UpperCAmelCase_      :   List[Any]      =	TaFilmDecoder(
													    input_dims=1_28							,  targets_length=2_56							,  max_decoder_noise_time=2_0_0_0_0.0							,  d_model=7_68							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  dropout_rate=0.1							,  )

													UpperCAmelCase_      :   Optional[int]      =	DDPMScheduler()

													UpperCAmelCase_      :   List[Any]      =	{
													    '''notes_encoder''': notes_encoder.eval(),
													    '''continuous_encoder''': continuous_encoder.eval(),
													    '''decoder''': decoder.eval(),
													    '''scheduler''': scheduler,
													    '''melgan''': None,
													}
													return components

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  Union[str, Any]=0 )       ->							Any:







													"""simple docstring"""
													if str(__magic_name__ ).startswith('''mps''' ):
																			UpperCAmelCase_      :   Dict      =	torch.manual_seed(__magic_name__ )
													else:
																			UpperCAmelCase_      :   Optional[int]      =	torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''input_tokens''': [
													        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
													    ],
													    '''generator''': generator,
													    '''num_inference_steps''': 4,
													    '''output_type''': '''mel''',
													}
													return inputs

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_      :   str      =	self.get_dummy_components()
													UpperCAmelCase_      :   Optional[Any]      =	SpectrogramDiffusionPipeline(**__magic_name__ )
													UpperCAmelCase_      :   str      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   Dict      =	self.get_dummy_inputs(__magic_name__ )
													UpperCAmelCase_      :   Any      =	pipe(**__magic_name__ )
													UpperCAmelCase_      :   str      =	output.audios

													UpperCAmelCase_      :   List[Any]      =	mel[0, -3:, -3:]

													assert mel_slice.shape == (3, 3)
													UpperCAmelCase_      :   Union[str, Any]      =	np.array(
													    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0] )
													assert np.abs(mel_slice.flatten() - expected_slice ).max() < 1E-2

							@skip_mps
							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Tuple:







													"""simple docstring"""
													return super().test_save_load_local()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													return super().test_save_load_optional_components()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							List[Any]:







													"""simple docstring"""
													return super().test_attention_slicing_forward_pass()

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							int:







													"""simple docstring"""
													pass

							def      UpperCAmelCase__						(				self				:  Any )       ->							Dict:







													"""simple docstring"""
													pass



							@skip_mps
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class        __a   (unittest.TestCase  ):

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

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







													"""simple docstring"""
													# TODO - test that pipeline can decode tokens in a callback
													# so that music can be played live
													UpperCAmelCase_      :   Dict      =	torch_device

													UpperCAmelCase_      :   List[str]      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   Union[str, Any]      =	pipe.melgan
													UpperCAmelCase_      :   List[str]      =	None

													UpperCAmelCase_      :   Optional[int]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													def callback(__magic_name__				:  str							,  __magic_name__				:  List[str] ):
																			# decode mel to audio
																			UpperCAmelCase_      :   Tuple      =	melgan(input_features=mel_output.astype(np.floataa ) )[0]
																			assert len(audio[0] ) == 8_19_20 * (step + 1)
																			# simulate that audio is played
																			return audio

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()
													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	input_tokens[:3]
													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													pipe(__magic_name__							,  num_inference_steps=5							,  generator=__magic_name__							,  callback=__magic_name__							,  output_type='''mel''' )

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







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	torch_device

													UpperCAmelCase_      :   Any      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   Any      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )
													# just run two denoising loops
													UpperCAmelCase_      :   int      =	input_tokens[:2]

													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Optional[Any]      =	pipe(__magic_name__							,  num_inference_steps=2							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	output.audios[0]

													assert abs(np.abs(__magic_name__ ).sum() - 3_6_1_2.8_4_1 ) < 1E-1



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







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	torch_device

													UpperCAmelCase_      :   Optional[int]      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   List[Any]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[Any]      =	processor(__magic_name__ )

													# just run 4 denoising loops
													UpperCAmelCase_      :   Dict      =	input_tokens[:4]

													UpperCAmelCase_      :   Tuple      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	pipe(__magic_name__							,  num_inference_steps=1_00							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[int]      =	output.audios[0]
													assert abs(np.abs(__magic_name__ ).sum() - 9_3_8_9.1_1_1_1 ) < 5E-2









import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


if is_flax_available():
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch




@require_flax
class  A_ :







 """simple docstring"""





 def __init__(      self       :			Tuple							,     _a       :			Optional[int]							,     _a       :			Tuple=1_4							,     _a       :			List[Any]=7							,     _a       :			List[str]=True							,     _a       :			Tuple=True							,     _a       :			Any=True							,     _a       :			str=9_9							,     _a       :			str=3_2							,     _a       :			Optional[Any]=5							,     _a       :			int=4							,     _a       :			Union[str, Any]=3_7							,     _a       :			List[Any]="gelu"							,     _a       :			List[Any]=0.1							,     _a       :			Tuple=0.1							,     _a       :			int=5_1_2							,     _a       :			Tuple=0.0_2							,     _a       :			Dict=None							,     )     ->      Tuple:
      UpperCAmelCase    :					List[Any]			=      parent
      UpperCAmelCase    :					str			=      batch_size
      UpperCAmelCase    :					Dict			=      seq_length
      UpperCAmelCase    :					Any			=      is_training
      UpperCAmelCase    :					Union[str, Any]			=      use_input_mask
      UpperCAmelCase    :					int			=      use_labels
      UpperCAmelCase    :					Optional[Any]			=      vocab_size
      UpperCAmelCase    :					Union[str, Any]			=      d_model
      UpperCAmelCase    :					Optional[Any]			=      num_hidden_layers
      UpperCAmelCase    :					Tuple			=      num_attention_heads
      UpperCAmelCase    :					str			=      ffn_dim
      UpperCAmelCase    :					Dict			=      activation_function
      UpperCAmelCase    :					int			=      activation_dropout
      UpperCAmelCase    :					Union[str, Any]			=      attention_dropout
      UpperCAmelCase    :					Optional[int]			=      max_position_embeddings
      UpperCAmelCase    :					str			=      initializer_range
      UpperCAmelCase    :					Tuple			=      None
      UpperCAmelCase    :					List[Any]			=      0
      UpperCAmelCase    :					List[str]			=      2
      UpperCAmelCase    :					Dict			=      1
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      List[str]:
      UpperCAmelCase    :					str			=      np.clip(ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size					)							,     3							,     self.vocab_size					)

      UpperCAmelCase    :					Union[str, Any]			=      None
      if self.use_input_mask:
           UpperCAmelCase    :					int			=      random_attention_mask([self.batch_size, self.seq_length]					)

      UpperCAmelCase    :					Union[str, Any]			=      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 (config, input_ids, input_mask)
 def 	_UpperCamelCase     (      self       :			str					)     ->      List[str]:
      UpperCAmelCase    :					Tuple			=      self.prepare_config_and_inputs()
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      config_and_inputs
      UpperCAmelCase    :					str			=      {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      List[str]:
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      self.prepare_config_and_inputs()

      UpperCAmelCase    :					List[Any]			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      UpperCAmelCase    :					Any			=      ids_tensor([self.batch_size, self.seq_length]							,     vocab_size=2					)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Optional[int]							,     _a       :			Dict							,     _a       :			Tuple							,     _a       :			Dict					)     ->      str:
      UpperCAmelCase    :					List[str]			=      2_0
      UpperCAmelCase    :					Optional[Any]			=      model_class_name(_a					)

      UpperCAmelCase    :					Dict			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					List[Any]			=      jnp.ones((input_ids.shape[0], max_decoder_length)							,     dtype="i4"					)

      UpperCAmelCase    :					str			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)
      UpperCAmelCase    :					List[str]			=      model(
          input_ids[:, :-1]							,     attention_mask=_a							,     past_key_values=_a							,     position_ids=_a							,     )

      UpperCAmelCase    :					Any			=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype="i4"					)
      UpperCAmelCase    :					Dict			=      model(
          input_ids[:, -1:]							,     attention_mask=_a							,     past_key_values=outputs_cache.past_key_values							,     position_ids=_a							,     )

      UpperCAmelCase    :					int			=      model(_a					)

      UpperCAmelCase    :					Optional[Any]			=      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 	_UpperCamelCase     (      self       :			str							,     _a       :			List[Any]							,     _a       :			Any							,     _a       :			Optional[Any]							,     _a       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					List[Any]			=      2_0
      UpperCAmelCase    :					Any			=      model_class_name(_a					)

      UpperCAmelCase    :					Any			=      jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]							,     axis=-1							,     )

      UpperCAmelCase    :					Optional[int]			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					int			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)

      UpperCAmelCase    :					Any			=      model(
          input_ids[:, :-1]							,     attention_mask=_a							,     past_key_values=_a							,     position_ids=_a							,     )
      UpperCAmelCase    :					List[Any]			=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype="i4"					)
      UpperCAmelCase    :					Any			=      model(
          input_ids[:, -1:]							,     past_key_values=outputs_cache.past_key_values							,     attention_mask=_a							,     position_ids=_a							,     )

      UpperCAmelCase    :					Union[str, Any]			=      model(_a							,     attention_mask=_a					)
      UpperCAmelCase    :					int			=      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_sentencepiece
@require_flax
class  A_ (			a_   ,    a_   ,    unittest.TestCase  ):







 """simple docstring"""





 _UpperCamelCase					:			Tuple     	=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 _UpperCamelCase					:			int     	=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					Any			=      FlaxXGLMModelTester(self					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_a							,     _a							,     _a							,     _a					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Tuple			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               _a							,     _a							,     _a							,     _a					)
 @slow
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      UpperCAmelCase    :					int			=      XGLMTokenizer.from_pretrained("XGLM"							,     padding_side="left"					)
      UpperCAmelCase    :					Optional[int]			=      tokenizer(["Hello this is a long string", "Hey"]							,     return_tensors="np"							,     padding=_a							,     truncation=_a					)

      UpperCAmelCase    :					int			=      FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)
      UpperCAmelCase    :					Optional[Any]			=      1
      UpperCAmelCase    :					Tuple			=      False

      UpperCAmelCase    :					Any			=      jax.jit(model.generate					)

      UpperCAmelCase    :					Any			=      jit_generate(inputs["input_ids"]							,     attention_mask=inputs["attention_mask"]					).sequences

      UpperCAmelCase    :					Any			=      tokenizer.batch_decode(_a							,     skip_special_tokens=_a					)

      UpperCAmelCase    :					Union[str, Any]			=      [
          "Hello this is a long string of questions, but I'm not sure if I'm",
          "Hey, I'm a newbie to the forum and I'",
      ]

      self.assertListEqual(_a							,     _a					)
 @is_pt_flax_cross_test
 def 	_UpperCamelCase     (      self       :			Union[str, Any]					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[Any]			=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__					):
                # prepare inputs
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					int			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                UpperCAmelCase    :					int			=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                UpperCAmelCase    :					str			=      getattr(_a							,     _a					)

                UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      pt_inputs["input_ids"].shape
                UpperCAmelCase    :					str			=      np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)					)
                for batch_idx, start_index in enumerate(_a					):
                     UpperCAmelCase    :					str			=      0
                     UpperCAmelCase    :					Optional[Any]			=      1
                     UpperCAmelCase    :					int			=      0
                     UpperCAmelCase    :					Union[str, Any]			=      1
                UpperCAmelCase    :					Dict			=      pt_model_class(_a					).eval()
                # Flax models don't use the `use_cache` option and cache is not returned as a default.
                # So we disable `use_cache` here for PyTorch model.
                UpperCAmelCase    :					List[Any]			=      False
                UpperCAmelCase    :					Union[str, Any]			=      model_class(_a							,     dtype=jnp.floataa					)
                UpperCAmelCase    :					Union[str, Any]			=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,     _a					)
                UpperCAmelCase    :					List[Any]			=      fx_state

                with torch.no_grad():
                     UpperCAmelCase    :					str			=      pt_model(**_a					).to_tuple()

                UpperCAmelCase    :					int			=      fx_model(**_a					).to_tuple()
                self.assertEqual(len(_a					)							,     len(_a					)							,     "Output lengths differ between Flax and PyTorch"					)
                for fx_output, pt_output in zip(_a							,     _a					):
                     self.assert_almost_equals(fx_output[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2					)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(_a					)
                     UpperCAmelCase    :					Optional[Any]			=      model_class.from_pretrained(_a							,     from_pt=_a					)

                UpperCAmelCase    :					Optional[Any]			=      fx_model_loaded(**_a					).to_tuple()
                self.assertEqual(
                    len(_a					)							,     len(_a					)							,     "Output lengths differ between Flax and PyTorch"					)
                for fx_output_loaded, pt_output in zip(_a							,     _a					):
                     self.assert_almost_equals(fx_output_loaded[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2					)
 @is_pt_flax_cross_test
 def 	_UpperCamelCase     (      self       :			int					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[int]			=      self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__					):
                # prepare inputs
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					Optional[Any]			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                UpperCAmelCase    :					Optional[Any]			=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                UpperCAmelCase    :					Dict			=      getattr(_a							,     _a					)

                UpperCAmelCase    :					Optional[int]			=      pt_model_class(_a					).eval()
                UpperCAmelCase    :					int			=      False
                UpperCAmelCase    :					int			=      model_class(_a							,     dtype=jnp.floataa					)

                UpperCAmelCase    :					List[Any]			=      load_flax_weights_in_pytorch_model(_a							,     fx_model.params					)
                UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      pt_inputs["input_ids"].shape
                UpperCAmelCase    :					int			=      np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)					)
                for batch_idx, start_index in enumerate(_a					):
                     UpperCAmelCase    :					Dict			=      0
                     UpperCAmelCase    :					Any			=      1
                     UpperCAmelCase    :					Optional[int]			=      0
                     UpperCAmelCase    :					Optional[int]			=      1

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                with torch.no_grad():
                     UpperCAmelCase    :					int			=      pt_model(**_a					).to_tuple()

                UpperCAmelCase    :					Optional[Any]			=      fx_model(**_a					).to_tuple()
                self.assertEqual(len(_a					)							,     len(_a					)							,     "Output lengths differ between Flax and PyTorch"					)
                for fx_output, pt_output in zip(_a							,     _a					):
                     self.assert_almost_equals(fx_output[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2					)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(_a					)
                     UpperCAmelCase    :					List[Any]			=      pt_model_class.from_pretrained(_a							,     from_flax=_a					)

                with torch.no_grad():
                     UpperCAmelCase    :					List[Any]			=      pt_model_loaded(**_a					).to_tuple()

                self.assertEqual(
                    len(_a					)							,     len(_a					)							,     "Output lengths differ between Flax and PyTorch"					)
                for fx_output, pt_output in zip(_a							,     _a					):
                     self.assert_almost_equals(fx_output[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2					)

 @slow
 def 	_UpperCamelCase     (      self       :			Any					)     ->      str:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase    :					Dict			=      model_class_name.from_pretrained("facebook/xglm-564M"					)
           UpperCAmelCase    :					List[Any]			=      model(np.ones((1, 1)					)					)
           self.assertIsNotNone(_a					)



