
'''simple docstring'''



import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


UpperCamelCase__      :		Optional[int]       							=  "base_with_context"
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
			_SCREAMING_SNAKE_CASE      =				nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)    ,      requires_grad=SCREAMING_SNAKE_CASE_						)
			for lyr_num, lyr in enumerate(model.encoders						):
						_SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

						_SCREAMING_SNAKE_CASE      =				ly_weight["""attention"""]
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)
			return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)    ,      requires_grad=SCREAMING_SNAKE_CASE_						)

			for lyr_num, lyr in enumerate(model.encoders						):
						_SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
						_SCREAMING_SNAKE_CASE      =				ly_weight["""attention"""]

						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)

			return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)    ,      requires_grad=SCREAMING_SNAKE_CASE_						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(
			    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T						)						)

			for lyr_num, lyr in enumerate(model.decoders						):
						_SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]						)						)

						_SCREAMING_SNAKE_CASE      =				nn.Parameter(
						    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)

						_SCREAMING_SNAKE_CASE      =				ly_weight["""self_attention"""]
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

						_SCREAMING_SNAKE_CASE      =				ly_weight["""MultiHeadDotProductAttention_0"""]
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]						)						)

						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(
						    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
						_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

			_SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T						)						)

			return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(args.checkpoint_path						)
			_SCREAMING_SNAKE_CASE      =				jnp.tree_util.tree_map(onp.array    ,      SCREAMING_SNAKE_CASE_						)

			_SCREAMING_SNAKE_CASE      =				[
			    """from __gin__ import dynamic_registration""",
			    """from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
			    """diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
			    """diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
			]

			_SCREAMING_SNAKE_CASE      =				os.path.join(args.checkpoint_path    ,      """.."""    ,      """config.gin"""						)
			_SCREAMING_SNAKE_CASE      =				inference.parse_training_gin_file(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
			_SCREAMING_SNAKE_CASE      =				inference.InferenceModel(args.checkpoint_path    ,      SCREAMING_SNAKE_CASE_						)

			_SCREAMING_SNAKE_CASE      =				DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    ,      variance_type="""fixed_large"""						)

			_SCREAMING_SNAKE_CASE      =				SpectrogramNotesEncoder(
			    max_length=synth_model.sequence_length["""inputs"""]    ,      vocab_size=synth_model.model.module.config.vocab_size    ,      d_model=synth_model.model.module.config.emb_dim    ,      dropout_rate=synth_model.model.module.config.dropout_rate    ,      num_layers=synth_model.model.module.config.num_encoder_layers    ,      num_heads=synth_model.model.module.config.num_heads    ,      d_kv=synth_model.model.module.config.head_dim    ,      d_ff=synth_model.model.module.config.mlp_dim    ,      feed_forward_proj="""gated-gelu"""    ,      )

			_SCREAMING_SNAKE_CASE      =				SpectrogramContEncoder(
			    input_dims=synth_model.audio_codec.n_dims    ,      targets_context_length=synth_model.sequence_length["""targets_context"""]    ,      d_model=synth_model.model.module.config.emb_dim    ,      dropout_rate=synth_model.model.module.config.dropout_rate    ,      num_layers=synth_model.model.module.config.num_encoder_layers    ,      num_heads=synth_model.model.module.config.num_heads    ,      d_kv=synth_model.model.module.config.head_dim    ,      d_ff=synth_model.model.module.config.mlp_dim    ,      feed_forward_proj="""gated-gelu"""    ,      )

			_SCREAMING_SNAKE_CASE      =				TaFilmDecoder(
			    input_dims=synth_model.audio_codec.n_dims    ,      targets_length=synth_model.sequence_length["""targets_context"""]    ,      max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time    ,      d_model=synth_model.model.module.config.emb_dim    ,      num_layers=synth_model.model.module.config.num_decoder_layers    ,      num_heads=synth_model.model.module.config.num_heads    ,      d_kv=synth_model.model.module.config.head_dim    ,      d_ff=synth_model.model.module.config.mlp_dim    ,      dropout_rate=synth_model.model.module.config.dropout_rate    ,      )

			_SCREAMING_SNAKE_CASE      =				load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,      SCREAMING_SNAKE_CASE_						)
			_SCREAMING_SNAKE_CASE      =				load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,      SCREAMING_SNAKE_CASE_						)
			_SCREAMING_SNAKE_CASE      =				load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,      SCREAMING_SNAKE_CASE_						)

			_SCREAMING_SNAKE_CASE      =				OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""						)

			_SCREAMING_SNAKE_CASE      =				SpectrogramDiffusionPipeline(
			    notes_encoder=SCREAMING_SNAKE_CASE_    ,      continuous_encoder=SCREAMING_SNAKE_CASE_    ,      decoder=SCREAMING_SNAKE_CASE_    ,      scheduler=SCREAMING_SNAKE_CASE_    ,      melgan=SCREAMING_SNAKE_CASE_    ,      )
			if args.save:
						pipe.save_pretrained(args.output_path						)


if __name__ == "__main__":
					UpperCamelCase__      :		List[Any]       							=  argparse.ArgumentParser()

					parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
					parser.add_argument(
					    "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
					)
					parser.add_argument(
					    "--checkpoint_path",
					    default=f"""{MODEL}/checkpoint_500000""",
					    type=str,
					    required=False,
					    help="Path to the original jax model checkpoint.",
					)
					UpperCamelCase__      :		List[Any]       							=  parser.parse_args()

					main(args)








'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
     import jax.numpy as jnp

if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch

class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				{
              """task_specific_params""": {
                  """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
                  """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
                  """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
              }
          }
          _SCREAMING_SNAKE_CASE      =				{
              """task_specific_params.summarization.length_penalty""": 1.0,
              """task_specific_params.summarization.max_length""": 1_28,
              """task_specific_params.summarization.min_length""": 12,
              """task_specific_params.summarization.num_beams""": 4,
              """task_specific_params.summarization_cnn.length_penalty""": 2.0,
              """task_specific_params.summarization_cnn.max_length""": 1_42,
              """task_specific_params.summarization_cnn.min_length""": 56,
              """task_specific_params.summarization_cnn.num_beams""": 4,
              """task_specific_params.summarization_xsum.length_penalty""": 1.0,
              """task_specific_params.summarization_xsum.max_length""": 62,
              """task_specific_params.summarization_xsum.min_length""": 11,
              """task_specific_params.summarization_xsum.num_beams""": 6,
          }

          self.assertEqual(flatten_dict(A__      )       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							x.transpose()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							x.transpose((1, 2, 0)      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							transpose(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							transpose(A__       ,							axes=(1, 2, 0)      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							transpose(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							transpose(A__       ,							axes=(1, 2, 0)      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(transpose(A__      )       ,							np.asarray(transpose(A__      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(transpose(A__       ,							axes=(1, 2, 0)      )       ,							np.asarray(transpose(A__       ,							axes=(1, 2, 0)      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							np.reshape(A__       ,							(4, 3)      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							np.reshape(A__       ,							(12, 5)      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							reshape(A__       ,							(4, 3)      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							reshape(A__       ,							(12, 5)      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							reshape(A__       ,							(4, 3)      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							reshape(A__       ,							(12, 5)      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(4, 3)      )       ,							np.asarray(reshape(A__       ,							(4, 3)      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(reshape(A__       ,							(12, 5)      )       ,							np.asarray(reshape(A__       ,							(12, 5)      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							np.squeeze(A__      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							np.squeeze(A__       ,							axis=2      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							squeeze(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							squeeze(A__       ,							axis=2      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							squeeze(A__      ).numpy()      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							squeeze(A__       ,							axis=2      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(squeeze(A__      )       ,							np.asarray(squeeze(A__      )      )      )      )

          _SCREAMING_SNAKE_CASE      =				np.random.randn(1       ,							4       ,							1       ,							5      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(squeeze(A__       ,							axis=2      )       ,							np.asarray(squeeze(A__       ,							axis=2      )      )      )      )






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							np.expand_dims(A__       ,							axis=1      )      )      )






       @require_torch
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							expand_dims(A__       ,							axis=1      ).numpy()      )      )






       @require_tf
       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				tf.constant(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							expand_dims(A__       ,							axis=1      ).numpy()      )      )






       @require_flax
       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				np.random.randn(3       ,							4      )
          _SCREAMING_SNAKE_CASE      =				jnp.array(A__      )
          self.assertTrue(np.allclose(expand_dims(A__       ,							axis=1      )       ,							np.asarray(expand_dims(A__       ,							axis=1      )      )      )      )






