
"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 __SCREAMING_SNAKE_CASE							=     'platform'

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )




def       A_  (				__lowercase						,	__lowercase						,	__lowercase=None						,	__lowercase=None						,	__lowercase=None						,	__lowercase=None						,	__lowercase=None						,	__lowercase=None						,	):
 if attention_mask is None:
  UpperCamelCase_     :					Union[str, Any]   	=np.where(input_ids != config.pad_token_id						,	1						,	0	)
 if decoder_attention_mask is None:
  UpperCamelCase_     :					List[str]   	=np.where(decoder_input_ids != config.pad_token_id						,	1						,	0	)
 if head_mask is None:
  UpperCamelCase_     :					int   	=np.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  UpperCamelCase_     :					Union[str, Any]   	=np.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  UpperCamelCase_     :					str   	=np.ones((config.decoder_layers, config.decoder_attention_heads)	)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }





class 				a__					:



     def __init__(							self :Any      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :str=13      ,  _lowerCamelCase :Optional[int]=7      ,  _lowerCamelCase :str=True      ,  _lowerCamelCase :Optional[int]=False      ,  _lowerCamelCase :Tuple=99      ,  _lowerCamelCase :str=16      ,  _lowerCamelCase :Any=2      ,  _lowerCamelCase :Tuple=4      ,  _lowerCamelCase :Optional[Any]=4      ,  _lowerCamelCase :List[str]="gelu"      ,  _lowerCamelCase :Optional[int]=0.1      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Dict=32      ,  _lowerCamelCase :Optional[Any]=2      ,  _lowerCamelCase :Tuple=1      ,  _lowerCamelCase :Optional[Any]=0      ,  _lowerCamelCase :int=0.02      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=parent
      UpperCamelCase_     :					str   	=batch_size
      UpperCamelCase_     :					Union[str, Any]   	=seq_length
      UpperCamelCase_     :					str   	=is_training
      UpperCamelCase_     :					int   	=use_labels
      UpperCamelCase_     :					List[str]   	=vocab_size
      UpperCamelCase_     :					int   	=hidden_size
      UpperCamelCase_     :					Any   	=num_hidden_layers
      UpperCamelCase_     :					List[str]   	=num_attention_heads
      UpperCamelCase_     :					Union[str, Any]   	=intermediate_size
      UpperCamelCase_     :					Any   	=hidden_act
      UpperCamelCase_     :					int   	=hidden_dropout_prob
      UpperCamelCase_     :					int   	=attention_probs_dropout_prob
      UpperCamelCase_     :					int   	=max_position_embeddings
      UpperCamelCase_     :					Union[str, Any]   	=eos_token_id
      UpperCamelCase_     :					Any   	=pad_token_id
      UpperCamelCase_     :					Any   	=bos_token_id
      UpperCamelCase_     :					str   	=initializer_range



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,  self.vocab_size   )      ,  3      ,  self.vocab_size   )
      UpperCamelCase_     :					int   	=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,  dtype=np.intaa   ))      ,  -1   )

      UpperCamelCase_     :					int   	=shift_tokens_right(_lowerCamelCase      ,  1      ,  2   )

      UpperCamelCase_     :					Dict   	=BlenderbotSmallConfig(
          vocab_size=self.vocab_size      ,  d_model=self.hidden_size      ,  encoder_layers=self.num_hidden_layers      ,  decoder_layers=self.num_hidden_layers      ,  encoder_attention_heads=self.num_attention_heads      ,  decoder_attention_heads=self.num_attention_heads      ,  encoder_ffn_dim=self.intermediate_size      ,  decoder_ffn_dim=self.intermediate_size      ,  dropout=self.hidden_dropout_prob      ,  attention_dropout=self.attention_probs_dropout_prob      ,  max_position_embeddings=self.max_position_embeddings      ,  eos_token_id=self.eos_token_id      ,  bos_token_id=self.bos_token_id      ,  pad_token_id=self.pad_token_id      ,  initializer_range=self.initializer_range      ,  use_cache=_lowerCamelCase      ,  )
      UpperCamelCase_     :					Optional[int]   	=prepare_blenderbot_inputs_dict(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase   )
      return config, inputs_dict



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=self.prepare_config_and_inputs()
      return config, inputs_dict



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :Dict      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=20
      UpperCamelCase_     :					Any   	=model_class_name(_lowerCamelCase   )

      UpperCamelCase_     :					List[Any]   	=model.encode(inputs_dict['input_ids']   )

      UpperCamelCase_	,     UpperCamelCase_     :					Any   	=(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      UpperCamelCase_     :					Any   	=model.init_cache(decoder_input_ids.shape[0]      ,  _lowerCamelCase      ,  _lowerCamelCase   )
      UpperCamelCase_     :					str   	=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,  dtype='i4'   )

      UpperCamelCase_     :					Any   	=jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]      ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,  )
      UpperCamelCase_     :					int   	=model.decode(
          decoder_input_ids[:, :-1]      ,  _lowerCamelCase      ,  decoder_attention_mask=_lowerCamelCase      ,  past_key_values=_lowerCamelCase      ,  decoder_position_ids=_lowerCamelCase      ,  )

      UpperCamelCase_     :					int   	=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,  dtype='i4'   )
      UpperCamelCase_     :					Any   	=model.decode(
          decoder_input_ids[:, -1:]      ,  _lowerCamelCase      ,  decoder_attention_mask=_lowerCamelCase      ,  past_key_values=outputs_cache.past_key_values      ,  decoder_position_ids=_lowerCamelCase      ,  )

      UpperCamelCase_     :					List[Any]   	=model.decode(_lowerCamelCase      ,  _lowerCamelCase   )

      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}'''   )




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=20
      UpperCamelCase_     :					Union[str, Any]   	=model_class_name(_lowerCamelCase   )

      UpperCamelCase_     :					List[str]   	=model.encode(inputs_dict['input_ids']   )

      UpperCamelCase_	,     UpperCamelCase_     :					List[Any]   	=(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      UpperCamelCase_     :					Optional[Any]   	=jnp.concatenate(
          [
              decoder_attention_mask,
              jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
          ]      ,  axis=-1      ,  )

      UpperCamelCase_     :					Any   	=model.init_cache(decoder_input_ids.shape[0]      ,  _lowerCamelCase      ,  _lowerCamelCase   )
      UpperCamelCase_     :					Union[str, Any]   	=jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]      ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,  )

      UpperCamelCase_     :					Optional[int]   	=model.decode(
          decoder_input_ids[:, :-1]      ,  _lowerCamelCase      ,  decoder_attention_mask=_lowerCamelCase      ,  past_key_values=_lowerCamelCase      ,  decoder_position_ids=_lowerCamelCase      ,  )
      UpperCamelCase_     :					Dict   	=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,  dtype='i4'   )
      UpperCamelCase_     :					Tuple   	=model.decode(
          decoder_input_ids[:, -1:]      ,  _lowerCamelCase      ,  past_key_values=outputs_cache.past_key_values      ,  decoder_attention_mask=_lowerCamelCase      ,  decoder_position_ids=_lowerCamelCase      ,  )

      UpperCamelCase_     :					Any   	=model.decode(_lowerCamelCase      ,  _lowerCamelCase      ,  decoder_attention_mask=_lowerCamelCase   )

      UpperCamelCase_     :					Union[str, 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}'''   )





@require_flax
class 				a__					(						unittest.TestCase       ):
     UpperCAmelCase__         =      99



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=np.array(
          [
              [71, 82, 18, 33, 46, 91, 2],
              [68, 34, 26, 58, 30, 82, 2],
              [5, 97, 17, 39, 94, 40, 2],
              [76, 83, 94, 25, 70, 78, 2],
              [87, 59, 41, 35, 48, 66, 2],
              [55, 13, 16, 58, 5, 2, 1],  # note padding
              [64, 27, 31, 51, 12, 75, 2],
              [52, 64, 86, 17, 83, 39, 2],
              [48, 61, 9, 24, 71, 82, 2],
              [26, 1, 60, 48, 22, 13, 2],
              [21, 5, 62, 28, 14, 76, 2],
              [45, 98, 37, 86, 59, 48, 2],
              [70, 70, 50, 9, 28, 0, 2],
          ]      ,  dtype=np.intaa      ,  )

      UpperCamelCase_     :					List[str]   	=input_ids.shape[0]
      UpperCamelCase_     :					Any   	=BlenderbotSmallConfig(
          vocab_size=self.vocab_size      ,  d_model=24      ,  encoder_layers=2      ,  decoder_layers=2      ,  encoder_attention_heads=2      ,  decoder_attention_heads=2      ,  encoder_ffn_dim=32      ,  decoder_ffn_dim=32      ,  max_position_embeddings=48      ,  eos_token_id=2      ,  pad_token_id=1      ,  bos_token_id=0      ,  )
      return config, input_ids, batch_size



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_     :					Any   	=self._get_config_and_data()
      UpperCamelCase_     :					Tuple   	=FlaxBlenderbotSmallForConditionalGeneration(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=lm_model(input_ids=_lowerCamelCase   )
      UpperCamelCase_     :					str   	=(batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape      ,  _lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=BlenderbotSmallConfig(
          vocab_size=self.vocab_size      ,  d_model=14      ,  encoder_layers=2      ,  decoder_layers=2      ,  encoder_attention_heads=2      ,  decoder_attention_heads=2      ,  encoder_ffn_dim=8      ,  decoder_ffn_dim=8      ,  max_position_embeddings=48      ,  )
      UpperCamelCase_     :					Tuple   	=FlaxBlenderbotSmallForConditionalGeneration(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,  dtype=np.intaa   )
      UpperCamelCase_     :					int   	=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,  dtype=np.intaa   )
      UpperCamelCase_     :					Optional[int]   	=lm_model(input_ids=_lowerCamelCase      ,  decoder_input_ids=_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=(*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape      ,  _lowerCamelCase   )




     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,  dtype=np.intaa   )
      UpperCamelCase_     :					Tuple   	=shift_tokens_right(_lowerCamelCase      ,  1      ,  2   )
      UpperCamelCase_     :					Dict   	=np.equal(_lowerCamelCase      ,  1   ).astype(np.floataa   ).sum()
      UpperCamelCase_     :					List[str]   	=np.equal(_lowerCamelCase      ,  1   ).astype(np.floataa   ).sum()
      self.assertEqual(shifted.shape      ,  input_ids.shape   )
      self.assertEqual(_lowerCamelCase      ,  n_pad_before - 1   )
      self.assertTrue(np.equal(shifted[:, 0]      ,  2   ).all()   )





@require_flax
class 				a__					(						A__   ,							unittest.TestCase   ,							A__       ):
     UpperCAmelCase__         =      True
     UpperCAmelCase__         =      (
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     UpperCAmelCase__         =      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=FlaxBlenderbotSmallModelTester(self   )



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[int]   	=self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase   )



     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
        UpperCamelCase_     :					int   	=self._prepare_for_class(_lowerCamelCase      ,  _lowerCamelCase   )
        UpperCamelCase_     :					Any   	=model_class(_lowerCamelCase   )

        @jax.jit
        def encode_jitted(_lowerCamelCase :List[str]      ,  _lowerCamelCase :Dict=None      ,  **_lowerCamelCase :Optional[int]   ):
         return model.encode(input_ids=_lowerCamelCase      ,  attention_mask=_lowerCamelCase   )

        with self.subTest('JIT Enabled'   ):
         UpperCamelCase_     :					str   	=encode_jitted(**_lowerCamelCase   ).to_tuple()

        with self.subTest('JIT Disabled'   ):
         with jax.disable_jit():
          UpperCamelCase_     :					Any   	=encode_jitted(**_lowerCamelCase   ).to_tuple()

        self.assertEqual(len(_lowerCamelCase   )      ,  len(_lowerCamelCase   )   )
        for jitted_output, output in zip(_lowerCamelCase      ,  _lowerCamelCase   ):
         self.assertEqual(jitted_output.shape      ,  output.shape   )



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					int   	=self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
        UpperCamelCase_     :					List[Any]   	=model_class(_lowerCamelCase   )
        UpperCamelCase_     :					Any   	=model.encode(inputs_dict['input_ids']      ,  inputs_dict['attention_mask']   )

        UpperCamelCase_     :					Any   	={
            'decoder_input_ids': inputs_dict['decoder_input_ids'],
            'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
            'encoder_outputs': encoder_outputs,
        }

        @jax.jit
        def decode_jitted(_lowerCamelCase :str      ,  _lowerCamelCase :Union[str, Any]      ,  _lowerCamelCase :Optional[int]   ):
         return model.decode(
             decoder_input_ids=_lowerCamelCase      ,  decoder_attention_mask=_lowerCamelCase      ,  encoder_outputs=_lowerCamelCase      ,  )

        with self.subTest('JIT Enabled'   ):
         UpperCamelCase_     :					Any   	=decode_jitted(**_lowerCamelCase   ).to_tuple()

        with self.subTest('JIT Disabled'   ):
         with jax.disable_jit():
          UpperCamelCase_     :					List[str]   	=decode_jitted(**_lowerCamelCase   ).to_tuple()

        self.assertEqual(len(_lowerCamelCase   )      ,  len(_lowerCamelCase   )   )
        for jitted_output, output in zip(_lowerCamelCase      ,  _lowerCamelCase   ):
         self.assertEqual(jitted_output.shape      ,  output.shape   )




     @slow
     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      for model_class_name in self.all_model_classes:
       UpperCamelCase_     :					Optional[int]   	=model_class_name.from_pretrained('facebook/blenderbot_small-90M'   )
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       UpperCamelCase_     :					Optional[Any]   	=np.ones((1, 1)   ) * model.config.eos_token_id
       UpperCamelCase_     :					Any   	=model(_lowerCamelCase   )
       self.assertIsNotNone(_lowerCamelCase   )


"""simple docstring"""






import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__SCREAMING_SNAKE_CASE							=     {
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 65_536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 65_536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 48_000,
        'sample_size': 131_072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 16_000,
        'sample_size': 65_536,
    },
}




def       A_  (				__lowercase						,	__lowercase	):
 return torch.atana(__lowercase						,	__lowercase	) / math.pi * 2




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=torch.sin(t * math.pi / 2	) ** 2
 UpperCamelCase_     :					Union[str, Any]   	=(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(__lowercase						,	__lowercase	)





class 				a__					(						A__       ):
     pass





class 				a__					(						nn.Module       ):



     def __init__(							self :Any      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__()

      UpperCamelCase_     :					Optional[int]   	=DiffusionAttnUnetaD(_lowerCamelCase      ,  n_attn_layers=4   )
      UpperCamelCase_     :					List[str]   	=deepcopy(self.diffusion   )
      UpperCamelCase_     :					int   	=torch.quasirandom.SobolEngine(1      ,  scramble=_lowerCamelCase   )




def       A_  (				__lowercase	):
 UpperCamelCase_     :					List[str]   	=MODELS_MAP[model_name]['url']
 os.system(F'''wget {url} ./'''	)

 return F'''./{model_name}.ckpt'''


__SCREAMING_SNAKE_CASE							=     {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__SCREAMING_SNAKE_CASE							=     {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__SCREAMING_SNAKE_CASE							=     {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
__SCREAMING_SNAKE_CASE							=     {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

__SCREAMING_SNAKE_CASE							=     {
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

__SCREAMING_SNAKE_CASE							=     {
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}




def       A_  (				__lowercase	):
 if name.startswith('skip'	):
  return name.replace('skip'						,	RES_CONV_MAP['skip']	)

 # name has to be of format main.{digit}
 if not name.startswith('main.'	):
  raise ValueError(F'''ResConvBlock error with {name}'''	)

 return name.replace(name[:6]						,	RES_CONV_MAP[name[:6]]	)




def       A_  (				__lowercase	):
 for key, value in ATTN_MAP.items():
  if name.startswith(__lowercase	) and not isinstance(__lowercase						,	__lowercase	):
   return name.replace(__lowercase						,	__lowercase	)
  elif name.startswith(__lowercase	):
   return [name.replace(__lowercase						,	__lowercase	) for v in value]
 raise ValueError(F'''Attn error with {name}'''	)




def       A_  (				__lowercase						,	__lowercase=13	):
 UpperCamelCase_     :					Optional[Any]   	=input_string

 if string.split('.'	)[0] == "timestep_embed":
  return string.replace('timestep_embed'						,	'time_proj'	)

 UpperCamelCase_     :					Tuple   	=0
 if string.startswith('net.3.'	):
  depth += 1
  UpperCamelCase_     :					str   	=string[6:]
 elif string.startswith('net.'	):
  UpperCamelCase_     :					List[str]   	=string[4:]

 while string.startswith('main.7.'	):
  depth += 1
  UpperCamelCase_     :					Optional[int]   	=string[7:]

 if string.startswith('main.'	):
  UpperCamelCase_     :					Union[str, Any]   	=string[5:]

 # mid block
 if string[:2].isdigit():
  UpperCamelCase_     :					List[str]   	=string[:2]
  UpperCamelCase_     :					Any   	=string[2:]
 else:
  UpperCamelCase_     :					List[Any]   	=string[0]
  UpperCamelCase_     :					Tuple   	=string[1:]

 if depth == max_depth:
  UpperCamelCase_     :					Optional[Any]   	=MID_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Optional[int]   	='mid_block'
 elif depth > 0 and int(__lowercase	) < 7:
  UpperCamelCase_     :					int   	=DOWN_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Dict   	=F'''down_blocks.{depth}'''
 elif depth > 0 and int(__lowercase	) > 7:
  UpperCamelCase_     :					Optional[Any]   	=UP_NUM_TO_LAYER[layer_num]
  UpperCamelCase_     :					Tuple   	=F'''up_blocks.{max_depth - depth - 1}'''
 elif depth == 0:
  UpperCamelCase_     :					Optional[int]   	=DEPTH_0_TO_LAYER[layer_num]
  UpperCamelCase_     :					List[str]   	=F'''up_blocks.{max_depth - 1}''' if int(__lowercase	) > 3 else 'down_blocks.0'

 if not string_left.startswith('.'	):
  raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''	)

 UpperCamelCase_     :					Union[str, Any]   	=string_left[1:]

 if "resnets" in new_layer:
  UpperCamelCase_     :					Dict   	=convert_resconv_naming(__lowercase	)
 elif "attentions" in new_layer:
  UpperCamelCase_     :					List[Any]   	=convert_attn_naming(__lowercase	)
  UpperCamelCase_     :					str   	=new_string_left

 if not isinstance(__lowercase						,	__lowercase	):
  UpperCamelCase_     :					int   	=prefix + '.' + new_layer + '.' + string_left
 else:
  UpperCamelCase_     :					List[Any]   	=[prefix + '.' + new_layer + '.' + s for s in string_left]
 return new_string




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	={}
 for k, v in state_dict.items():
  if k.endswith('kernel'	):
   # up- and downsample layers, don't have trainable weights
   continue

  UpperCamelCase_     :					Optional[Any]   	=rename(__lowercase	)

  # check if we need to transform from Conv => Linear for attention
  if isinstance(__lowercase						,	__lowercase	):
   UpperCamelCase_     :					Union[str, Any]   	=transform_conv_attns(__lowercase						,	__lowercase						,	__lowercase	)
  else:
   UpperCamelCase_     :					Union[str, Any]   	=v

 return new_state_dict




def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) == 1:
  if len(v.shape	) == 3:
   # weight
   UpperCamelCase_     :					Tuple   	=v[:, :, 0]
  else:
   # bias
   UpperCamelCase_     :					Any   	=v
 else:
  # qkv matrices
  UpperCamelCase_     :					Tuple   	=v.shape[0]
  UpperCamelCase_     :					Optional[Any]   	=trippled_shape // 3
  for i in range(3	):
   if len(v.shape	) == 3:
    UpperCamelCase_     :					str   	=v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    UpperCamelCase_     :					List[str]   	=v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict




def       A_  (				__lowercase	):
 UpperCamelCase_     :					int   	=torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)

 UpperCamelCase_     :					Dict   	=args.model_path.split('/'	)[-1].split('.'	)[0]
 if not os.path.isfile(args.model_path	):
  assert (
      model_name == args.model_path
  ), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
  UpperCamelCase_     :					Optional[Any]   	=download(__lowercase	)

 UpperCamelCase_     :					str   	=MODELS_MAP[model_name]['sample_rate']
 UpperCamelCase_     :					Any   	=MODELS_MAP[model_name]['sample_size']

 UpperCamelCase_     :					str   	=Object()
 UpperCamelCase_     :					List[str]   	=sample_size
 UpperCamelCase_     :					Optional[int]   	=sample_rate
 UpperCamelCase_     :					Tuple   	=0

 UpperCamelCase_     :					Optional[int]   	=UNetaDModel(sample_size=__lowercase						,	sample_rate=__lowercase	)
 UpperCamelCase_     :					Union[str, Any]   	=diffusers_model.state_dict()

 UpperCamelCase_     :					Any   	=DiffusionUncond(__lowercase	)
 orig_model.load_state_dict(torch.load(args.model_path						,	map_location=__lowercase	)['state_dict']	)
 UpperCamelCase_     :					List[str]   	=orig_model.diffusion_ema.eval()
 UpperCamelCase_     :					Tuple   	=orig_model.state_dict()
 UpperCamelCase_     :					Optional[int]   	=rename_orig_weights(__lowercase	)

 UpperCamelCase_     :					Any   	=set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
 UpperCamelCase_     :					Tuple   	=set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

 assert len(__lowercase	) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith('kernel'	) for k in list(__lowercase	)	), F'''Problem with {diffusers_minus_renamed}'''

 for key, value in renamed_state_dict.items():
  assert (
      diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
  ), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
  if key == "time_proj.weight":
   UpperCamelCase_     :					List[str]   	=value.squeeze()

  UpperCamelCase_     :					str   	=value

 diffusers_model.load_state_dict(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=1_00
 UpperCamelCase_     :					List[str]   	=33

 UpperCamelCase_     :					Any   	=IPNDMScheduler(num_train_timesteps=__lowercase	)

 UpperCamelCase_     :					List[Any]   	=torch.manual_seed(__lowercase	)
 UpperCamelCase_     :					List[str]   	=torch.randn([1, 2, config.sample_size]						,	generator=__lowercase	).to(__lowercase	)

 UpperCamelCase_     :					str   	=torch.linspace(1						,	0						,	steps + 1						,	device=__lowercase	)[:-1]
 UpperCamelCase_     :					Dict   	=get_crash_schedule(__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=DanceDiffusionPipeline(unet=__lowercase						,	scheduler=__lowercase	)

 UpperCamelCase_     :					Optional[Any]   	=torch.manual_seed(33	)
 UpperCamelCase_     :					Optional[Any]   	=pipe(num_inference_steps=__lowercase						,	generator=__lowercase	).audios

 UpperCamelCase_     :					Dict   	=sampling.iplms_sample(__lowercase						,	__lowercase						,	__lowercase						,	{}	)
 UpperCamelCase_     :					List[Any]   	=generated.clamp(-1						,	1	)

 UpperCamelCase_     :					Optional[int]   	=(generated - audio).abs().sum()
 UpperCamelCase_     :					Union[str, Any]   	=(generated - audio).abs().max()

 if args.save:
  pipe.save_pretrained(args.checkpoint_path	)

 print('Diff sum'						,	__lowercase	)
 print('Diff max'						,	__lowercase	)

 assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''

 print(F'''Conversion for {model_name} successful!'''	)


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     argparse.ArgumentParser()

 parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
 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=None, type=str, required=True, help='Path to the output model.')
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()

 main(args)
