


'''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


__UpperCAmelCase       =							{
    '''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  _snake_case       (					A			,   A	)						->   Any:
							return torch.atana(A			,   A	) / math.pi * 2


def  _snake_case       (					A	)						->   str:
							lowerCAmelCase__	       =     torch.sin(t * math.pi / 2	) ** 2
							lowerCAmelCase__	       =     (1 - sigma**2) ** 0.5
							return alpha_sigma_to_t(A			,   A	)




class 		a__    (							a__  ):





							'''simple docstring'''






							pass




class 		a__    (							nn.Module  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							Any:
														super().__init__()

														lowerCAmelCase__	       =     DiffusionAttnUnetaD(lowerCamelCase_ ,   n_attn_layers=4       )
														lowerCAmelCase__	       =     deepcopy(self.diffusion       )
														lowerCAmelCase__	       =     torch.quasirandom.SobolEngine(1 ,   scramble=lowerCamelCase_       )


def  _snake_case       (					A	)						->   List[Any]:
							lowerCAmelCase__	       =     MODELS_MAP[model_name]['''url''']
							os.system(F"""wget {url} ./"""	)

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


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

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

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


def  _snake_case       (					A	)						->   str:
							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  _snake_case       (					A	)						->   int:
							for key, value in ATTN_MAP.items():
														if name.startswith(A	) and not isinstance(A			,   A	):
																					return name.replace(A			,   A	)
														elif name.startswith(A	):
																					return [name.replace(A			,   A	) for v in value]
							raise ValueError(F"""Attn error with {name}"""	)


def  _snake_case       (					A			,   A=13	)						->   Optional[int]:
							lowerCAmelCase__	       =     input_string

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

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

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

							if string.startswith('''main.'''	):
														lowerCAmelCase__	       =     string[5:]

							# mid block
							if string[:2].isdigit():
														lowerCAmelCase__	       =     string[:2]
														lowerCAmelCase__	       =     string[2:]
							else:
														lowerCAmelCase__	       =     string[0]
														lowerCAmelCase__	       =     string[1:]

							if depth == max_depth:
														lowerCAmelCase__	       =     MID_NUM_TO_LAYER[layer_num]
														lowerCAmelCase__	       =     '''mid_block'''
							elif depth > 0 and int(A	) < 7:
														lowerCAmelCase__	       =     DOWN_NUM_TO_LAYER[layer_num]
														lowerCAmelCase__	       =     F"""down_blocks.{depth}"""
							elif depth > 0 and int(A	) > 7:
														lowerCAmelCase__	       =     UP_NUM_TO_LAYER[layer_num]
														lowerCAmelCase__	       =     F"""up_blocks.{max_depth - depth - 1}"""
							elif depth == 0:
														lowerCAmelCase__	       =     DEPTH_0_TO_LAYER[layer_num]
														lowerCAmelCase__	       =     F"""up_blocks.{max_depth - 1}""" if int(A	) > 3 else '''down_blocks.0'''

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

							lowerCAmelCase__	       =     string_left[1:]

							if "resnets" in new_layer:
														lowerCAmelCase__	       =     convert_resconv_naming(A	)
							elif "attentions" in new_layer:
														lowerCAmelCase__	       =     convert_attn_naming(A	)
														lowerCAmelCase__	       =     new_string_left

							if not isinstance(A			,   A	):
														lowerCAmelCase__	       =     prefix + '''.''' + new_layer + '''.''' + string_left
							else:
														lowerCAmelCase__	       =     [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
							return new_string


def  _snake_case       (					A	)						->   Optional[int]:
							lowerCAmelCase__	       =     {}
							for k, v in state_dict.items():
														if k.endswith('''kernel'''	):
																					# up- and downsample layers, don't have trainable weights
																					continue

														lowerCAmelCase__	       =     rename(A	)

														# check if we need to transform from Conv => Linear for attention
														if isinstance(A			,   A	):
																					lowerCAmelCase__	       =     transform_conv_attns(A			,   A			,   A	)
														else:
																					lowerCAmelCase__	       =     v

							return new_state_dict


def  _snake_case       (					A			,   A			,   A	)						->   Any:
							if len(A	) == 1:
														if len(v.shape	) == 3:
																					# weight
																					lowerCAmelCase__	       =     v[:, :, 0]
														else:
																					# bias
																					lowerCAmelCase__	       =     v
							else:
														# qkv matrices
														lowerCAmelCase__	       =     v.shape[0]
														lowerCAmelCase__	       =     trippled_shape // 3
														for i in range(3	):
																					if len(v.shape	) == 3:
																												lowerCAmelCase__	       =     v[i * single_shape : (i + 1) * single_shape, :, 0]
																					else:
																												lowerCAmelCase__	       =     v[i * single_shape : (i + 1) * single_shape]
							return new_state_dict


def  _snake_case       (					A	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)

							lowerCAmelCase__	       =     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()}"""
														lowerCAmelCase__	       =     download(A	)

							lowerCAmelCase__	       =     MODELS_MAP[model_name]['''sample_rate''']
							lowerCAmelCase__	       =     MODELS_MAP[model_name]['''sample_size''']

							lowerCAmelCase__	       =     Object()
							lowerCAmelCase__	       =     sample_size
							lowerCAmelCase__	       =     sample_rate
							lowerCAmelCase__	       =     0

							lowerCAmelCase__	       =     UNetaDModel(sample_size=A			,   sample_rate=A	)
							lowerCAmelCase__	       =     diffusers_model.state_dict()

							lowerCAmelCase__	       =     DiffusionUncond(A	)
							orig_model.load_state_dict(torch.load(args.model_path			,   map_location=A	)['''state_dict''']	)
							lowerCAmelCase__	       =     orig_model.diffusion_ema.eval()
							lowerCAmelCase__	       =     orig_model.state_dict()
							lowerCAmelCase__	       =     rename_orig_weights(A	)

							lowerCAmelCase__	       =     set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
							lowerCAmelCase__	       =     set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

							assert len(A	) == 0, F"""Problem with {renamed_minus_diffusers}"""
							assert all(k.endswith('''kernel'''	) for k in list(A	)	), 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":
																					lowerCAmelCase__	       =     value.squeeze()

														lowerCAmelCase__	       =     value

							diffusers_model.load_state_dict(A	)

							lowerCAmelCase__	       =     100
							lowerCAmelCase__	       =     33

							lowerCAmelCase__	       =     IPNDMScheduler(num_train_timesteps=A	)

							lowerCAmelCase__	       =     torch.manual_seed(A	)
							lowerCAmelCase__	       =     torch.randn([1, 2, config.sample_size]			,   generator=A	).to(A	)

							lowerCAmelCase__	       =     torch.linspace(1			,   0			,   steps + 1			,   device=A	)[:-1]
							lowerCAmelCase__	       =     get_crash_schedule(A	)

							lowerCAmelCase__	       =     DanceDiffusionPipeline(unet=A			,   scheduler=A	)

							lowerCAmelCase__	       =     torch.manual_seed(33	)
							lowerCAmelCase__	       =     pipe(num_inference_steps=A			,   generator=A	).audios

							lowerCAmelCase__	       =     sampling.iplms_sample(A			,   A			,   A			,   {}	)
							lowerCAmelCase__	       =     generated.clamp(-1			,   1	)

							lowerCAmelCase__	       =     (generated - audio).abs().sum()
							lowerCAmelCase__	       =     (generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
		__UpperCAmelCase       =							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.''')
		__UpperCAmelCase       =							parser.parse_args()

		main(args)



'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCAmelCase       =							{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							lowercase__      :       List[Any]     						=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														lowercase__      :       Optional[Any]     						=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														lowercase__      :       Tuple     						=				{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]       )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''This is bad'''] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														    ] ,   )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														# Legacy behavior
														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]       )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''Something else'''] ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														    ] ,   )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''Something else'''] ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        {'''label''': '''LABEL_0''', '''score''': 0.504},
														        {'''label''': '''LABEL_0''', '''score''': 0.504},
														    ] ,   )







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														import torch

														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt''' ,   device=torch.device('''cpu'''       ) ,   )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )







							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''tf'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )







							@slow
							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     pipeline('''text-classification'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''This is bad !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''NEGATIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''Birds are a type of animal'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 0.988}]       )







							@slow
							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     pipeline('''text-classification''' ,   framework='''tf'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''This is bad !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''NEGATIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''Birds are a type of animal'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 0.988}]       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Tuple:
														lowerCAmelCase__	       =     TextClassificationPipeline(model=lowerCamelCase_ ,   tokenizer=lowerCamelCase_       )
														return text_classifier, ["HuggingFace is in", "This is another test"]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														lowerCAmelCase__	       =     '''HuggingFace is in'''
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )

														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}]       )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )

														lowerCAmelCase__	       =     ['''HuggingFace is in ''', '''Paris is in France''']
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}, {'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] ,   )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_ ,   top_k=lowerCamelCase_       )
														lowerCAmelCase__	       =     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [[{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] * N, [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] * N] ,   )

														lowerCAmelCase__	       =     {'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   {'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )} ,   )
														self.assertTrue(outputs['''label'''] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														lowerCAmelCase__	       =     [['''HuggingFace is in ''', '''Paris is in France''']]
														with self.assertRaises(lowerCamelCase_       ):
																					text_classifier(lowerCamelCase_       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														lowerCAmelCase__	       =     text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] ,   )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )