





import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image

       from transformers.image_utils import load_image
else:


       class        __SCREAMING_SNAKE_CASE      :







              @staticmethod
              def 						__lowerCamelCase   (				*A					:     Optional[Any]		, **A					:     str      )       ->List[str]:
                 pass



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







          """simple docstring"""
          return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_A :   Dict  			=  (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)


@is_pipeline_test
@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):
       _UpperCAmelCase				:  Dict			   =   MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







       @require_pytesseract
       @require_vision
       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Optional[Any]		, A					:     Optional[int]		, A					:     str      )       ->Any:
          lowerCamelCase__       :						Any        =							pipeline(
              '''document-question-answering'''		, model=A		, tokenizer=A		, image_processor=A      )

          lowerCamelCase__       :						int        =							INVOICE_URL
          lowerCamelCase__       :						str        =							list(zip(*apply_tesseract(load_image(A      )		, A		, ''''''      )      )      )
          lowerCamelCase__       :						Dict        =							'''What is the placebo?'''
          lowerCamelCase__       :						List[Any]        =							[
              {
                  '''image''': load_image(A      ),
                  '''question''': question,
              },
              {
                  '''image''': image,
                  '''question''': question,
              },
              {
                  '''image''': image,
                  '''question''': question,
                  '''word_boxes''': word_boxes,
              },
          ]
          return dqa_pipeline, examples







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     Any		, A					:     Dict      )       ->Optional[int]:
          lowerCamelCase__       :						Union[str, Any]        =							dqa_pipeline(A		, top_k=2      )
          self.assertEqual(
              A		, [
                  [
                      {'''score''': ANY(A      ), '''answer''': ANY(A      ), '''start''': ANY(A      ), '''end''': ANY(A      )},
                      {'''score''': ANY(A      ), '''answer''': ANY(A      ), '''start''': ANY(A      ), '''end''': ANY(A      )},
                  ]
              ]
              * 3		, )







       @require_torch
       @require_detectrona
       @require_pytesseract
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[Any]:
          lowerCamelCase__       :						Tuple        =							pipeline('''document-question-answering'''		, model='''hf-internal-testing/tiny-random-layoutlmv2'''      )
          lowerCamelCase__       :						List[Any]        =							INVOICE_URL
          lowerCamelCase__       :						Optional[int]        =							'''How many cats are there?'''

          lowerCamelCase__       :						Dict        =							[
              {'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 3_8, '''end''': 3_9},
              {'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 3_8, '''end''': 4_0},
          ]
          lowerCamelCase__       :						Dict        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(nested_simplify(A		, decimals=4      )		, A      )

          lowerCamelCase__       :						List[Any]        =							dqa_pipeline({'''image''': image, '''question''': question}		, top_k=2      )
          self.assertEqual(nested_simplify(A		, decimals=4      )		, A      )

          # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
          # Empty answer probably
          lowerCamelCase__       :						Optional[Any]        =							'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
          lowerCamelCase__       :						int        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(A		, []      )

          # We can optionnally pass directly the words and bounding boxes
          lowerCamelCase__       :						Tuple        =							'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
          lowerCamelCase__       :						Optional[Any]        =							[]
          lowerCamelCase__       :						Dict        =							[]
          lowerCamelCase__       :						Dict        =							dqa_pipeline(image=A		, question=A		, words=A		, boxes=A		, top_k=2      )
          self.assertEqual(A		, []      )







       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->str:
          lowerCamelCase__       :						List[Any]        =							pipeline(
              '''document-question-answering'''		, model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''		, revision='''9977165'''		, )
          lowerCamelCase__       :						Union[str, Any]        =							INVOICE_URL
          lowerCamelCase__       :						int        =							'''What is the invoice number?'''

          lowerCamelCase__       :						Optional[int]        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
              ]		, )

          lowerCamelCase__       :						Union[str, Any]        =							dqa_pipeline({'''image''': image, '''question''': question}		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
              ]		, )

          lowerCamelCase__       :						int        =							dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  [
                      {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                      {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  ],
              ]
              * 2		, )







       @slow
       @require_torch
       @require_detectrona
       @require_pytesseract
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Tuple:
          lowerCamelCase__       :						Any        =							pipeline(
              '''document-question-answering'''		, model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''		, revision='''9977165'''		, max_seq_len=5_0		, )
          lowerCamelCase__       :						Optional[int]        =							INVOICE_URL
          lowerCamelCase__       :						Union[str, Any]        =							'''What is the invoice number?'''

          lowerCamelCase__       :						Tuple        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
                  {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
              ]		, )

          lowerCamelCase__       :						Optional[int]        =							dqa_pipeline({'''image''': image, '''question''': question}		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
                  {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
              ]		, )

          lowerCamelCase__       :						Optional[Any]        =							dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  [
                      {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
                      {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  ]
              ]
              * 2		, )







       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          lowerCamelCase__       :						int        =							AutoTokenizer.from_pretrained(
              '''impira/layoutlm-document-qa'''		, revision='''3dc6de3'''		, add_prefix_space=A      )
          lowerCamelCase__       :						str        =							pipeline(
              '''document-question-answering'''		, model='''impira/layoutlm-document-qa'''		, tokenizer=A		, revision='''3dc6de3'''		, )
          lowerCamelCase__       :						Union[str, Any]        =							INVOICE_URL
          lowerCamelCase__       :						str        =							'''What is the invoice number?'''

          lowerCamelCase__       :						Dict        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
              ]		, )

          lowerCamelCase__       :						int        =							dqa_pipeline({'''image''': image, '''question''': question}		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
              ]		, )

          lowerCamelCase__       :						Tuple        =							dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  [
                      {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                      {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
                  ]
              ]
              * 2		, )

          lowerCamelCase__       :						Optional[int]        =							list(zip(*apply_tesseract(load_image(A      )		, A		, ''''''      )      )      )

          # This model should also work if `image` is set to None
          lowerCamelCase__       :						Any        =							dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 2_3, '''end''': 2_3},
              ]		, )







       @slow
       @require_torch
       @require_pytesseract
       @require_vision
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[Any]:
          lowerCamelCase__       :						str        =							AutoTokenizer.from_pretrained(
              '''impira/layoutlm-document-qa'''		, revision='''3dc6de3'''		, add_prefix_space=A      )
          lowerCamelCase__       :						Union[str, Any]        =							pipeline(
              '''document-question-answering'''		, model='''impira/layoutlm-document-qa'''		, tokenizer=A		, revision='''3dc6de3'''		, max_seq_len=5_0		, )
          lowerCamelCase__       :						str        =							INVOICE_URL
          lowerCamelCase__       :						Any        =							'''What is the invoice number?'''

          lowerCamelCase__       :						Optional[Any]        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
              ]		, )

          lowerCamelCase__       :						Any        =							dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  [
                      {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                      {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  ]
              ]
              * 2		, )

          lowerCamelCase__       :						str        =							list(zip(*apply_tesseract(load_image(A      )		, A		, ''''''      )      )      )

          # This model should also work if `image` is set to None
          lowerCamelCase__       :						str        =							dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}		, top_k=2      )
          self.assertEqual(
              nested_simplify(A		, decimals=4      )		, [
                  {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
                  {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 1_6, '''end''': 1_6},
              ]		, )







       @slow
       @require_torch
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->Tuple:
          lowerCamelCase__       :						Tuple        =							pipeline(
              '''document-question-answering'''		, model='''naver-clova-ix/donut-base-finetuned-docvqa'''		, tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa'''      )		, feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa'''		, )

          lowerCamelCase__       :						Optional[int]        =							INVOICE_URL
          lowerCamelCase__       :						Any        =							'''What is the invoice number?'''
          lowerCamelCase__       :						Dict        =							dqa_pipeline(image=A		, question=A		, top_k=2      )
          self.assertEqual(nested_simplify(A		, decimals=4      )		, [{'''answer''': '''us-001'''}]      )







       @require_tf
       @unittest.skip('''Document question answering not implemented in TF'''      )
       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[Any]:
          pass









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


_A :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
   lowerCamelCase__       :						List[str]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)
   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Any        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						int        =							ly_weight['''attention''']
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Tuple        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						str        =							ly_weight['''attention''']

      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Tuple        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)

   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

   lowerCamelCase__       :						str        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(
       torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T					)					)

   for lyr_num, lyr in enumerate(model.decoders					):
      lowerCamelCase__       :						List[Any]        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						Any        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T					)					)

      lowerCamelCase__       :						Optional[Any]        =							ly_weight['''self_attention''']
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

      lowerCamelCase__       :						Dict        =							ly_weight['''MultiHeadDotProductAttention_0''']
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T					)					)

   return model



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







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							[
       '''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()''',
   ]

   lowerCamelCase__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

   lowerCamelCase__       :						int        =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,  variance_type='''fixed_large'''					)

   lowerCamelCase__       :						str        =							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'''      ,  )

   lowerCamelCase__       :						int        =							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'''      ,  )

   lowerCamelCase__       :						Optional[int]        =							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      ,  )

   lowerCamelCase__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[Any]        =							SpectrogramDiffusionPipeline(
       notes_encoder=UpperCAmelCase      ,  continuous_encoder=UpperCAmelCase      ,  decoder=UpperCAmelCase      ,  scheduler=UpperCAmelCase      ,  melgan=UpperCAmelCase      ,  )
   if args.save:
      pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
       _A :   int  			=  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.',
       )
       _A :   Tuple  			=  parser.parse_args()

       main(args)



