

import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


__A     :   List[str]    	=			random.Random()



def    snake_case_(_UpperCamelCase				, _UpperCamelCase=1.0				, _UpperCamelCase=None				, _UpperCamelCase=None  )							->			str:



  """simple docstring"""






  if rng is None:
    _snake_case		=							global_rng

  _snake_case		=							[]
  for batch_idx in range(shape[0]  ):
    values.append([]  )
    for _ in range(shape[1]  ):
      values[-1].append(rng.random() * scale  )

  return values



@require_torch
class 						lowercase_					(						unittest.TestCase  ):



      def __init__(    self   :       List[str]    ,      A__   :       Any    ,      A__   :       Union[str, Any]=7    ,      A__   :       Optional[int]=400    ,      A__   :       str=2000    ,      A__   :       List[Any]=1    ,      A__   :       Optional[int]=0.0    ,      A__   :       List[Any]=24000    ,      A__   :       List[str]=True    ,      )					->  Dict:
        _snake_case		=							parent
        _snake_case		=							batch_size
        _snake_case		=							min_seq_length
        _snake_case		=							max_seq_length
        _snake_case		=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _snake_case		=							feature_size
        _snake_case		=							padding_value
        _snake_case		=							sampling_rate
        _snake_case		=							return_attention_mask



      def 		UpperCamelCase_	(    self   :       Dict	)					->  Any:
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
        }







      def 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       Any=False    ,      A__   :       int=False	)					->  Dict:
        def _flatten(A__   :       Optional[int]	):
          return list(itertools.chain(*_lowerCAmelCase	)	)

        if equal_length:
          _snake_case		=							floats_list((self.batch_size, self.max_seq_length)	)
        else:
          # make sure that inputs increase in size
          _snake_case		=							[
              _flatten(floats_list((x, self.feature_size)	)	)
              for x in range(self.min_seq_length    ,      self.max_seq_length    ,      self.seq_length_diff	)
          ]

        if numpify:
          _snake_case		=							[np.asarray(_lowerCAmelCase	) for x in audio_inputs]

        return audio_inputs



@require_torch
class 						lowercase_					(						A__     ,							unittest.TestCase  ):
      UpperCamelCase_    :	Any		   =       EncodecFeatureExtractor



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Any:
        _snake_case		=							EnCodecFeatureExtractionTester(self	)



      def 		UpperCamelCase_	(    self   :       Any	)					->  Union[str, Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _snake_case		=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
        # create three inputs of length 800, 1000, and 1200
        _snake_case		=							[floats_list((1, x)	)[0] for x in range(800    ,      1400    ,      200	)]
        _snake_case		=							[np.asarray(_lowerCAmelCase	) for audio_input in audio_inputs]

        # Test not batched input
        _snake_case		=							feat_extract(audio_inputs[0]    ,      return_tensors='''np'''	).input_values
        _snake_case		=							feat_extract(np_audio_inputs[0]    ,      return_tensors='''np'''	).input_values
        self.assertTrue(np.allclose(_lowerCAmelCase    ,      _lowerCAmelCase    ,      atol=1e-3	)	)

        # Test batched
        _snake_case		=							feat_extract(_lowerCAmelCase    ,      padding=_lowerCAmelCase    ,      return_tensors='''np'''	).input_values
        _snake_case		=							feat_extract(_lowerCAmelCase    ,      padding=_lowerCAmelCase    ,      return_tensors='''np'''	).input_values
        for enc_seq_a, enc_seq_a in zip(_lowerCAmelCase    ,      _lowerCAmelCase	):
          self.assertTrue(np.allclose(_lowerCAmelCase    ,      _lowerCAmelCase    ,      atol=1e-3	)	)



      def 		UpperCamelCase_	(    self   :       Dict	)					->  List[str]:
        _snake_case		=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
        _snake_case		=							np.random.rand(100	).astype(np.floataa	)
        _snake_case		=							np_audio_inputs.tolist()

        for inputs in [py_audio_inputs, np_audio_inputs]:
          _snake_case		=							feature_extractor.pad([{'''input_values''': inputs}]    ,      return_tensors='''np'''	)
          self.assertTrue(np_processed.input_values.dtype == np.floataa	)
          _snake_case		=							feature_extractor.pad([{'''input_values''': inputs}]    ,      return_tensors='''pt'''	)
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)



      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       List[str]	)					->  int:
        from datasets import load_dataset

        _snake_case		=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''    ,      '''clean'''    ,      split='''validation'''	)
        # automatic decoding with librispeech
        _snake_case		=							ds.sort('''id'''	).select(range(_lowerCAmelCase	)	)[:num_samples]['''audio''']

        return [x["array"] for x in audio_samples]



      def 		UpperCamelCase_	(    self   :       int	)					->  Any:
        # fmt: off
        _snake_case		=							torch.tensor(
            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]	)
        # fmt: on
        _snake_case		=							self._load_datasamples(1	)
        _snake_case		=							EncodecFeatureExtractor()
        _snake_case		=							feature_extractor(_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(input_values.shape    ,      (1, 1, 93680)	)
        self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,      _lowerCAmelCase    ,      atol=1e-6	)	)



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Union[str, Any]:
        # fmt: off
        _snake_case		=							torch.tensor(
            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]	)
        # fmt: on
        _snake_case		=							self._load_datasamples(1	)
        _snake_case		=							[np.tile(input_audio[0][None]    ,      reps=(2, 1)	)]
        input_audio[0][1] *= 0.5
        _snake_case		=							EncodecFeatureExtractor(feature_size=2	)
        _snake_case		=							feature_extractor(_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(input_values.shape    ,      (1, 2, 93680)	)
        self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,      _lowerCAmelCase    ,      atol=1e-6	)	)
        self.assertTrue(torch.allclose(input_values[0, 1, :30]    ,      EXPECTED_INPUT_VALUES * 0.5    ,      atol=1e-6	)	)







      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  int:
        _snake_case		=							self._load_datasamples(2	)
        # would be easier if the stride was like
        _snake_case		=							EncodecFeatureExtractor(feature_size=1    ,      chunk_length_s=1    ,      overlap=0.01	)

        # pad and trunc raise an error ?
        with self.assertRaisesRegex(
            _lowerCAmelCase    ,      '''^Both padding and truncation were set. Make sure you only set one.$'''    ,      ):
          _snake_case		=							feature_extractor(
              _lowerCAmelCase    ,      padding='''max_length'''    ,      truncation=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values

        # truncate to chunk
        _snake_case		=							feature_extractor(_lowerCAmelCase    ,      truncation=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (2, 1, 71520)	)  # 2 chunks

        # force truncate to max_length
        _snake_case		=							feature_extractor(
            _lowerCAmelCase    ,      truncation=_lowerCAmelCase    ,      max_length=48000    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (2, 1, 48000)	)

        # pad to chunk
        _snake_case		=							feature_extractor(_lowerCAmelCase    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(padded_outputs.shape    ,      (2, 1, 95280)	)

        # pad to chunk
        _snake_case		=							feature_extractor(_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (2, 1, 95280)	)

        # force pad to max length
        _snake_case		=							feature_extractor(
            _lowerCAmelCase    ,      padding='''max_length'''    ,      max_length=100000    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (2, 1, 100000)	)

        # force no pad
        with self.assertRaisesRegex(
            _lowerCAmelCase    ,      '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''    ,      ):
          _snake_case		=							feature_extractor(_lowerCAmelCase    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values

        _snake_case		=							feature_extractor(input_audio[0]    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (1, 1, 93680)	)

        # no pad if no chunk_length_s
        _snake_case		=							None
        with self.assertRaisesRegex(
            _lowerCAmelCase    ,      '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''    ,      ):
          _snake_case		=							feature_extractor(_lowerCAmelCase    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values

        _snake_case		=							feature_extractor(input_audio[0]    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (1, 1, 93680)	)

        # no pad if no overlap
        _snake_case		=							2
        _snake_case		=							None
        with self.assertRaisesRegex(
            _lowerCAmelCase    ,      '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''    ,      ):
          _snake_case		=							feature_extractor(_lowerCAmelCase    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values

        _snake_case		=							feature_extractor(input_audio[0]    ,      padding=_lowerCAmelCase    ,      return_tensors='''pt'''	).input_values
        self.assertEquals(truncated_outputs.shape    ,      (1, 1, 93680)	)




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_weight.chunk(3				, dim=0  )
  _snake_case,  _snake_case,  _snake_case		=							pt_attn_layer.in_proj_bias.chunk(3				, dim=0  )

  _snake_case		=							pt_attn_layer.out_proj.weight
  _snake_case		=							pt_attn_layer.out_proj.bias

  _snake_case		=							q_proj
  _snake_case		=							q_proj_bias

  _snake_case		=							k_proj
  _snake_case		=							k_proj_bias

  _snake_case		=							v_proj
  _snake_case		=							v_proj_bias

  _snake_case		=							out_proj_weights
  _snake_case		=							out_proj_bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






  copy_linear(hf_mlp.fca				, pt_mlp.c_fc  )
  copy_linear(hf_mlp.fca				, pt_mlp.c_proj  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							pt_linear.weight
  _snake_case		=							pt_linear.bias



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )
  copy_linear(hf_layer.layer_norma				, pt_layer.ln_a  )

  # copy MLP
  copy_mlp(hf_layer.mlp				, pt_layer.mlp  )

  # copy attn
  copy_attn_layer(hf_layer.self_attn				, pt_layer.attn  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  for hf_layer, pt_layer in zip(_UpperCamelCase				, _UpperCamelCase  ):
    copy_layer(_UpperCamelCase				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=							pt_model.token_embedding.weight
  _snake_case		=							pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm				, pt_model.ln_final  )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers				, pt_model.transformer.resblocks  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  _snake_case		=							pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model				, _UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm				, pt_model.visual.ln_pre  )
  copy_linear(hf_model.vision_model.post_layernorm				, pt_model.visual.ln_post  )

  # copy embeds
  _snake_case		=							pt_model.visual.conva.weight.data
  _snake_case		=							pt_model.visual.class_embedding
  _snake_case		=							pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers				, pt_model.visual.transformer.resblocks  )



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=None  )							->			Any:



  """simple docstring"""






  if config_path is not None:
    _snake_case		=							CLIPConfig.from_pretrained(_UpperCamelCase  )
  else:
    _snake_case		=							CLIPConfig(projection_dim=512				, text_config={}				, vision_config={}  )

  _snake_case		=							CLIPModel(_UpperCamelCase  ).eval()

  _snake_case,  _snake_case		=							load(_UpperCamelCase				, device='''cpu'''				, jit=_UpperCamelCase  )
  _snake_case		=							pt_model.eval()

  copy_text_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  copy_vison_model_and_projection(_UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							pt_model.logit_scale

  _snake_case		=							torch.arange(0				, 77  ).unsqueeze(0  )
  _snake_case		=							torch.randn(1				, 3				, 224				, 224  )

  _snake_case		=							hf_model(input_ids=_UpperCamelCase				, pixel_values=_UpperCamelCase				, return_dict=_UpperCamelCase  )
  _snake_case		=							hf_outputs.logits_per_image
  _snake_case		=							hf_outputs.logits_per_text
  _snake_case,  _snake_case		=							pt_model(_UpperCamelCase				, _UpperCamelCase  )

  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )
  assert torch.allclose(_UpperCamelCase				, _UpperCamelCase				, atol=1E-3  )

  hf_model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 __A    	=			parser.parse_args()

 convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

