




import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					CLIPTokenizer
      lowerCAmelCase__			: str    =					CLIPTokenizerFast
      lowerCAmelCase__			: Dict    =					True
      lowerCAmelCase__			: Dict    =					{}
      lowerCAmelCase__			: str    =					False







      def       snake_case	(     self					: Dict				):
          super().setUp()

          # fmt: off
          __UpperCamelCase  		=						['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
          # fmt: on
          __UpperCamelCase  		=						dict(zip(snake_case ,       range(len(snake_case				)				)				)				)
          __UpperCamelCase  		=						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
          __UpperCamelCase  		=						{'''unk_token''': '''<unk>'''}

          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''vocab_file''']				)
          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''merges_file''']				)
          with open(self.vocab_file ,       '''w''' ,       encoding='''utf-8'''				) as fp:
              fp.write(json.dumps(snake_case				) + '''\n'''				)
          with open(self.merges_file ,       '''w''' ,       encoding='''utf-8'''				) as fp:
              fp.write('''\n'''.join(snake_case				)				)







      def       snake_case	(     self					: int ,       **snake_case					: List[Any]				):
          kwargs.update(self.special_tokens_map				)
          return CLIPTokenizer.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: List[str] ,       **snake_case					: Tuple				):
          kwargs.update(self.special_tokens_map				)
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: str				):
          __UpperCamelCase  		=						'''lower newer'''
          __UpperCamelCase  		=						'''lower newer'''
          return input_text, output_text







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						CLIPTokenizer(self.vocab_file ,       self.merges_file ,       **self.special_tokens_map				)
          __UpperCamelCase  		=						'''lower newer'''
          __UpperCamelCase  		=						['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
          __UpperCamelCase  		=						tokenizer.tokenize(snake_case				)
          self.assertListEqual(snake_case ,       snake_case				)

          __UpperCamelCase  		=						tokens + [tokenizer.unk_token]
          __UpperCamelCase  		=						[10, 2, 16, 9, 3, 2, 16, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case				) ,       snake_case				)







      @require_ftfy
      def       snake_case	(     self					: List[Any]				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                  __UpperCamelCase  		=						self.tokenizer_class.from_pretrained(snake_case ,       **snake_case				)
                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(snake_case ,       **snake_case				)

                  __UpperCamelCase  		=						'''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
                  __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                  __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                  self.assertListEqual(snake_case ,       snake_case				)

                  # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                  # with Tilde) encoded in 2 different ways
                  __UpperCamelCase  		=						'''xa\u0303y''' + ''' ''' + '''x\xe3y'''
                  __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                  __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                  self.assertListEqual(snake_case ,       snake_case				)

                  # Test that the tokenization is identical on unicode of space type
                  __UpperCamelCase  		=						[
                      '''\u0009''',  # (horizontal tab, '\t')
                      '''\u000B''',  # (vertical tab)
                      '''\u000C''',  # (form feed)
                      '''\u0020''',  # (space, ' ')
                      '''\u200E''',  # (left-to-right mark):w
                      '''\u200F''',  # (right-to-left mark)
                  ]
                  for unicode_seq in spaces_unicodes:
                      __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                      __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                      self.assertListEqual(snake_case ,       snake_case				)

                  # Test that the tokenization is identical on unicode of line break type
                  __UpperCamelCase  		=						[
                      '''\u000A''',  # (line feed, '\n')
                      '''\r\n''',  # (carriage return and line feed, '\r\n')
                      '''\u000D''',  # (carriage return, '\r')
                      '''\r''',  # (carriage return, '\r')
                      '''\u000D''',  # (carriage return, '\r')
                      '''\u2028''',  # (line separator)
                      '''\u2029''',  # (paragraph separator)
                      # "\u0085", # (next line)
                  ]

                  # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                  # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                  # space (and thus into an empty list).

                  for unicode_seq in line_break_unicodes:
                      __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                      __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                      self.assertListEqual(snake_case ,       snake_case				)







      def       snake_case	(     self					: Tuple				):
          # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                  __UpperCamelCase  		=						'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                  __UpperCamelCase  		=						F"{text_of_1_token} {text_of_1_token}"

                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(
                      snake_case ,       use_fast=snake_case ,       )
                  __UpperCamelCase  		=						tokenizer_r(snake_case ,       return_offsets_mapping=snake_case ,       add_special_tokens=snake_case				)
                  self.assertEqual(encoding.offset_mapping[0] ,       (0, len(snake_case				))				)
                  self.assertEqual(
                      encoding.offset_mapping[1] ,       (len(snake_case				) + 1, len(snake_case				) + 1 + len(snake_case				)) ,       )

                  __UpperCamelCase  		=						F" {text}"

                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(
                      snake_case ,       use_fast=snake_case ,       )
                  __UpperCamelCase  		=						tokenizer_r(snake_case ,       return_offsets_mapping=snake_case ,       add_special_tokens=snake_case				)
                  self.assertEqual(encoding.offset_mapping[0] ,       (1, 1 + len(snake_case				))				)
                  self.assertEqual(
                      encoding.offset_mapping[1] ,       (1 + len(snake_case				) + 1, 1 + len(snake_case				) + 1 + len(snake_case				)) ,       )







      def       snake_case	(     self					: Tuple				):
          # Test related to the breaking change introduced in transformers v4.17.0
          # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
          with self.assertRaises(snake_case				) as context:
              self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''				)

          self.assertTrue(
              context.exception.args[0].startswith(
                  '''The `backend_tokenizer` provided does not match the expected format.'''				)				)







      @require_ftfy
      def       snake_case	(     self					: Tuple				):
          super().test_tokenization_python_rust_equals()







      def       snake_case	(     self					: List[Any]				):
          # CLIP always lower cases letters
          pass












import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)

a_   		=     {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      )

    if weight_type is not None:
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      ).shape
    else:
        __UpperCamelCase  		=						hf_pointer.shape

    assert hf_shape == value.shape, (
        F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        F" {value.shape} for {full_name}"
    )

    if weight_type == "weight":
        __UpperCamelCase  		=						value
    elif weight_type == "weight_g":
        __UpperCamelCase  		=						value
    elif weight_type == "weight_v":
        __UpperCamelCase  		=						value
    elif weight_type == "bias":
        __UpperCamelCase  		=						value
    else:
        __UpperCamelCase  		=						value

    logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase  		=						False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase  		=						'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
                    __UpperCamelCase  		=						True
                    if "*" in mapped_key:
                        __UpperCamelCase  		=						name.split(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase  		=						mapped_key.replace('''*'''					,     lowercase_      )
                    if "weight_g" in name:
                        __UpperCamelCase  		=						'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase  		=						'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase  		=						'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase  		=						'''bias'''
                    else:
                        __UpperCamelCase  		=						None
                    set_recursively(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
                continue
        if not is_used:
            unused_weights.append(lowercase_      )

    logger.warning(F"Unused weights: {unused_weights}"      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						full_name.split('''conv_layers.'''      )[-1]
    __UpperCamelCase  		=						name.split('''.'''      )
    __UpperCamelCase  		=						int(items[0]      )
    __UpperCamelCase  		=						int(items[1]      )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                " found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
            )
            __UpperCamelCase  		=						value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						SEWConfig()
    if is_finetuned:
        __UpperCamelCase  		=						model.wav_encoder.wav_model.cfg
    else:
        __UpperCamelCase  		=						model.cfg

    __UpperCamelCase  		=						fs_config.conv_bias
    __UpperCamelCase  		=						eval(fs_config.conv_feature_layers      )
    __UpperCamelCase  		=						[x[0] for x in conv_layers]
    __UpperCamelCase  		=						[x[1] for x in conv_layers]
    __UpperCamelCase  		=						[x[2] for x in conv_layers]
    __UpperCamelCase  		=						'''gelu'''
    __UpperCamelCase  		=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase  		=						0.0
    __UpperCamelCase  		=						fs_config.activation_fn.name
    __UpperCamelCase  		=						fs_config.encoder_embed_dim
    __UpperCamelCase  		=						0.02
    __UpperCamelCase  		=						fs_config.encoder_ffn_embed_dim
    __UpperCamelCase  		=						1E-5
    __UpperCamelCase  		=						fs_config.encoder_layerdrop
    __UpperCamelCase  		=						fs_config.encoder_attention_heads
    __UpperCamelCase  		=						fs_config.conv_pos_groups
    __UpperCamelCase  		=						fs_config.conv_pos
    __UpperCamelCase  		=						len(lowercase_      )
    __UpperCamelCase  		=						fs_config.encoder_layers
    __UpperCamelCase  		=						fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        __UpperCamelCase  		=						model.cfg
        __UpperCamelCase  		=						fs_config.final_dropout
        __UpperCamelCase  		=						fs_config.layerdrop
    __UpperCamelCase  		=						fs_config.activation_dropout
    __UpperCamelCase  		=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    __UpperCamelCase  		=						fs_config.attention_dropout
    __UpperCamelCase  		=						fs_config.dropout_input
    __UpperCamelCase  		=						fs_config.dropout
    __UpperCamelCase  		=						fs_config.mask_channel_length
    __UpperCamelCase  		=						fs_config.mask_channel_prob
    __UpperCamelCase  		=						fs_config.mask_length
    __UpperCamelCase  		=						fs_config.mask_prob

    __UpperCamelCase  		=						'''Wav2Vec2FeatureExtractor'''
    __UpperCamelCase  		=						'''Wav2Vec2CTCTokenizer'''

    return config




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_=True      )       ->	Any:

    """simple docstring"""


    if is_finetuned:
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]					,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
    else:
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

    if config_path is not None:
        __UpperCamelCase  		=						SEWConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						convert_config(model[0]					,     lowercase_      )
    __UpperCamelCase  		=						model[0].eval()

    __UpperCamelCase  		=						True if config.feat_extract_norm == '''layer''' else False
    __UpperCamelCase  		=						WavaVecaFeatureExtractor(
        feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=lowercase_					,     return_attention_mask=lowercase_					,     )

    if is_finetuned:
        if dict_path:
            __UpperCamelCase  		=						Dictionary.load(lowercase_      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.pad_index
            __UpperCamelCase  		=						target_dict.bos_index
            __UpperCamelCase  		=						target_dict.eos_index
            __UpperCamelCase  		=						len(target_dict.symbols      )
            __UpperCamelCase  		=						os.path.join(lowercase_					,     '''vocab.json'''      )
            if not os.path.isdir(lowercase_      ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_      )      )
                return
            os.makedirs(lowercase_					,     exist_ok=lowercase_      )
            with open(lowercase_					,     '''w'''					,     encoding='''utf-8'''      ) as vocab_handle:
                json.dump(target_dict.indices					,     lowercase_      )
            __UpperCamelCase  		=						WavaVecaCTCTokenizer(
                lowercase_					,     unk_token=target_dict.unk_word					,     pad_token=target_dict.pad_word					,     bos_token=target_dict.bos_word					,     eos_token=target_dict.eos_word					,     word_delimiter_token='''|'''					,     do_lower_case=lowercase_					,     )
            __UpperCamelCase  		=						WavaVecaProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )
            processor.save_pretrained(lowercase_      )

        __UpperCamelCase  		=						SEWForCTC(lowercase_      )
    else:
        __UpperCamelCase  		=						SEWModel(lowercase_      )
        feature_extractor.save_pretrained(lowercase_      )

    recursively_load_weights(lowercase_					,     lowercase_					,     lowercase_      )

    hf_model.save_pretrained(lowercase_      )


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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 a_   		=     parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






