

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	lowerCAmelCase__		      =       """pt"""
elif is_tf_available():
	lowerCAmelCase__		      =       """tf"""
else:
	lowerCAmelCase__		      =       """jax"""

class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				PerceiverTokenizer
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							super().setUp()
							A__						=	PerceiverTokenizer()
							tokenizer.save_pretrained(self.tmpdirname				)


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"				)


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  PerceiverTokenizer:
							'''simple docstring'''






							return self.tokenizer_class.from_pretrained(self.tmpdirname					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=False					,					lowercase=20					,					lowercase=5				)     ->  Tuple[str, list]:
							'''simple docstring'''






							A__						=	[]
							for i in range(len(lowercase				)				):
											try:
															A__						=	tokenizer.decode([i]					,					clean_up_tokenization_spaces=lowercase				)
											except UnicodeDecodeError:
															pass
											toks.append((i, tok)				)

							A__						=	list(filter(lambda lowercase				: re.match(R"^[ a-zA-Z]+$"					,					t[1]				)					,					lowercase				)				)
							A__						=	list(filter(lambda lowercase				: [t[0]] == tokenizer.encode(t[1]					,					add_special_tokens=lowercase				)					,					lowercase				)				)
							if max_length is not None and len(lowercase				) > max_length:
											A__						=	toks[:max_length]
							if min_length is not None and len(lowercase				) < min_length and len(lowercase				) > 0:
											while len(lowercase				) < min_length:
															A__						=	toks + toks
        # toks_str = [t[1] for t in toks]
							A__						=	[t[0] for t in toks]

							# Ensure consistency
							A__						=	tokenizer.decode(lowercase					,					clean_up_tokenization_spaces=lowercase				)
							if " " not in output_txt and len(lowercase				) > 1:
											A__						=	(
											    tokenizer.decode([toks_ids[0]]					,					clean_up_tokenization_spaces=lowercase				)
											    + " "
											    + tokenizer.decode(toks_ids[1:]					,					clean_up_tokenization_spaces=lowercase				)
											)
							if with_prefix_space:
											A__						=	" " + output_txt
							A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
							return output_txt, output_ids


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	"Unicode €."
							A__						=	tokenizer(lowercase				)
							A__						=	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
							self.assertEqual(encoded["input_ids"]					,					lowercase				)

							# decoding
							A__						=	tokenizer.decode(lowercase				)
							self.assertEqual(lowercase					,					"[CLS]Unicode €.[SEP]"				)

							A__						=	tokenizer("e è é ê ë"				)
							A__						=	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
							self.assertEqual(encoded["input_ids"]					,					lowercase				)
							# decoding
							A__						=	tokenizer.decode(lowercase				)
							self.assertEqual(lowercase					,					"[CLS]e è é ê ë[SEP]"				)

							# encode/decode, but with `encode` instead of `__call__`
							self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"				)				)					,					"[CLS]e è é ê ë[SEP]"				)


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	["A long paragraph for summarization.", "Another paragraph for summarization."]
							# fmt: off
							A__						=	[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
							# fmt: on
							A__						=	tokenizer(lowercase					,					padding=lowercase					,					return_tensors=lowercase				)
							self.assertIsInstance(lowercase					,					lowercase				)

							if FRAMEWORK != "jax":
											A__						=	list(batch.input_ids.numpy()[0]				)
							else:
											A__						=	list(batch.input_ids.tolist()[0]				)

							self.assertListEqual(lowercase					,					lowercase				)

							self.assertEqual((2, 38)					,					batch.input_ids.shape				)
							self.assertEqual((2, 38)					,					batch.attention_mask.shape				)


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	["A long paragraph for summarization.", "Another paragraph for summarization."]
							A__						=	tokenizer(lowercase					,					padding=lowercase					,					return_tensors=lowercase				)
							# check if input_ids are returned and no decoder_input_ids
							self.assertIn("input_ids"					,					lowercase				)
							self.assertIn("attention_mask"					,					lowercase				)
							self.assertNotIn("decoder_input_ids"					,					lowercase				)
							self.assertNotIn("decoder_attention_mask"					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	[
							    "Summary of the text.",
							    "Another summary.",
							]
							A__						=	tokenizer(
							    text_target=lowercase					,					max_length=32					,					padding="max_length"					,					truncation=lowercase					,					return_tensors=lowercase				)
							self.assertEqual(32					,					targets["input_ids"].shape[1]				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.get_tokenizers()
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															self.assertNotEqual(tokenizer.model_max_length					,					42				)

        # Now let's start the test
							A__						=	self.get_tokenizers()
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															# Isolate this from the other tests because we save additional tokens/etc
															A__						=	tempfile.mkdtemp()

															A__						=	" He is very happy, UNwant\u00E9d,running"
															A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															tokenizer.save_pretrained(lowercase				)

															A__						=	tokenizer.__class__.from_pretrained(lowercase				)
															A__						=	after_tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															self.assertListEqual(lowercase					,					lowercase				)

															shutil.rmtree(lowercase				)

							A__						=	self.get_tokenizers(model_max_length=42				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															# Isolate this from the other tests because we save additional tokens/etc
															A__						=	tempfile.mkdtemp()

															A__						=	" He is very happy, UNwant\u00E9d,running"
															tokenizer.add_tokens(["bim", "bambam"]				)
															A__						=	tokenizer.additional_special_tokens
															additional_special_tokens.append("new_additional_special_token"				)
															tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}				)
															A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															tokenizer.save_pretrained(lowercase				)

															A__						=	tokenizer.__class__.from_pretrained(lowercase				)
															A__						=	after_tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															self.assertListEqual(lowercase					,					lowercase				)
															self.assertIn("new_additional_special_token"					,					after_tokenizer.additional_special_tokens				)
															self.assertEqual(after_tokenizer.model_max_length					,					42				)

															A__						=	tokenizer.__class__.from_pretrained(lowercase					,					model_max_length=43				)
															self.assertEqual(tokenizer.model_max_length					,					43				)

															shutil.rmtree(lowercase				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	[]
							if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

							if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

							for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer_utils.save_pretrained(lowercase				)

															with open(os.path.join(lowercase					,					"special_tokens_map.json"				)					,					encoding="utf-8"				) as json_file:
																			A__						=	json.load(lowercase				)

															with open(os.path.join(lowercase					,					"tokenizer_config.json"				)					,					encoding="utf-8"				) as json_file:
																			A__						=	json.load(lowercase				)

															A__						=	[F'<extra_id_{i}>' for i in range(125				)]

															A__						=	added_tokens_extra_ids + [
															    "an_additional_special_token"
															]
															A__						=	added_tokens_extra_ids + [
															    "an_additional_special_token"
															]

															with open(os.path.join(lowercase					,					"special_tokens_map.json"				)					,					"w"					,					encoding="utf-8"				) as outfile:
																			json.dump(lowercase					,					lowercase				)
															with open(os.path.join(lowercase					,					"tokenizer_config.json"				)					,					"w"					,					encoding="utf-8"				) as outfile:
																			json.dump(lowercase					,					lowercase				)

															# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
															# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
															# "special_tokens_map.json" files
															A__						=	tokenizer_class.from_pretrained(
															    lowercase					,					)
															self.assertIn(
															    "an_additional_special_token"					,					tokenizer_without_change_in_init.additional_special_tokens				)
															self.assertEqual(
															    ["an_additional_special_token"]					,					tokenizer_without_change_in_init.convert_ids_to_tokens(
															        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]				)				)					,					)

															# Now we test that we can change the value of additional_special_tokens in the from_pretrained
															A__						=	added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"					,					lstrip=lowercase				)]
															A__						=	tokenizer_class.from_pretrained(
															    lowercase					,					additional_special_tokens=lowercase					,					)

															self.assertIn("a_new_additional_special_token"					,					tokenizer.additional_special_tokens				)
															self.assertEqual(
															    ["a_new_additional_special_token"]					,					tokenizer.convert_ids_to_tokens(
															        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]				)				)					,					)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							self.assertEqual(tokenizer.decode([178]				)					,					"�"				)


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.get_tokenizers(fast=lowercase					,					do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__						=	["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
															A__						=	tokenizer.convert_tokens_to_string(lowercase				)

															self.assertIsInstance(lowercase					,					lowercase				)









import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						Optional[Any]      =      {
    '''post_extract_proj''': '''feature_projection.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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase_ :						int      =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]					)		->			Dict:






	"""simple docstring"""



	for attribute in key.split("""."""					):
		UpperCamelCase     :Dict          =	getattr(__magic_name__	,			__magic_name__					)

	if weight_type is not None:
		UpperCamelCase     :Optional[int]          =	getattr(__magic_name__	,			__magic_name__					).shape
	else:
		UpperCamelCase     :Optional[int]          =	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     :str          =	value
	elif weight_type == "weight_g":
		UpperCamelCase     :int          =	value
	elif weight_type == "weight_v":
		UpperCamelCase     :int          =	value
	elif weight_type == "bias":
		UpperCamelCase     :List[Any]          =	value
	else:
		UpperCamelCase     :Any          =	value

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  List[str]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[]
	UpperCamelCase     :Dict          =	fairseq_model.state_dict()

	UpperCamelCase     :int          =	hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCamelCase     :str          =	False
		if "conv_layers" in name:
			load_conv_layer(
			    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			hf_model.config.feat_extract_norm == """group"""	,			)
			UpperCamelCase     :Union[str, Any]          =	True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
					UpperCamelCase     :Optional[int]          =	True
					if "*" in mapped_key:
						UpperCamelCase     :List[Any]          =	name.split(__magic_name__					)[0].split("""."""					)[-2]
						UpperCamelCase     :int          =	mapped_key.replace("""*"""	,			__magic_name__					)
					if "weight_g" in name:
						UpperCamelCase     :List[Any]          =	"""weight_g"""
					elif "weight_v" in name:
						UpperCamelCase     :List[Any]          =	"""weight_v"""
					elif "bias" in name and "relative_attention_bias" not in name:
						UpperCamelCase     :Any          =	"""bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						UpperCamelCase     :List[str]          =	"""weight"""
					else:
						UpperCamelCase     :Optional[int]          =	None

					set_recursively(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)
				continue
		if not is_used:
			unused_weights.append(__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	full_name.split("""conv_layers."""					)[-1]
	UpperCamelCase     :int          =	name.split("""."""					)
	UpperCamelCase     :str          =	int(items[0]					)
	UpperCamelCase     :str          =	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     :Tuple          =	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     :Dict          =	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     :Tuple          =	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     :Union[str, Any]          =	value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(__magic_name__					)

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  str=None					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	torch.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	WavLMConfigOrig(checkpoint["""cfg"""]					)
	UpperCamelCase     :int          =	WavLMOrig(__magic_name__					)
	model.load_state_dict(checkpoint["""model"""]					)
	model.eval()

	if config_path is not None:
		UpperCamelCase     :List[Any]          =	WavLMConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :Any          =	WavLMConfig()

	UpperCamelCase     :Dict          =	WavLMModel(__magic_name__					)

	recursively_load_weights(__magic_name__	,			__magic_name__					)

	hf_wavlm.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						Union[str, Any]      =      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''')
	UpperCAmelCase_ :						Optional[int]      =      parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
