"""simple docstring"""







import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_a  :  Tuple=				logging.get_logger("transformers.models.speecht5")

_a  :  int=				{
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
_a  :  Union[str, Any]=				{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
_a  :  Any=				{
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
_a  :  str=				{
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
_a  :  str=				{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
_a  :  str=				{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
_a  :  int=				{
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
_a  :  int=				{
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
_a  :  List[Any]=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_a  :  List[Any]=				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a  :  Optional[Any]=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a  :  int=				[]
_a  :  int=				[
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
_a  :  Tuple=				IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
_a  :  Tuple=				IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
_a  :  Union[str, Any]=				IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	for attribute in key.split('.'						):
		__snake_case    :							int   	=				getattr(UpperCAmelCase_    ,							UpperCAmelCase_						)

	if weight_type is not None:
		__snake_case    :							Optional[int]   	=				getattr(UpperCAmelCase_    ,							UpperCAmelCase_						).shape
	else:
		__snake_case    :							Optional[int]   	=				hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    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":
		__snake_case    :							int   	=				value
	elif weight_type == "weight_g":
		__snake_case    :							Any   	=				value
	elif weight_type == "weight_v":
		__snake_case    :							Optional[int]   	=				value
	elif weight_type == "bias":
		__snake_case    :							Optional[int]   	=				value
	elif weight_type == "running_mean":
		__snake_case    :							Union[str, Any]   	=				value
	elif weight_type == "running_var":
		__snake_case    :							Optional[int]   	=				value
	elif weight_type == "num_batches_tracked":
		__snake_case    :							List[Any]   	=				value
	else:
		__snake_case    :							List[Any]   	=				value

	logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Any						)							->    Tuple:

	'''simple docstring'''





	for key in ignore_keys:
		if key.endswith('.*'						):
			if name.startswith(key[:-1]						):
				return True
		elif ".*." in key:
			__snake_case	,       __snake_case    :							Tuple   	=				key.split('.*.'						)
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       Union[str, Any]    ,							UpperCAmelCase_					:       Dict						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							str   	=				[]

	if task == "s2t":
		__snake_case    :							str   	=				hf_model.speechta.encoder.prenet.feature_encoder
		__snake_case    :							Union[str, Any]   	=				MAPPING_S2T
		__snake_case    :							List[str]   	=				IGNORE_KEYS_S2T
	elif task == "t2s":
		__snake_case    :							List[str]   	=				None
		__snake_case    :							str   	=				MAPPING_T2S
		__snake_case    :							str   	=				IGNORE_KEYS_T2S
	elif task == "s2s":
		__snake_case    :							Tuple   	=				hf_model.speechta.encoder.prenet.feature_encoder
		__snake_case    :							Union[str, Any]   	=				MAPPING_S2S
		__snake_case    :							Optional[Any]   	=				IGNORE_KEYS_S2S
	else:
		raise ValueError(F"Unsupported task: {task}"						)

	for name, value in fairseq_dict.items():
		if should_ignore(UpperCAmelCase_    ,							UpperCAmelCase_						):
			logger.info(F"{name} was ignored"						)
			continue

		__snake_case    :							Optional[int]   	=				False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							hf_model.config.feat_extract_norm == 'group'    ,							)
			__snake_case    :							Optional[Any]   	=				True
		else:
			for key, mapped_key in MAPPING.items():
				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

				if "*" in key:
					__snake_case	,       __snake_case    :							Tuple   	=				key.split('.*.'						)
					if prefix in name and suffix in name:
						__snake_case    :							Optional[Any]   	=				suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					__snake_case    :							Optional[Any]   	=				True
					if "*" in mapped_key:
						__snake_case    :							Tuple   	=				name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						__snake_case    :							Tuple   	=				mapped_key.replace('*'    ,							UpperCAmelCase_						)
					if "weight_g" in name:
						__snake_case    :							List[str]   	=				'weight_g'
					elif "weight_v" in name:
						__snake_case    :							int   	=				'weight_v'
					elif "bias" in name:
						__snake_case    :							int   	=				'bias'
					elif "weight" in name:
						__snake_case    :							Tuple   	=				'weight'
					elif "running_mean" in name:
						__snake_case    :							List[Any]   	=				'running_mean'
					elif "running_var" in name:
						__snake_case    :							Optional[Any]   	=				'running_var'
					elif "num_batches_tracked" in name:
						__snake_case    :							int   	=				'num_batches_tracked'
					else:
						__snake_case    :							Optional[Any]   	=				None
					set_recursively(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

	logger.warning(F"Unused weights: {unused_weights}"						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       Optional[int]						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Any   	=				full_name.split('conv_layers.'						)[-1]
	__snake_case    :							Dict   	=				name.split('.'						)
	__snake_case    :							Optional[int]   	=				int(items[0]						)
	__snake_case    :							Union[str, Any]   	=				int(items[1]						)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."						)
			__snake_case    :							Optional[int]   	=				value
			logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."						)
			__snake_case    :							List[str]   	=				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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."						)
			__snake_case    :							Optional[int]   	=				value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."						)
			__snake_case    :							Tuple   	=				value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
	else:
		unused_weights.append(UpperCAmelCase_						)
@torch.no_grad()
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       List[Any]=None    ,							UpperCAmelCase_					:       Any=None    ,							UpperCAmelCase_					:       Tuple=None    ,							)							->    int:

	'''simple docstring'''





	if config_path is not None:
		__snake_case    :							int   	=				SpeechTaConfig.from_pretrained(UpperCAmelCase_						)
	else:
		__snake_case    :							int   	=				SpeechTaConfig()

	if task == "s2t":
		__snake_case    :							Optional[Any]   	=				config.max_text_positions
		__snake_case    :							Dict   	=				SpeechTaForSpeechToText(UpperCAmelCase_						)
	elif task == "t2s":
		__snake_case    :							Any   	=				18_76
		__snake_case    :							Optional[int]   	=				6_00
		__snake_case    :							str   	=				config.max_speech_positions
		__snake_case    :							str   	=				SpeechTaForTextToSpeech(UpperCAmelCase_						)
	elif task == "s2s":
		__snake_case    :							Optional[int]   	=				18_76
		__snake_case    :							Dict   	=				config.max_speech_positions
		__snake_case    :							Optional[Any]   	=				SpeechTaForSpeechToSpeech(UpperCAmelCase_						)
	else:
		raise ValueError(F"Unknown task name: {task}"						)

	if vocab_path:
		__snake_case    :							List[str]   	=				SpeechTaTokenizer(UpperCAmelCase_    ,							model_max_length=config.max_text_positions						)

		# Mask token behaves like a normal word, i.e. include the space before it
		__snake_case    :							Dict   	=				AddedToken('<mask>'    ,							lstrip=UpperCAmelCase_    ,							rstrip=UpperCAmelCase_						)
		__snake_case    :							Optional[int]   	=				mask_token
		tokenizer.add_special_tokens({'mask_token': mask_token}						)
		tokenizer.add_tokens(['<ctc_blank>']						)

	__snake_case    :							List[str]   	=				SpeechTaFeatureExtractor()
	__snake_case    :							int   	=				SpeechTaProcessor(tokenizer=UpperCAmelCase_    ,							feature_extractor=UpperCAmelCase_						)
	processor.save_pretrained(UpperCAmelCase_						)

	__snake_case    :							int   	=				torch.load(UpperCAmelCase_						)
	recursively_load_weights(fairseq_checkpoint['model']    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)

	model.save_pretrained(UpperCAmelCase_						)

	if repo_id:
		print('Pushing to the hub...'						)
		processor.push_to_hub(UpperCAmelCase_						)
		model.push_to_hub(UpperCAmelCase_						)


if __name__ == "__main__":
	_a  :  Tuple=				argparse.ArgumentParser()
	parser.add_argument(
	    "--task",
	    default="s2t",
	    type=str,
	    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
	)
	parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
	parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
	parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
	)
	parser.add_argument(
	    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
	)

	_a  :  Dict=				parser.parse_args()
	convert_speechta_checkpoint(
	    args.task,
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.vocab_path,
	    args.push_to_hub,
	)






"""simple docstring"""







import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers import AutoModelForCausalLM

@require_torch
class 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							Optional[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Any   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							List[str]   	=				TextStreamer(_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							Optional[Any]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

					__snake_case    :							str   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							int   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							int   	=				tokenizer.decode(greedy_ids[0])

					__snake_case    :							int   	=				TextIteratorStreamer(_A)
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							Any   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()
					__snake_case    :							Dict   	=				''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Optional[int]   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							Union[str, Any]   	=				greedy_ids[:, input_ids.shape[1] :]
					__snake_case    :							Optional[Any]   	=				tokenizer.decode(new_greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							str   	=				TextStreamer(_A			,						skip_prompt=_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							List[str]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			Dict:
					# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
					# with actual models -- the dummy models' tokenizers are not aligned with their models, and
					# `skip_special_tokens=True` has no effect on them
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[str]   	=				torch.ones((1, 5)			,						device=_A).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						__snake_case    :							Tuple   	=				TextStreamer(_A			,						skip_special_tokens=_A)
						model.generate(_A			,						max_new_tokens=1			,						do_sample=_A			,						streamer=_A)

					# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
					# re-tokenized, must only contain one token
					__snake_case    :							Tuple   	=				cs.out[:-1]  # Remove the final "\n"
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt')
					self.assertEqual(streamer_text_tokenized.input_ids.shape			,						(1, 1))






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							str   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()

					# The streamer will timeout after 0.001 seconds, so an exception will be raised
					with self.assertRaises(_A):
						__snake_case    :							Any   	=				''
						for new_text in streamer:
							streamer_text += new_text





