





import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    FalconForCausalLM,
					    FalconForQuestionAnswering,
					    FalconForSequenceClassification,
					    FalconForTokenClassification,
					    FalconModel,
					)



class 							_UpperCamelCase					:

							def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]=3					,   _SCREAMING_SNAKE_CASE: Any=7					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: Optional[int]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=False					,   _SCREAMING_SNAKE_CASE: str=True					,   _SCREAMING_SNAKE_CASE: Any=99					,   _SCREAMING_SNAKE_CASE: List[str]=32					,   _SCREAMING_SNAKE_CASE: List[str]=5					,   _SCREAMING_SNAKE_CASE: Optional[int]=4					,   _SCREAMING_SNAKE_CASE: str=37					,   _SCREAMING_SNAKE_CASE: Optional[int]="gelu"					,   _SCREAMING_SNAKE_CASE: str=0.1					,   _SCREAMING_SNAKE_CASE: Any=0.1					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: Optional[Any]=16					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=2					,   _SCREAMING_SNAKE_CASE: Tuple=0.02					,   _SCREAMING_SNAKE_CASE: Optional[Any]=3					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=4					,   _SCREAMING_SNAKE_CASE: Tuple=None					,   )       -> Tuple:




														"""simple docstring"""


														UpperCamelCase_          =  parent
														UpperCamelCase_          =  batch_size
														UpperCamelCase_          =  seq_length
														UpperCamelCase_          =  is_training
														UpperCamelCase_          =  use_input_mask
														UpperCamelCase_          =  use_token_type_ids
														UpperCamelCase_          =  use_labels
														UpperCamelCase_          =  vocab_size
														UpperCamelCase_          =  hidden_size
														UpperCamelCase_          =  num_hidden_layers
														UpperCamelCase_          =  num_attention_heads
														UpperCamelCase_          =  intermediate_size
														UpperCamelCase_          =  hidden_act
														UpperCamelCase_          =  hidden_dropout_prob
														UpperCamelCase_          =  attention_probs_dropout_prob
														UpperCamelCase_          =  max_position_embeddings
														UpperCamelCase_          =  type_vocab_size
														UpperCamelCase_          =  type_sequence_label_size
														UpperCamelCase_          =  initializer_range
														UpperCamelCase_          =  num_labels
														UpperCamelCase_          =  num_choices
														UpperCamelCase_          =  scope

							def lowercase (		self: Any		)       -> Any:




														"""simple docstring"""


														UpperCamelCase_          =  ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size		)

														UpperCamelCase_          =  None
														if self.use_input_mask:
																					UpperCamelCase_          =  random_attention_mask([self.batch_size, self.seq_length]		)

														UpperCamelCase_          =  None

														UpperCamelCase_          =  None
														UpperCamelCase_          =  None
														UpperCamelCase_          =  None
														if self.use_labels:
																					UpperCamelCase_          =  ids_tensor([self.batch_size]					,   self.type_sequence_label_size		)
																					UpperCamelCase_          =  ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels		)
																					UpperCamelCase_          =  ids_tensor([self.batch_size]					,   self.num_choices		)

														UpperCamelCase_          =  self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def lowercase (		self: Any		)       -> Union[str, Any]:




														"""simple docstring"""


														return FalconConfig(
														    vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   is_decoder=_SCREAMING_SNAKE_CASE					,   initializer_range=self.initializer_range					,   pad_token_id=1					,   new_decoder_architecture=_SCREAMING_SNAKE_CASE					,   )

							def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Dict					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Optional[int]:




														"""simple docstring"""


														UpperCamelCase_          =  FalconModel(config=_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE		)
														self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)		)

							def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Tuple					,   )       -> str:




														"""simple docstring"""


														UpperCamelCase_          =  True
														UpperCamelCase_          =  FalconModel(_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(
														    _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   )
														UpperCamelCase_          =  model(
														    _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   )
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE		)
														self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)		)

							def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   )       -> int:




														"""simple docstring"""


														UpperCamelCase_          =  FalconForCausalLM(config=_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
														self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)		)

							def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Union[str, Any]					,   )       -> List[Any]:




														"""simple docstring"""


														UpperCamelCase_          =  True
														UpperCamelCase_          =  True
														UpperCamelCase_          =  FalconForCausalLM(config=_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()

														# first forward pass
														UpperCamelCase_          =  model(
														    _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   use_cache=_SCREAMING_SNAKE_CASE					,   )
														UpperCamelCase_          =  outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														UpperCamelCase_          =  ids_tensor((self.batch_size, 3)					,   config.vocab_size		)
														UpperCamelCase_          =  ids_tensor((self.batch_size, 3)					,   vocab_size=2		)

														# append to next input_ids and
														UpperCamelCase_          =  torch.cat([input_ids, next_tokens]					,   dim=-1		)
														UpperCamelCase_          =  torch.cat([input_mask, next_mask]					,   dim=-1		)

														UpperCamelCase_          =  model(
														    _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   output_hidden_states=_SCREAMING_SNAKE_CASE					,   )["hidden_states"][0]
														UpperCamelCase_          =  model(
														    _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   encoder_hidden_states=_SCREAMING_SNAKE_CASE					,   encoder_attention_mask=_SCREAMING_SNAKE_CASE					,   past_key_values=_SCREAMING_SNAKE_CASE					,   output_hidden_states=_SCREAMING_SNAKE_CASE					,   )["hidden_states"][0]

														# select random slice
														UpperCamelCase_          =  ids_tensor((1,)					,   output_from_past.shape[-1]		).item()
														UpperCamelCase_          =  output_from_no_past[:, -3:, random_slice_idx].detach()
														UpperCamelCase_          =  output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   atol=1e-3		)		)



							def lowercase (		self: Tuple		)       -> int:




														"""simple docstring"""


														UpperCamelCase_          =  self.prepare_config_and_inputs()
														(
														    (
														    UpperCamelCase_ 
														)       , (
														    UpperCamelCase_ 
														)       , (
														    UpperCamelCase_ 
														)       , (
														    UpperCamelCase_ 
														)       , (
														    UpperCamelCase_ 
														)       , (
														    UpperCamelCase_ 
														)       , (
														    UpperCamelCase_ 
														)       ,  
														)          =  config_and_inputs
														UpperCamelCase_          =  {"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict



@require_torch
class 							_UpperCamelCase					(	lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						unittest.TestCase     ):
							_UpperCamelCase				:					Optional[Any]         =   (
							    (
							        FalconModel,
							        FalconForCausalLM,
							        FalconForSequenceClassification,
							        FalconForTokenClassification,
							        FalconForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							_UpperCamelCase				:					Union[str, Any]         =   (FalconForCausalLM,) if is_torch_available() else ()
							_UpperCamelCase				:					Optional[int]         =   (
							    {
							        '''feature-extraction''': FalconModel,
							        '''text-classification''': FalconForSequenceClassification,
							        '''text-generation''': FalconForCausalLM,
							        '''question-answering''': FalconForQuestionAnswering,
							        '''token-classification''': FalconForTokenClassification,
							        '''zero-shot''': FalconForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_UpperCamelCase				:					Any         =   False
							_UpperCamelCase				:					int         =   False

							def lowercase (		self: str		)       -> Optional[int]:




														"""simple docstring"""


														UpperCamelCase_          =  FalconModelTester(self		)
														UpperCamelCase_          =  ConfigTester(self					,   config_class=_SCREAMING_SNAKE_CASE					,   hidden_size=37		)

							def lowercase (		self: Optional[Any]		)       -> Optional[int]:




														"""simple docstring"""


														self.config_tester.run_common_tests()

							def lowercase (		self: str		)       -> Any:




														"""simple docstring"""


														UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

							def lowercase (		self: List[Any]		)       -> Dict:




														"""simple docstring"""


														UpperCamelCase_       , *UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
														for alibi in [True, False]:
																					UpperCamelCase_          =  alibi
																					self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE					,   *_SCREAMING_SNAKE_CASE		)

							def lowercase (		self: Any		)       -> str:




														"""simple docstring"""


														UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase_          =  3
														UpperCamelCase_          =  input_dict["input_ids"]
														UpperCamelCase_          =  input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size		)
														UpperCamelCase_          =  FalconForSequenceClassification(_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
														self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)		)

							def lowercase (		self: Dict		)       -> List[str]:




														"""simple docstring"""


														UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase_          =  3
														UpperCamelCase_          =  "single_label_classification"
														UpperCamelCase_          =  input_dict["input_ids"]
														UpperCamelCase_          =  input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size		)
														UpperCamelCase_          =  FalconForSequenceClassification(_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
														self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)		)

							def lowercase (		self: List[str]		)       -> int:




														"""simple docstring"""


														UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase_          =  input_dict["input_ids"]
														UpperCamelCase_          =  FalconForCausalLM(_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   use_cache=_SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  input_ids.shape[0]
														UpperCamelCase_          =  model._convert_to_rw_cache(result.past_key_values		)
														UpperCamelCase_          =  model._convert_cache_to_standard_format(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
														for layer in range(len(_SCREAMING_SNAKE_CASE		)		):
																					for tensor_idx in range(2		):
																												self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3		)
																												self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4		)
																												self.assertTrue(
																												    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]		)		)

							def lowercase (		self: Union[str, Any]		)       -> Any:




														"""simple docstring"""


														UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase_          =  3
														UpperCamelCase_          =  "multi_label_classification"
														UpperCamelCase_          =  input_dict["input_ids"]
														UpperCamelCase_          =  input_ids.ne(1		).to(_SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  ids_tensor(
														    [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size		).to(torch.float		)
														UpperCamelCase_          =  FalconForSequenceClassification(_SCREAMING_SNAKE_CASE		)
														model.to(_SCREAMING_SNAKE_CASE		)
														model.eval()
														UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   labels=_SCREAMING_SNAKE_CASE		)
														self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)		)



							def lowercase (		self: List[Any]		)       -> List[Any]:




														"""simple docstring"""


														for model_class in self.all_generative_model_classes:
																					UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

																					# If it doesn't support cache, pass the test
																					if not hasattr(_SCREAMING_SNAKE_CASE					,   "use_cache"		):
																												return

																					UpperCamelCase_          =  model_class(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
																					if "use_cache" not in inputs:
																												UpperCamelCase_          =  True
																					UpperCamelCase_          =  model(**_SCREAMING_SNAKE_CASE		)

																					# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
																					if "past_key_values" not in outputs:
																												return

																					UpperCamelCase_          =  (
																					    getattr(_SCREAMING_SNAKE_CASE					,   "decoder_layers"					,   _SCREAMING_SNAKE_CASE		)
																					    or getattr(_SCREAMING_SNAKE_CASE					,   "num_decoder_layers"					,   _SCREAMING_SNAKE_CASE		)
																					    or config.num_hidden_layers
																					)
																					UpperCamelCase_          =  getattr(_SCREAMING_SNAKE_CASE					,   "num_kv_heads"					,   config.num_attention_heads		)
																					UpperCamelCase_          =  getattr(_SCREAMING_SNAKE_CASE					,   "d_model"					,   config.hidden_size		)
																					UpperCamelCase_          =  embed_dim // num_attention_heads

																					UpperCamelCase_          =  outputs["past_key_values"]
																					self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

																					UpperCamelCase_       , UpperCamelCase_          =  inputs["input_ids"].shape
																					for i in range(_SCREAMING_SNAKE_CASE		):
																												if config.new_decoder_architecture:
																																			UpperCamelCase_          =  config.num_attention_heads
																												elif config.multi_query:
																																			UpperCamelCase_          =  1
																												self.assertEqual(len(past_kv[0]		)					,   2		)  # K V for the decoder = 2
																												self.assertEqual(
																												    past_kv[i][0].shape					,   (batch_size, num_attention_heads, seq_length, per_head_embed_dim)		)
																												self.assertEqual(
																												    past_kv[i][1].shape					,   (batch_size, num_attention_heads, seq_length, per_head_embed_dim)		)



@require_torch
class 							_UpperCamelCase					(	unittest.TestCase     ):

							@slow
							def lowercase (		self: Tuple		)       -> int:




														"""simple docstring"""


														UpperCamelCase_          =  AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"		)
														UpperCamelCase_          =  FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"		)
														model.eval()
														model.to(_SCREAMING_SNAKE_CASE		)
														UpperCamelCase_          =  tokenizer("My favorite food is"					,   return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  (
														    "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
														)

														UpperCamelCase_          =  model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=19		)
														UpperCamelCase_          =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)[0]

														self.assertEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

							@slow
							def lowercase (		self: Dict		)       -> Dict:




														"""simple docstring"""


														for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
																					UpperCamelCase_          =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)
																					UpperCamelCase_          =  FalconForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
																					model.eval()
																					model.to(_SCREAMING_SNAKE_CASE		)
																					UpperCamelCase_          =  tokenizer("My favorite food is"					,   return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

																					# We just test that these run without errors - the models are randomly initialized
																					# and so the actual text outputs will be garbage
																					model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=4		)
																					model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=4		)
																					model.generate(**_SCREAMING_SNAKE_CASE					,   num_beams=2					,   max_new_tokens=4		)



							@slow
							def lowercase (		self: Union[str, Any]		)       -> Any:




														"""simple docstring"""


														with torch.no_grad():
																					for repo in [
																					    "Rocketknight1/falcon-rw-1b",
																					    "Rocketknight1/tiny-random-falcon-7b",
																					    "Rocketknight1/tiny-random-falcon-40b",
																					]:
																												UpperCamelCase_          =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)
																												UpperCamelCase_          =  FalconForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE		)
																												model.eval()
																												model.to(device=_SCREAMING_SNAKE_CASE		)
																												UpperCamelCase_          =  tokenizer("My favorite food is"					,   return_tensors="pt"		).to(_SCREAMING_SNAKE_CASE		)

																												# Test results are the same with and without cache
																												UpperCamelCase_          =  model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=20					,   use_cache=_SCREAMING_SNAKE_CASE		)
																												UpperCamelCase_          =  model.generate(**_SCREAMING_SNAKE_CASE					,   do_sample=_SCREAMING_SNAKE_CASE					,   max_new_tokens=20					,   use_cache=_SCREAMING_SNAKE_CASE		)
																												self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0		)








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()
_UpperCAmelCase        =				logging.get_logger('transformers.models.speecht5')

_UpperCAmelCase        =				{
    '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',
}
_UpperCAmelCase        =				{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_UpperCAmelCase        =				{
    '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',
}
_UpperCAmelCase        =				{
    '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',
}
_UpperCAmelCase        =				{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_UpperCAmelCase        =				{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_UpperCAmelCase        =				{
    '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',
}
_UpperCAmelCase        =				{
    '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',
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				[]
_UpperCAmelCase        =				[
    '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',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Any:
       for attribute in key.split("."  ):
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  )

       if weight_type is not None:
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  ).shape
       else:
              UpperCamelCase_          =  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":
              UpperCamelCase_          =  value
       elif weight_type == "weight_g":
              UpperCamelCase_          =  value
       elif weight_type == "weight_v":
              UpperCamelCase_          =  value
       elif weight_type == "bias":
              UpperCamelCase_          =  value
       elif weight_type == "running_mean":
              UpperCamelCase_          =  value
       elif weight_type == "running_var":
              UpperCamelCase_          =  value
       elif weight_type == "num_batches_tracked":
              UpperCamelCase_          =  value
       else:
              UpperCamelCase_          =  value

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       for key in ignore_keys:
              if key.endswith(".*"  ):
                     if name.startswith(key[:-1]  ):
                            return True
              elif ".*." in key:
                     UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[Any]:
       UpperCamelCase_          =  []

       if task == "s2t":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2T
              UpperCamelCase_          =  IGNORE_KEYS_S2T
       elif task == "t2s":
              UpperCamelCase_          =  None
              UpperCamelCase_          =  MAPPING_T2S
              UpperCamelCase_          =  IGNORE_KEYS_T2S
       elif task == "s2s":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2S
              UpperCamelCase_          =  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

              UpperCamelCase_          =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		hf_model.config.feat_extract_norm == "group"					,		)
                     UpperCamelCase_          =  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:
                                   UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                                   if prefix in name and suffix in name:
                                          UpperCamelCase_          =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   UpperCamelCase_          =  True
                                   if "*" in mapped_key:
                                          UpperCamelCase_          =  name.split(UpperCamelCase_  )[0].split("."  )[-2]
                                          UpperCamelCase_          =  mapped_key.replace("*"					,		UpperCamelCase_  )
                                   if "weight_g" in name:
                                          UpperCamelCase_          =  "weight_g"
                                   elif "weight_v" in name:
                                          UpperCamelCase_          =  "weight_v"
                                   elif "bias" in name:
                                          UpperCamelCase_          =  "bias"
                                   elif "weight" in name:
                                          UpperCamelCase_          =  "weight"
                                   elif "running_mean" in name:
                                          UpperCamelCase_          =  "running_mean"
                                   elif "running_var" in name:
                                          UpperCamelCase_          =  "running_var"
                                   elif "num_batches_tracked" in name:
                                          UpperCamelCase_          =  "num_batches_tracked"
                                   else:
                                          UpperCamelCase_          =  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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       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:
                     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.'''  )
                     UpperCamelCase_          =  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.'''  )
                     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:
                     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.'''  )
                     UpperCamelCase_          =  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.'''  )
                     UpperCamelCase_          =  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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		str:
       if config_path is not None:
              UpperCamelCase_          =  SpeechTaConfig.from_pretrained(UpperCamelCase_  )
       else:
              UpperCamelCase_          =  SpeechTaConfig()

       if task == "s2t":
              UpperCamelCase_          =  config.max_text_positions
              UpperCamelCase_          =  SpeechTaForSpeechToText(UpperCamelCase_  )
       elif task == "t2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  600
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForTextToSpeech(UpperCamelCase_  )
       elif task == "s2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForSpeechToSpeech(UpperCamelCase_  )
       else:
              raise ValueError(F'''Unknown task name: {task}'''  )

       if vocab_path:
              UpperCamelCase_          =  SpeechTaTokenizer(UpperCamelCase_					,		model_max_length=config.max_text_positions  )

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

       UpperCamelCase_          =  SpeechTaFeatureExtractor()
       UpperCamelCase_          =  SpeechTaProcessor(tokenizer=UpperCamelCase_					,		feature_extractor=UpperCamelCase_  )
       processor.save_pretrained(UpperCamelCase_  )

       UpperCamelCase_          =  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__":
     _UpperCAmelCase        =				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.'
     )

     _UpperCAmelCase        =				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,
     )

