

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    _a						:


     '''simple docstring'''






     def __init__(			self   ,	A__   ,	A__=3   ,	A__=7   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=True   ,	A__=99   ,	A__=32   ,	A__=5   ,	A__=4   ,	A__=37   ,	A__="gelu"   ,	A__=0.1   ,	A__=0.1   ,	A__=512   ,	A__=16   ,	A__=2   ,	A__=0.0_2   ,	A__=3   ,	A__=4   ,	A__=None   ,	):
      A__							:   List[Any]					       =		parent
      A__							:   List[str]					       =		batch_size
      A__							:   Optional[int]					       =		seq_length
      A__							:   Optional[int]					       =		is_training
      A__							:   Any					       =		use_input_mask
      A__							:   Tuple					       =		use_token_type_ids
      A__							:   str					       =		use_labels
      A__							:   Tuple					       =		vocab_size
      A__							:   Any					       =		hidden_size
      A__							:   List[str]					       =		num_hidden_layers
      A__							:   Optional[int]					       =		num_attention_heads
      A__							:   Optional[Any]					       =		intermediate_size
      A__							:   Optional[Any]					       =		hidden_act
      A__							:   Tuple					       =		hidden_dropout_prob
      A__							:   Union[str, Any]					       =		attention_probs_dropout_prob
      A__							:   List[str]					       =		max_position_embeddings
      A__							:   Union[str, Any]					       =		type_vocab_size
      A__							:   str					       =		type_sequence_label_size
      A__							:   Tuple					       =		initializer_range
      A__							:   Tuple					       =		num_labels
      A__							:   Dict					       =		num_choices
      A__							:   List[str]					       =		scope






     def   __A      (			self      ):
      A__							:   Any					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

      A__							:   Any					       =		None
      if self.use_input_mask:
       A__							:   int					       =		random_attention_mask([self.batch_size, self.seq_length]      )

      A__							:   str					       =		None

      A__							:   Union[str, Any]					       =		None
      A__							:   List[str]					       =		None
      A__							:   Optional[Any]					       =		None
      if self.use_labels:
       A__							:   Any					       =		ids_tensor([self.batch_size]   ,	self.type_sequence_label_size      )
       A__							:   List[str]					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels      )
       A__							:   List[Any]					       =		ids_tensor([self.batch_size]   ,	self.num_choices      )

      A__							:   Dict					       =		self.get_config()

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






     def   __A      (			self      ):
      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=A__   ,	initializer_range=self.initializer_range   ,	pad_token_id=1   ,	new_decoder_architecture=A__   ,	)






     def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__      ):
      A__							:   List[str]					       =		FalconModel(config=A__      )
      model.to(A__      )
      model.eval()
      A__							:   int					       =		model(A__   ,	attention_mask=A__      )
      A__							:   Union[str, Any]					       =		model(A__      )
      self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )






     def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
      A__							:   Union[str, Any]					       =		True
      A__							:   Union[str, Any]					       =		FalconModel(A__      )
      model.to(A__      )
      model.eval()
      A__							:   Tuple					       =		model(
          A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	)
      A__							:   Union[str, Any]					       =		model(
          A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	)
      A__							:   List[str]					       =		model(A__   ,	attention_mask=A__      )
      self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)      )






     def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
      A__							:   Any					       =		FalconForCausalLM(config=A__      )
      model.to(A__      )
      model.eval()
      A__							:   Tuple					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
      self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )






     def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
      A__							:   Optional[Any]					       =		True
      A__							:   Union[str, Any]					       =		True
      A__							:   int					       =		FalconForCausalLM(config=A__      )
      model.to(A__      )
      model.eval()

      # first forward pass
      A__							:   List[Any]					       =		model(
          A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	use_cache=A__   ,	)
      A__							:   Tuple					       =		outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      A__							:   Tuple					       =		ids_tensor((self.batch_size, 3)   ,	config.vocab_size      )
      A__							:   List[Any]					       =		ids_tensor((self.batch_size, 3)   ,	vocab_size=2      )

      # append to next input_ids and
      A__							:   Optional[int]					       =		torch.cat([input_ids, next_tokens]   ,	dim=-1      )
      A__							:   List[str]					       =		torch.cat([input_mask, next_mask]   ,	dim=-1      )

      A__							:   Optional[int]					       =		model(
          A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	output_hidden_states=A__   ,	)["""hidden_states"""][0]
      A__							:   Any					       =		model(
          A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	past_key_values=A__   ,	output_hidden_states=A__   ,	)["""hidden_states"""][0]

      # select random slice
      A__							:   List[Any]					       =		ids_tensor((1,)   ,	output_from_past.shape[-1]      ).item()
      A__							:   List[str]					       =		output_from_no_past[:, -3:, random_slice_idx].detach()
      A__							:   Optional[Any]					       =		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(A__   ,	A__   ,	atol=1e-3      )      )






     def   __A      (			self      ):
      A__							:   List[str]					       =		self.prepare_config_and_inputs()
      (
          A__       
      )							:   Tuple					       =		config_and_inputs
      A__							:   List[str]					       =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class    _a						(__magic_name__		,       __magic_name__		,       __magic_name__		,       unittest.TestCase    ):


     '''simple docstring'''






     UpperCAmelCase__:       List[Any]  							=	(
         (
             FalconModel,
             FalconForCausalLM,
             FalconForSequenceClassification,
             FalconForTokenClassification,
             FalconForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__:       Tuple  							=	(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__:       str  							=	False
     UpperCAmelCase__:       int  							=	False






     def   __A      (			self      ):
      A__							:   List[Any]					       =		FalconModelTester(self      )
      A__							:   Union[str, Any]					       =		ConfigTester(self   ,	config_class=A__   ,	hidden_size=37      )






     def   __A      (			self      ):
      self.config_tester.run_common_tests()






     def   __A      (			self      ):
      A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__      )






     def   __A      (			self      ):
      A__							:   List[Any]					       =		self.model_tester.prepare_config_and_inputs()
      for alibi in [True, False]:
       A__							:   Tuple					       =		alibi
       self.model_tester.create_and_check_model(A__   ,	*A__      )






     def   __A      (			self      ):
      A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs_for_common()
      A__							:   Optional[int]					       =		3
      A__							:   int					       =		input_dict["""input_ids"""]
      A__							:   int					       =		input_ids.ne(1      ).to(A__      )
      A__							:   Union[str, Any]					       =		ids_tensor([self.model_tester.batch_size]   ,	self.model_tester.type_sequence_label_size      )
      A__							:   Optional[int]					       =		FalconForSequenceClassification(A__      )
      model.to(A__      )
      model.eval()
      A__							:   int					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
      self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels)      )






     def   __A      (			self      ):
      A__							:   str					       =		self.model_tester.prepare_config_and_inputs_for_common()
      A__							:   Dict					       =		3
      A__							:   Tuple					       =		"""single_label_classification"""
      A__							:   List[Any]					       =		input_dict["""input_ids"""]
      A__							:   Dict					       =		input_ids.ne(1      ).to(A__      )
      A__							:   Dict					       =		ids_tensor([self.model_tester.batch_size]   ,	self.model_tester.type_sequence_label_size      )
      A__							:   Any					       =		FalconForSequenceClassification(A__      )
      model.to(A__      )
      model.eval()
      A__							:   Any					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
      self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels)      )






     def   __A      (			self      ):
      A__							:   Optional[Any]					       =		self.model_tester.prepare_config_and_inputs_for_common()
      A__							:   List[str]					       =		input_dict["""input_ids"""]
      A__							:   List[str]					       =		FalconForCausalLM(A__      )
      model.to(A__      )
      model.eval()
      A__							:   Any					       =		model(A__   ,	use_cache=A__      )
      A__							:   Any					       =		input_ids.shape[0]
      A__							:   Union[str, Any]					       =		model._convert_to_rw_cache(result.past_key_values      )
      A__							:   int					       =		model._convert_cache_to_standard_format(A__   ,	A__      )
      for layer in range(len(A__      )      ):
       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   __A      (			self      ):
      A__							:   Dict					       =		self.model_tester.prepare_config_and_inputs_for_common()
      A__							:   Optional[Any]					       =		3
      A__							:   List[Any]					       =		"""multi_label_classification"""
      A__							:   Tuple					       =		input_dict["""input_ids"""]
      A__							:   List[Any]					       =		input_ids.ne(1      ).to(A__      )
      A__							:   Optional[Any]					       =		ids_tensor(
          [self.model_tester.batch_size, config.num_labels]   ,	self.model_tester.type_sequence_label_size      ).to(torch.float      )
      A__							:   Optional[int]					       =		FalconForSequenceClassification(A__      )
      model.to(A__      )
      model.eval()
      A__							:   List[Any]					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
      self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels)      )






     def   __A      (			self      ):
      # Falcon can have different numbers of KV-heads than the number of query heads, so we need
      # to override this test to use the right head counts.
      for model_class in self.all_generative_model_classes:
       A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs_for_common()

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

       A__							:   Optional[Any]					       =		model_class(A__      ).to(A__      )
       if "use_cache" not in inputs:
        A__							:   Optional[int]					       =		True
       A__							:   List[Any]					       =		model(**A__      )

       # 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

       A__							:   str					       =		(
           getattr(A__   ,	"""decoder_layers"""   ,	A__      )
           or getattr(A__   ,	"""num_decoder_layers"""   ,	A__      )
           or config.num_hidden_layers
       )
       A__							:   Dict					       =		getattr(A__   ,	"""num_kv_heads"""   ,	config.num_attention_heads      )
       A__							:   List[str]					       =		getattr(A__   ,	"""d_model"""   ,	config.hidden_size      )
       A__							:   Union[str, Any]					       =		embed_dim // num_attention_heads

       A__							:   str					       =		outputs["""past_key_values"""]
       self.assertEqual(len(A__      )   ,	A__      )

       A__							:   int					       =		inputs["""input_ids"""].shape
       for i in range(A__      ):
        if config.new_decoder_architecture:
         A__							:   Any					       =		config.num_attention_heads
        elif config.multi_query:
         A__							:   List[Any]					       =		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    _a						(unittest.TestCase    ):


     '''simple docstring'''






     @slow
     def   __A      (			self      ):
      A__							:   Dict					       =		AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b"""      )
      A__							:   List[Any]					       =		FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b"""      )
      model.eval()
      model.to(A__      )
      A__							:   Optional[int]					       =		tokenizer("""My favorite food is"""   ,	return_tensors="""pt"""      ).to(A__      )

      A__							:   Optional[Any]					       =		(
          """My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
      )

      A__							:   Any					       =		model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=19      )
      A__							:   Optional[int]					       =		tokenizer.batch_decode(A__      )[0]

      self.assertEqual(A__   ,	A__      )






     @slow
     def   __A      (			self      ):
      # The big models are way too big for the CI, so we use tiny random models that resemble their
      # architectures but with much smaller and fewer layers
      for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
       A__							:   Dict					       =		AutoTokenizer.from_pretrained(A__      )
       A__							:   List[str]					       =		FalconForCausalLM.from_pretrained(A__      )
       model.eval()
       model.to(A__      )
       A__							:   Union[str, Any]					       =		tokenizer("""My favorite food is"""   ,	return_tensors="""pt"""      ).to(A__      )

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






     @slow
     def   __A      (			self      ):
      # The big models are way too big for the CI, so we use tiny random models that resemble their
      # architectures but with much smaller and fewer layers
      with torch.no_grad():
       for repo in [
           "Rocketknight1/falcon-rw-1b",
           "Rocketknight1/tiny-random-falcon-7b",
           "Rocketknight1/tiny-random-falcon-40b",
       ]:
        A__							:   Dict					       =		AutoTokenizer.from_pretrained(A__      )
        A__							:   Any					       =		FalconForCausalLM.from_pretrained(A__      )
        model.eval()
        model.to(device=A__      )
        A__							:   List[str]					       =		tokenizer("""My favorite food is"""   ,	return_tensors="""pt"""      ).to(A__      )

        # Test results are the same with and without cache
        A__							:   Tuple					       =		model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=20   ,	use_cache=A__      )
        A__							:   Optional[Any]					       =		model.generate(**A__   ,	do_sample=A__   ,	max_new_tokens=20   ,	use_cache=A__      )
        self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0      )






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



