
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.xglm.modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		)




@require_tf
class 				lowerCamelCase__							:




	'''simple docstring'''




	_A			=  XGLMConfig
	_A			=  {}
	_A			=  """gelu"""







	def __init__(      self	:List[str]  ,     a	:Any  ,     a	:Dict=1_4  ,     a	:Dict=7  ,     a	:Dict=True  ,     a	:Union[str, Any]=True  ,     a	:Optional[int]=True  ,     a	:str=9_9  ,     a	:Tuple=3_2  ,     a	:Optional[int]=2  ,     a	:Union[str, Any]=4  ,     a	:Optional[Any]=3_7  ,     a	:Optional[int]="gelu"  ,     a	:Union[str, Any]=0.1  ,     a	:List[str]=0.1  ,     a	:Dict=5_1_2  ,     a	:str=0.02  ,     )	->				Any:
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      batch_size
								__UpperCamelCase			:     Any         =      seq_length
								__UpperCamelCase			:     Any         =      is_training
								__UpperCamelCase			:     List[str]         =      use_input_mask
								__UpperCamelCase			:     str         =      use_labels
								__UpperCamelCase			:     int         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[Any]         =      num_hidden_layers
								__UpperCamelCase			:     int         =      num_attention_heads
								__UpperCamelCase			:     Dict         =      ffn_dim
								__UpperCamelCase			:     Optional[int]         =      activation_function
								__UpperCamelCase			:     Optional[Any]         =      activation_dropout
								__UpperCamelCase			:     List[str]         =      attention_dropout
								__UpperCamelCase			:     Optional[int]         =      max_position_embeddings
								__UpperCamelCase			:     Dict         =      initializer_range
								__UpperCamelCase			:     Dict         =      None
								__UpperCamelCase			:     Optional[int]         =      0
								__UpperCamelCase			:     Optional[Any]         =      2
								__UpperCamelCase			:     str         =      1







	def      _lowerCamelCase       (      self	:int		)	->				str:
								return XGLMConfig.from_pretrained("facebook/xglm-564M"		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     Tuple         =      tf.clip_by_value(
								    ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size		)  ,     clip_value_min=0  ,     clip_value_max=3		)

								__UpperCamelCase			:     List[Any]         =      None
								if self.use_input_mask:
															__UpperCamelCase			:     Dict         =      random_attention_mask([self.batch_size, self.seq_length]		)

								__UpperCamelCase			:     Optional[int]         =      self.get_config()

								__UpperCamelCase			:     Dict         =      floats_tensor([self.num_hidden_layers, self.num_attention_heads]  ,     2		)

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								)







	def      _lowerCamelCase       (      self	:Tuple		)	->				List[str]:
								return XGLMConfig(
								    vocab_size=self.vocab_size  ,     d_model=self.hidden_size  ,     num_layers=self.num_hidden_layers  ,     attention_heads=self.num_attention_heads  ,     ffn_dim=self.ffn_dim  ,     activation_function=self.activation_function  ,     activation_dropout=self.activation_dropout  ,     attention_dropout=self.attention_dropout  ,     max_position_embeddings=self.max_position_embeddings  ,     initializer_range=self.initializer_range  ,     use_cache=a  ,     bos_token_id=self.bos_token_id  ,     eos_token_id=self.eos_token_id  ,     pad_token_id=self.pad_token_id  ,     return_dict=a  ,     )







	def      _lowerCamelCase       (      self	:int		)	->				int:
								__UpperCamelCase			:     Tuple         =      self.prepare_config_and_inputs()

								(
								    (
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		
								)			:     Optional[Any]         =      config_and_inputs

								__UpperCamelCase			:     List[str]         =      {
								    "input_ids": input_ids,
								    "head_mask": head_mask,
								}

								return config, inputs_dict




@require_tf
class 				lowerCamelCase__							(       __lowercase  ,		__lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	_A			=  (TFXGLMForCausalLM,) if is_tf_available() else ()
	_A			=  (
	    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
	)
	_A			=  False
	_A			=  False
	_A			=  False







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Any:
								__UpperCamelCase			:     Optional[Any]         =      TFXGLMModelTester(self		)
								__UpperCamelCase			:     List[Any]         =      ConfigTester(self  ,     config_class=a  ,     n_embd=3_7		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[Any]:
								self.config_tester.run_common_tests()







	@slow
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				List[Any]:

								for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase			:     List[str]         =      TFXGLMModel.from_pretrained(a		)
															self.assertIsNotNone(a		)







	@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."		)
	def      _lowerCamelCase       (      self	:List[str]		)	->				str:
								super().test_resize_token_embeddings()




@require_tf
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	@slow
	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Tuple=True		)	->				Optional[Any]:
								__UpperCamelCase			:     str         =      TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
								__UpperCamelCase			:     Tuple         =      tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]  ,     dtype=tf.intaa		)  # The dog
								# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
								# fmt: off
								__UpperCamelCase			:     Tuple         =      [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
								# fmt: on
								__UpperCamelCase			:     List[str]         =      model.generate(a  ,     do_sample=a  ,     num_beams=1		)
								if verify_outputs:
															self.assertListEqual(output_ids[0].numpy().tolist()  ,     a		)







	@slow
	def      _lowerCamelCase       (      self	:Tuple		)	->				Union[str, Any]:
								__UpperCamelCase			:     Any         =      XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)
								__UpperCamelCase			:     str         =      TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)

								tf.random.set_seed(0		)
								__UpperCamelCase			:     Union[str, Any]         =      tokenizer("Today is a nice day and"  ,     return_tensors="tf"		)
								__UpperCamelCase			:     int         =      tokenized.input_ids
								# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
								with tf.device(":/CPU:0"		):
															__UpperCamelCase			:     List[Any]         =      model.generate(a  ,     do_sample=a  ,     seed=[7, 0]		)
								__UpperCamelCase			:     Tuple         =      tokenizer.decode(output_ids[0]  ,     skip_special_tokens=a		)

								__UpperCamelCase			:     Tuple         =      (
								    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
								)
								self.assertEqual(a  ,     a		)







	@slow
	def      _lowerCamelCase       (      self	:int		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
								__UpperCamelCase			:     List[Any]         =      XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)

								__UpperCamelCase			:     List[Any]         =      "left"

								# use different length sentences to test batching
								__UpperCamelCase			:     int         =      [
								    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
								    "left-padding, such as in batched generation. The output for the sequence below should be the same "
								    "regardless of whether left padding is applied or not. When",
								    "Hello, my dog is a little",
								]

								__UpperCamelCase			:     int         =      tokenizer(a  ,     return_tensors="tf"  ,     padding=a		)
								__UpperCamelCase			:     Optional[Any]         =      inputs["input_ids"]

								__UpperCamelCase			:     List[str]         =      model.generate(input_ids=a  ,     attention_mask=inputs["attention_mask"]  ,     max_new_tokens=1_2		)

								__UpperCamelCase			:     List[Any]         =      tokenizer(sentences[0]  ,     return_tensors="tf"		).input_ids
								__UpperCamelCase			:     Optional[int]         =      model.generate(input_ids=a  ,     max_new_tokens=1_2		)

								__UpperCamelCase			:     Tuple         =      tokenizer(sentences[1]  ,     return_tensors="tf"		).input_ids
								__UpperCamelCase			:     Optional[int]         =      model.generate(input_ids=a  ,     max_new_tokens=1_2		)

								__UpperCamelCase			:     List[Any]         =      tokenizer.batch_decode(a  ,     skip_special_tokens=a		)
								__UpperCamelCase			:     Optional[Any]         =      tokenizer.decode(output_non_padded[0]  ,     skip_special_tokens=a		)
								__UpperCamelCase			:     Tuple         =      tokenizer.decode(output_padded[0]  ,     skip_special_tokens=a		)

								__UpperCamelCase			:     int         =      [
								    "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
								    "left-padding, such as in batched generation. The output for the sequence below should be the same "
								    "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
								    "a single",
								    "Hello, my dog is a little bit of a shy one, but he is very friendly",
								]
								self.assertListEqual(a  ,     a		)
								self.assertListEqual(a  ,     [non_padded_sentence, padded_sentence]		)

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

lowercase						:      List[str]									= {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 				lowerCamelCase__							(       __lowercase  ,		__lowercase):




	'''simple docstring'''




	_A			=  'nat'

	_A			=  {
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(      self	:Optional[Any]  ,     a	:Any=4  ,     a	:Any=3  ,     a	:int=6_4  ,     a	:Dict=[3, 4, 6, 5]  ,     a	:Dict=[2, 4, 8, 1_6]  ,     a	:Optional[Any]=7  ,     a	:Any=3.0  ,     a	:Optional[int]=True  ,     a	:int=0.0  ,     a	:Union[str, Any]=0.0  ,     a	:List[Any]=0.1  ,     a	:str="gelu"  ,     a	:Union[str, Any]=0.02  ,     a	:Tuple=1E-5  ,     a	:str=0.0  ,     a	:Optional[int]=None  ,     a	:Dict=None  ,     **a	:Optional[Any]  ,     )	->				int:
								super().__init__(**a		)

								__UpperCamelCase			:     Any         =      patch_size
								__UpperCamelCase			:     str         =      num_channels
								__UpperCamelCase			:     List[Any]         =      embed_dim
								__UpperCamelCase			:     str         =      depths
								__UpperCamelCase			:     str         =      len(a		)
								__UpperCamelCase			:     Optional[Any]         =      num_heads
								__UpperCamelCase			:     Dict         =      kernel_size
								__UpperCamelCase			:     Union[str, Any]         =      mlp_ratio
								__UpperCamelCase			:     Union[str, Any]         =      qkv_bias
								__UpperCamelCase			:     List[str]         =      hidden_dropout_prob
								__UpperCamelCase			:     Any         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Any         =      drop_path_rate
								__UpperCamelCase			:     Any         =      hidden_act
								__UpperCamelCase			:     Tuple         =      layer_norm_eps
								__UpperCamelCase			:     Dict         =      initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__UpperCamelCase			:     int         =      int(embed_dim * 2 ** (len(a		) - 1)		)
								__UpperCamelCase			:     List[Any]         =      layer_scale_init_value
								__UpperCamelCase			:     Optional[Any]         =      ["stem"] + [f'stage{idx}' for idx in range(1  ,     len(a		) + 1		)]
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      get_aligned_output_features_output_indices(
								    out_features=a  ,     out_indices=a  ,     stage_names=self.stage_names		)