


'''simple docstring'''



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 							__UpperCamelCase    :
	A_           =   XGLMConfig
	A_           =   {}
	A_           =   "gelu"




	def __init__(       self						,    __a						,    __a=14						,    __a=7						,    __a=True						,    __a=True						,    __a=True						,    __a=99						,    __a=32						,    __a=2						,    __a=4						,    __a=37						,    __a="gelu"						,    __a=0.1						,    __a=0.1						,    __a=512						,    __a=0.02						,    ):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  parent
						__a      :	Dict    =  batch_size
						__a      :	int    =  seq_length
						__a      :	str    =  is_training
						__a      :	List[str]    =  use_input_mask
						__a      :	Dict    =  use_labels
						__a      :	str    =  vocab_size
						__a      :	Tuple    =  d_model
						__a      :	Union[str, Any]    =  num_hidden_layers
						__a      :	str    =  num_attention_heads
						__a      :	int    =  ffn_dim
						__a      :	Optional[int]    =  activation_function
						__a      :	Optional[Any]    =  activation_dropout
						__a      :	Dict    =  attention_dropout
						__a      :	str    =  max_position_embeddings
						__a      :	Tuple    =  initializer_range
						__a      :	int    =  None
						__a      :	List[Any]    =  0
						__a      :	int    =  2
						__a      :	Dict    =  1




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return XGLMConfig.from_pretrained('facebook/xglm-564M'							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  tf.clip_by_value(
						    ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size							)						,    clip_value_min=0						,    clip_value_max=3							)

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

						__a      :	Dict    =  self.get_config()

						__a      :	Union[str, Any]    =  floats_tensor([self.num_hidden_layers, self.num_attention_heads]						,    2							)

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						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=UpperCamelCase__						,    bos_token_id=self.bos_token_id						,    eos_token_id=self.eos_token_id						,    pad_token_id=self.pad_token_id						,    return_dict=UpperCamelCase__						,    )





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.prepare_config_and_inputs()

						(
						    __a
						)      :	Union[str, Any]    =  config_and_inputs

						__a      :	List[Any]    =  {
						    '''input_ids''': input_ids,
						    '''head_mask''': head_mask,
						}

						return config, inputs_dict





@require_tf
class 							__UpperCamelCase    (		__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				unittest.TestCase ):
	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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  TFXGLMModelTester(self							)
						__a      :	List[Any]    =  ConfigTester(self						,    config_class=UpperCamelCase__						,    n_embd=37							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a      :	Union[str, Any]    =  TFXGLMModel.from_pretrained(UpperCamelCase__							)
											self.assertIsNotNone(UpperCamelCase__							)





	@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_resize_token_embeddings()





@require_tf
class 							__UpperCamelCase    (		unittest.TestCase ):




	@slow
	def 				__UpperCAmelCase							(       self						,    __a=True							):

						'''simple docstring'''





						__a      :	Dict    =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'							)
						__a      :	List[Any]    =  tf.convert_to_tensor([[2, 268, 9865]]						,    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
						__a      :	List[Any]    =  [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
						# fmt: on
						__a      :	int    =  model.generate(UpperCamelCase__						,    do_sample=UpperCamelCase__						,    num_beams=1							)
						if verify_outputs:
											self.assertListEqual(output_ids[0].numpy().tolist()						,    UpperCamelCase__							)




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'							)
						__a      :	Dict    =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'							)

						tf.random.set_seed(0							)
						__a      :	List[Any]    =  tokenizer('Today is a nice day and'						,    return_tensors='tf'							)
						__a      :	Union[str, Any]    =  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'							):
											__a      :	Optional[int]    =  model.generate(UpperCamelCase__						,    do_sample=UpperCamelCase__						,    seed=[7, 0]							)
						__a      :	Any    =  tokenizer.decode(output_ids[0]						,    skip_special_tokens=UpperCamelCase__							)

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





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'							)
						__a      :	List[Any]    =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'							)

						__a      :	List[Any]    =  '''left'''

						# use different length sentences to test batching
						__a      :	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''',
						]

						__a      :	Dict    =  tokenizer(UpperCamelCase__						,    return_tensors='tf'						,    padding=UpperCamelCase__							)
						__a      :	str    =  inputs['''input_ids''']

						__a      :	Union[str, Any]    =  model.generate(input_ids=UpperCamelCase__						,    attention_mask=inputs['attention_mask']						,    max_new_tokens=12							)

						__a      :	Tuple    =  tokenizer(sentences[0]						,    return_tensors='tf'							).input_ids
						__a      :	List[Any]    =  model.generate(input_ids=UpperCamelCase__						,    max_new_tokens=12							)

						__a      :	Union[str, Any]    =  tokenizer(sentences[1]						,    return_tensors='tf'							).input_ids
						__a      :	Dict    =  model.generate(input_ids=UpperCamelCase__						,    max_new_tokens=12							)

						__a      :	Optional[int]    =  tokenizer.batch_decode(UpperCamelCase__						,    skip_special_tokens=UpperCamelCase__							)
						__a      :	List[Any]    =  tokenizer.decode(output_non_padded[0]						,    skip_special_tokens=UpperCamelCase__							)
						__a      :	List[Any]    =  tokenizer.decode(output_padded[0]						,    skip_special_tokens=UpperCamelCase__							)

						__a      :	Union[str, Any]    =  [
						    '''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(UpperCamelCase__						,    UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__						,    [non_padded_sentence, padded_sentence]							)








'''simple docstring'''



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	__lowercase     :						int	=     np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	__lowercase     :						Any	=     [0, 25, 50]
	__lowercase     :						int	=     [25, 50, 75]
	__lowercase     :						List[str]	=     fuzz.membership.trimf(X, abca)
	__lowercase     :						Any	=     fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	__lowercase     :						List[Any]	=     np.ones(75)
	__lowercase     :						Any	=     np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	__lowercase     :						str	=     fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	__lowercase     :						List[Any]	=     fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	__lowercase     :						Optional[Any]	=     young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	__lowercase     :						str	=     young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	__lowercase     :						Optional[Any]	=     fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	__lowercase     :						Union[str, Any]	=     fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title('Young')
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title('Middle aged')
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title('union')
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title('intersection')
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title('complement_a')
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title('difference a/b')
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title('alg_sum')
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title('alg_product')
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title('bdd_sum')
	plt.grid(True)

	plt.subplot(4, 3, 10)
	plt.plot(X, bdd_difference)
	plt.title('bdd_difference')
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()



