



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_    :
							lowerCamelCase__   			=		XGLMConfig
							lowerCamelCase__   			=		{}
							lowerCamelCase__   			=		'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'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

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

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

														_lowerCAmelCase     :							int    =    self.get_config()

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

														return (
														    config,
														    input_ids,
														    input_mask,
														    head_mask,
														)







							def    snake_case__      (   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=__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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.prepare_config_and_inputs()

														(
														    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    
														)     :							List[Any]    =    config_and_inputs

														_lowerCAmelCase     :							List[Any]    =    {
														    "input_ids": input_ids,
														    "head_mask": head_mask,
														}

														return config, inputs_dict



@require_tf
class   UpperCAmelCase_    (       a   ,					a   ,					unittest.TestCase):
							lowerCamelCase__   			=		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
							lowerCamelCase__   			=		(TFXGLMForCausalLM,) if is_tf_available() else ()
							lowerCamelCase__   			=		(
							    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
							)
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    TFXGLMModelTester(self)
														_lowerCAmelCase     :							int    =    ConfigTester(self,			config_class=__a,			n_embd=37)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowerCAmelCase     :							Any    =    TFXGLMModel.from_pretrained(__a)
																					self.assertIsNotNone(__a)







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

														'''simple docstring'''

														super().test_resize_token_embeddings()



@require_tf
class   UpperCAmelCase_    (       unittest.TestCase):







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

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
														_lowerCAmelCase     :							Optional[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
														_lowerCAmelCase     :							Tuple    =    [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
														_lowerCAmelCase     :							Tuple    =    model.generate(__a,			do_sample=__a,			num_beams=1)
														if verify_outputs:
																					self.assertListEqual(output_ids[0].numpy().tolist(),			__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    XGLMTokenizer.from_pretrained("facebook/xglm-564M")
														_lowerCAmelCase     :							List[str]    =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")

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

														_lowerCAmelCase     :							Optional[Any]    =    (
														    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
														)
														self.assertEqual(__a,			__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
														_lowerCAmelCase     :							Any    =    XGLMTokenizer.from_pretrained("facebook/xglm-564M")

														_lowerCAmelCase     :							Union[str, Any]    =    "left"

														# use different length sentences to test batching
														_lowerCAmelCase     :							Tuple    =    [
														    "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",
														]

														_lowerCAmelCase     :							Tuple    =    tokenizer(__a,			return_tensors="tf",			padding=__a)
														_lowerCAmelCase     :							List[str]    =    inputs["input_ids"]

														_lowerCAmelCase     :							Any    =    model.generate(input_ids=__a,			attention_mask=inputs["attention_mask"],			max_new_tokens=12)

														_lowerCAmelCase     :							List[Any]    =    tokenizer(sentences[0],			return_tensors="tf").input_ids
														_lowerCAmelCase     :							int    =    model.generate(input_ids=__a,			max_new_tokens=12)

														_lowerCAmelCase     :							List[Any]    =    tokenizer(sentences[1],			return_tensors="tf").input_ids
														_lowerCAmelCase     :							Optional[Any]    =    model.generate(input_ids=__a,			max_new_tokens=12)

														_lowerCAmelCase     :							List[Any]    =    tokenizer.batch_decode(__a,			skip_special_tokens=__a)
														_lowerCAmelCase     :							Union[str, Any]    =    tokenizer.decode(output_non_padded[0],			skip_special_tokens=__a)
														_lowerCAmelCase     :							Union[str, Any]    =    tokenizer.decode(output_padded[0],			skip_special_tokens=__a)

														_lowerCAmelCase     :							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(__a,			__a)
														self.assertListEqual(__a,			[non_padded_sentence, padded_sentence])





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return 0.0






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]  )]  )
							_lowerCAmelCase     :							Optional[int]    =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]  )]  )
							return lowest, highest






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    512
							_lowerCAmelCase     :							Union[str, Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							Optional[Any]    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							int    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							str    =    np.abs(np.fft.fft(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Union[str, Any]    =    20 * np.logaa(_lowerCamelCase  )

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				, samplerate / 2 - 1  )
							plt.xlabel("Frequency (Hz)"  )
							plt.xscale("log"  )

							# Display within reasonable bounds
							_lowerCAmelCase     :							List[Any]    =    get_bounds(_lowerCamelCase				, _lowerCamelCase  )
							plt.ylim(max([-80, bounds[0]]  )				, min([80, bounds[1]]  )  )
							plt.ylabel("Gain (dB)"  )

							plt.plot(_lowerCamelCase  )
							plt.show()






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    512
							_lowerCAmelCase     :							Optional[Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							str    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							Optional[Any]    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							Optional[Any]    =    np.angle(np.fft.fft(_lowerCamelCase  )  )

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				, samplerate / 2 - 1  )
							plt.xlabel("Frequency (Hz)"  )
							plt.xscale("log"  )

							plt.ylim(-2 * pi				, 2 * pi  )
							plt.ylabel("Phase shift (Radians)"  )
							plt.plot(np.unwrap(_lowerCamelCase				, -2 * pi  )  )
							plt.show()
