




"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
  import jax.numpy as jnp

if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch

class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	{
           '''task_specific_params''': {
               '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
               '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
               '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
           }
       }
       UpperCamelCase              =	{
           '''task_specific_params.summarization.length_penalty''': 1.0,
           '''task_specific_params.summarization.max_length''': 1_2_8,
           '''task_specific_params.summarization.min_length''': 1_2,
           '''task_specific_params.summarization.num_beams''': 4,
           '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
           '''task_specific_params.summarization_cnn.max_length''': 1_4_2,
           '''task_specific_params.summarization_cnn.min_length''': 5_6,
           '''task_specific_params.summarization_cnn.num_beams''': 4,
           '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
           '''task_specific_params.summarization_xsum.max_length''': 6_2,
           '''task_specific_params.summarization_xsum.min_length''': 1_1,
           '''task_specific_params.summarization_xsum.num_beams''': 6,
       }

       self.assertEqual(flatten_dict(lowerCamelCase_)       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_)       ,					x.transpose()))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_       ,					axes=(1, 2, 0))       ,					x.transpose((1, 2, 0))))







   @require_torch
   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_)       ,					transpose(lowerCamelCase_).numpy()))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_       ,					axes=(1, 2, 0))       ,					transpose(lowerCamelCase_       ,					axes=(1, 2, 0)).numpy()))







   @require_tf
   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_)       ,					transpose(lowerCamelCase_).numpy()))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_       ,					axes=(1, 2, 0))       ,					transpose(lowerCamelCase_       ,					axes=(1, 2, 0)).numpy()))







   @require_flax
   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_)       ,					np.asarray(transpose(lowerCamelCase_))))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(transpose(lowerCamelCase_       ,					axes=(1, 2, 0))       ,					np.asarray(transpose(lowerCamelCase_       ,					axes=(1, 2, 0)))))







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(4, 3))       ,					np.reshape(lowerCamelCase_       ,					(4, 3))))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(1_2, 5))       ,					np.reshape(lowerCamelCase_       ,					(1_2, 5))))







   @require_torch
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(4, 3))       ,					reshape(lowerCamelCase_       ,					(4, 3)).numpy()))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(1_2, 5))       ,					reshape(lowerCamelCase_       ,					(1_2, 5)).numpy()))







   @require_tf
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(4, 3))       ,					reshape(lowerCamelCase_       ,					(4, 3)).numpy()))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(1_2, 5))       ,					reshape(lowerCamelCase_       ,					(1_2, 5)).numpy()))







   @require_flax
   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(4, 3))       ,					np.asarray(reshape(lowerCamelCase_       ,					(4, 3)))))

       UpperCamelCase              =	np.random.randn(3       ,					4       ,					5)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(reshape(lowerCamelCase_       ,					(1_2, 5))       ,					np.asarray(reshape(lowerCamelCase_       ,					(1_2, 5)))))







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	np.random.randn(1       ,					3       ,					4)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_)       ,					np.squeeze(lowerCamelCase_)))

       UpperCamelCase              =	np.random.randn(1       ,					4       ,					1       ,					5)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_       ,					axis=2)       ,					np.squeeze(lowerCamelCase_       ,					axis=2)))







   @require_torch
   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	np.random.randn(1       ,					3       ,					4)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_)       ,					squeeze(lowerCamelCase_).numpy()))

       UpperCamelCase              =	np.random.randn(1       ,					4       ,					1       ,					5)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_       ,					axis=2)       ,					squeeze(lowerCamelCase_       ,					axis=2).numpy()))







   @require_tf
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	np.random.randn(1       ,					3       ,					4)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_)       ,					squeeze(lowerCamelCase_).numpy()))

       UpperCamelCase              =	np.random.randn(1       ,					4       ,					1       ,					5)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_       ,					axis=2)       ,					squeeze(lowerCamelCase_       ,					axis=2).numpy()))







   @require_flax
   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	np.random.randn(1       ,					3       ,					4)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_)       ,					np.asarray(squeeze(lowerCamelCase_))))

       UpperCamelCase              =	np.random.randn(1       ,					4       ,					1       ,					5)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(squeeze(lowerCamelCase_       ,					axis=2)       ,					np.asarray(squeeze(lowerCamelCase_       ,					axis=2))))







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_       ,					axis=1)       ,					np.expand_dims(lowerCamelCase_       ,					axis=1)))







   @require_torch
   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	torch.tensor(lowerCamelCase_)
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_       ,					axis=1)       ,					expand_dims(lowerCamelCase_       ,					axis=1).numpy()))







   @require_tf
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	tf.constant(lowerCamelCase_)
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_       ,					axis=1)       ,					expand_dims(lowerCamelCase_       ,					axis=1).numpy()))







   @require_flax
   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	np.random.randn(3       ,					4)
       UpperCamelCase              =	jnp.array(lowerCamelCase_)
       self.assertTrue(np.allclose(expand_dims(lowerCamelCase_       ,					axis=1)       ,					np.asarray(expand_dims(lowerCamelCase_       ,					axis=1))))


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




