




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


if is_vision_available():
  import PIL


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			Any      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			Dict[str, int] = None      ,				UpperCAmelCase__   :			PILImageResampling = PILImageResampling.BICUBIC      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			Dict[str, int] = None      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			Union[int, float] = 1 / 255      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			Optional[Union[float, List[float]]] = None      ,				UpperCAmelCase__   :			Optional[Union[float, List[float]]] = None      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			List[Any]      ,				)		->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__   				=					size if size is not None else {'''shortest_edge''': 224}
           A__   				=					get_size_dict(UpperCAmelCase__      ,				default_to_square=UpperCAmelCase__)
           A__   				=					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A__   				=					get_size_dict(UpperCAmelCase__      ,				default_to_square=UpperCAmelCase__      ,				param_name='''crop_size''')

           A__   				=					do_resize
           A__   				=					size
           A__   				=					resample
           A__   				=					do_center_crop
           A__   				=					crop_size
           A__   				=					do_rescale
           A__   				=					rescale_factor
           A__   				=					do_normalize
           A__   				=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           A__   				=					image_std if image_std is not None else OPENAI_CLIP_STD
           A__   				=					do_convert_rgb


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			Dict[str, int]      ,				UpperCAmelCase__   :			PILImageResampling = PILImageResampling.BICUBIC      ,				UpperCAmelCase__   :			Optional[Union[str, ChannelDimension]] = None      ,				**UpperCAmelCase__   :			int      ,				)		->np.ndarray:





           '''simple docstring'''

           A__   				=					get_size_dict(UpperCAmelCase__      ,				default_to_square=UpperCAmelCase__)
           if "shortest_edge" not in size:
                  raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
           A__   				=					get_resize_output_image_size(UpperCAmelCase__      ,				size=size['''shortest_edge''']      ,				default_to_square=UpperCAmelCase__)
           return resize(UpperCAmelCase__      ,				size=UpperCAmelCase__      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			Dict[str, int]      ,				UpperCAmelCase__   :			Optional[Union[str, ChannelDimension]] = None      ,				**UpperCAmelCase__   :			Any      ,				)		->np.ndarray:





           '''simple docstring'''

           A__   				=					get_size_dict(UpperCAmelCase__)
           if "height" not in size or "width" not in size:
                  raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
           return center_crop(UpperCAmelCase__      ,				size=(size['''height'''], size['''width'''])      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			Union[int, float]      ,				UpperCAmelCase__   :			Optional[Union[str, ChannelDimension]] = None      ,				**UpperCAmelCase__   :			List[Any]      ,				)		->Any:





           '''simple docstring'''

           return rescale(UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			Union[float, List[float]]      ,				UpperCAmelCase__   :			Union[float, List[float]]      ,				UpperCAmelCase__   :			Optional[Union[str, ChannelDimension]] = None      ,				**UpperCAmelCase__   :			Any      ,				)		->np.ndarray:





           '''simple docstring'''

           return normalize(UpperCAmelCase__      ,				mean=UpperCAmelCase__      ,				std=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			ImageInput      ,				UpperCAmelCase__   :			bool = None      ,				UpperCAmelCase__   :			Dict[str, int] = None      ,				UpperCAmelCase__   :			PILImageResampling = None      ,				UpperCAmelCase__   :			bool = None      ,				UpperCAmelCase__   :			int = None      ,				UpperCAmelCase__   :			bool = None      ,				UpperCAmelCase__   :			float = None      ,				UpperCAmelCase__   :			bool = None      ,				UpperCAmelCase__   :			Optional[Union[float, List[float]]] = None      ,				UpperCAmelCase__   :			Optional[Union[float, List[float]]] = None      ,				UpperCAmelCase__   :			bool = None      ,				UpperCAmelCase__   :			Optional[Union[str, TensorType]] = None      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			str      ,				)		->PIL.Image.Image:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					size if size is not None else self.size
           A__   				=					get_size_dict(UpperCAmelCase__      ,				param_name='''size'''      ,				default_to_square=UpperCAmelCase__)
           A__   				=					resample if resample is not None else self.resample
           A__   				=					do_center_crop if do_center_crop is not None else self.do_center_crop
           A__   				=					crop_size if crop_size is not None else self.crop_size
           A__   				=					get_size_dict(UpperCAmelCase__      ,				param_name='''crop_size'''      ,				default_to_square=UpperCAmelCase__)
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					rescale_factor if rescale_factor is not None else self.rescale_factor
           A__   				=					do_normalize if do_normalize is not None else self.do_normalize
           A__   				=					image_mean if image_mean is not None else self.image_mean
           A__   				=					image_std if image_std is not None else self.image_std
           A__   				=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError(
                      '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                      '''torch.Tensor, tf.Tensor or jax.ndarray.''')

           if do_resize and size is None:
                  raise ValueError('''Size must be specified if do_resize is True.''')

           if do_center_crop and crop_size is None:
                  raise ValueError('''Crop size must be specified if do_center_crop is True.''')

           if do_rescale and rescale_factor is None:
                  raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

           if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  A__   				=					[convert_to_rgb(UpperCAmelCase__) for image in images]

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for image in images]

           if do_resize:
                  A__   				=					[self.resize(image=UpperCAmelCase__      ,				size=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_center_crop:
                  A__   				=					[self.center_crop(image=UpperCAmelCase__      ,				size=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__) for image in images]

           if do_normalize:
                  A__   				=					[self.normalize(image=UpperCAmelCase__      ,				mean=UpperCAmelCase__      ,				std=UpperCAmelCase__) for image in images]

           A__   				=					[to_channel_dimension_format(UpperCAmelCase__      ,				UpperCAmelCase__) for image in images]

           A__   				=					{'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__      ,				tensor_type=UpperCAmelCase__)








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_					:

    '''simple docstring'''






    UpperCAmelCase__													=   XGLMConfig
    UpperCAmelCase__													=   {}
    UpperCAmelCase__													=   '''gelu'''


    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=14      ,				UpperCAmelCase__   :			Tuple=7      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=99      ,				UpperCAmelCase__   :			Union[str, Any]=32      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			Dict=4      ,				UpperCAmelCase__   :			List[str]=37      ,				UpperCAmelCase__   :			Union[str, Any]="gelu"      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=512      ,				UpperCAmelCase__   :			List[Any]=0.02      ,				)		->str:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_input_mask
           A__   				=					use_labels
           A__   				=					vocab_size
           A__   				=					d_model
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					ffn_dim
           A__   				=					activation_function
           A__   				=					activation_dropout
           A__   				=					attention_dropout
           A__   				=					max_position_embeddings
           A__   				=					initializer_range
           A__   				=					None
           A__   				=					0
           A__   				=					2
           A__   				=					1


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

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

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

           A__   				=					self.get_config()

           A__   				=					floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,				2)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''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  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()

           (
               (
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							
           )   				=					config_and_inputs

           A__   				=					{
               '''input_ids''': input_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict


@require_tf
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






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


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					TFXGLMModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				n_embd=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[str]:





           '''simple docstring'''

           for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					TFXGLMModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)





    @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           super().test_resize_token_embeddings()


@require_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]=True)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					tf.convert_to_tensor([[2, 268, 9_865]]      ,				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__   				=					[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
           # fmt: on
           A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				num_beams=1)
           if verify_outputs:
                  self.assertListEqual(output_ids[0].numpy().tolist()      ,				UpperCAmelCase__)


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')
           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')

           tf.random.set_seed(0)
           A__   				=					tokenizer('''Today is a nice day and'''      ,				return_tensors='''tf''')
           A__   				=					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__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				seed=[7, 0])
           A__   				=					tokenizer.decode(output_ids[0]      ,				skip_special_tokens=UpperCAmelCase__)

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





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')

           A__   				=					'''left'''

           # use different length sentences to test batching
           A__   				=					[
               '''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__   				=					tokenizer(UpperCAmelCase__      ,				return_tensors='''tf'''      ,				padding=UpperCAmelCase__)
           A__   				=					inputs['''input_ids''']

           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				attention_mask=inputs['''attention_mask''']      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[0]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[1]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer.batch_decode(UpperCAmelCase__      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_non_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					[
               '''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])


