



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)
def 		UpperCamelCase	(   snake_case__       :    Union[tf.Tensor, np.ndarray]	)	->     List[int]:
   if isinstance(snake_case__       ,     np.ndarray	):
      return list(tensor.shape	)

   UpperCamelCase    :  List[Any]							=	tf.shape(snake_case__	)

   if tensor.shape == tf.TensorShape(snake_case__	):
      return dynamic

   UpperCamelCase    :  List[str]							=	tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(snake_case__	)]
def 		UpperCamelCase	(   snake_case__       :    tf.Tensor       ,     snake_case__       :    Optional[int] = None       ,     snake_case__       :    Optional[str] = None	)	->     tf.Tensor:
   return tf.nn.softmax(logits=logits + 1E-9       ,     axis=snake_case__       ,     name=snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Optional[int]=1E-5       ,     snake_case__       :    Tuple=-1	)	->     Optional[Any]:
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

   if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(snake_case__       ,     snake_case__	):
      raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'	)

   # Get mean and variance on the axis to be normalized
   UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	tf.nn.moments(snake_case__       ,     axes=[axis]       ,     keepdims=snake_case__	)

   if axis != -1:
      # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
      # on every dimension except axis
      UpperCamelCase    :  Union[str, Any]							=	[1] * inputs.shape.rank
      UpperCamelCase    :  Optional[int]							=	shape_list(snake_case__	)[axis]
      UpperCamelCase    :  List[Any]							=	tf.reshape(snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Dict							=	tf.reshape(snake_case__       ,     snake_case__	)

   # Compute layer normalization using the batch_normalization
   # function.
   UpperCamelCase    :  Dict							=	tf.nn.batch_normalization(
       snake_case__       ,     snake_case__       ,     snake_case__       ,     offset=snake_case__       ,     scale=snake_case__       ,     variance_epsilon=snake_case__       ,     )
   return outputs
def 		UpperCamelCase	(   snake_case__       :    List[Any]       ,     snake_case__       :    List[Any]=0       ,     snake_case__       :    Tuple=-1	)	->     Optional[int]:
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   if end_dim < 0:
      end_dim += input.shape.rank
   if start_dim < 0:
      start_dim += input.shape.rank

   if start_dim == end_dim:
      return input

   UpperCamelCase    :  Optional[Any]							=	tf.shape(snake_case__	)
   UpperCamelCase    :  Dict							=	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]	)
   UpperCamelCase    :  Optional[Any]							=	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]       ,     axis=0	)
   return tf.reshape(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    tf.Tensor	)	->     tf.Tensor:
   if not isinstance(snake_case__       ,     tf.Tensor	):
      UpperCamelCase    :  Optional[Any]							=	tf.convert_to_tensor(snake_case__	)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      UpperCamelCase    :  Optional[Any]							=	encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      UpperCamelCase    :  List[str]							=	encoder_attention_mask[:, None, None, :]
   # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
   # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
   # /transformer/transformer_layers.py#L270
   # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
   # encoder_extended_attention_mask.transpose(-1, -2))
   UpperCamelCase    :  int							=	(
       tf.cast(1       ,     encoder_attention_mask.dtype	) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask
def 		UpperCamelCase	(   snake_case__       :    tf.Tensor       ,     snake_case__       :    int       ,     snake_case__       :    str = "input_ids"	)	->     None:
   tf.debugging.assert_less(
       snake_case__       ,     tf.cast(snake_case__       ,     dtype=tensor.dtype	)       ,     message=(
           F"""The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case__	)}) must be smaller than the embedding """
           F"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
       )       ,     )
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Tuple       ,     snake_case__       :    List[str]	)	->     Tuple:
   UpperCamelCase    :  List[Any]							=	64512
   # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
   # because in that case even chunking the array would not make the saving
   # possible.
   UpperCamelCase    :  Optional[Any]							=	[x for x in data if len(snake_case__	) > HDF5_OBJECT_HEADER_LIMIT]

   # Expecting this to never be true.
   if bad_attributes:
      raise RuntimeError(
          'The following attributes cannot be saved to HDF5 file because '
          F"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
          F"""bytes: {bad_attributes}"""	)

   UpperCamelCase    :  List[Any]							=	np.asarray(snake_case__	)

   UpperCamelCase    :  Optional[int]							=	1
   UpperCamelCase    :  Optional[Any]							=	np.array_split(snake_case__       ,     snake_case__	)

   # This will never loop forever thanks to the test above.
   while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data	):
      num_chunks += 1
      UpperCamelCase    :  Dict							=	np.array_split(snake_case__       ,     snake_case__	)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(snake_case__	):
         UpperCamelCase    :  Any							=	chunk_data
   else:
      UpperCamelCase    :  int							=	data
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]	)	->     Union[str, Any]:
   if name in group.attrs:
      UpperCamelCase    :  int							=	[n.decode('utf8'	) if hasattr(snake_case__       ,     'decode'	) else n for n in group.attrs[name]]
   else:
      UpperCamelCase    :  Union[str, Any]							=	[]
      UpperCamelCase    :  str							=	0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode('utf8'	) if hasattr(snake_case__       ,     'decode'	) else n for n in group.attrs['%s%d' % (name, chunk_id)]]	)
         chunk_id += 1
   return data


def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     int:

   def _expand_single_ad_tensor(snake_case__       :    str	):
      if isinstance(snake_case__       ,     tf.Tensor	) and t.shape.rank == 1:
         return tf.expand_dims(snake_case__       ,     axis=-1	)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor       ,     snake_case__	)








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor



class 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=18,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=400,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			)     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  List[Any]							=	batch_size
          UpperCamelCase    :  int							=	num_channels
          UpperCamelCase    :  Any							=	image_size
          UpperCamelCase    :  Optional[int]							=	min_resolution
          UpperCamelCase    :  Optional[Any]							=	max_resolution
          UpperCamelCase    :  Union[str, Any]							=	do_resize
          UpperCamelCase    :  List[Any]							=	size
          UpperCamelCase    :  int							=	do_normalize






       def 					snake_case_       (			self       )     ->		Tuple:
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]       ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  ImageGPTImageProcessor if is_vision_available() else None
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  str							=	ImageGPTImageProcessingTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'clusters'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Tuple							=	self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size,			{'height': 18, 'width': 18}       )

          UpperCamelCase    :  Dict							=	self.image_processing_class.from_dict(self.image_processor_dict,			size=42       )
          self.assertEqual(image_processor.size,			{'height': 42, 'width': 42}       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.image_processing_class(**self.image_processor_dict       )
          UpperCamelCase    :  int							=	json.loads(image_processor.to_json_string()       )
          for key, value in self.image_processor_dict.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			obj[key]       )       )
             else:
                self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  List[str]							=	os.path.join(SCREAMING_SNAKE_CASE_,			'image_processor.json'       )
             image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Tuple							=	self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  List[Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  str							=	self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  Union[str, Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )






       @unittest.skip('ImageGPT requires clusters at initialization'       )
       def 					snake_case_       (			self       )     ->		str:
          pass






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	load_dataset('hf-internal-testing/fixtures_image_utils'       ,     split='test'	)

   UpperCamelCase    :  int							=	Image.open(dataset[4]['file']	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(dataset[5]['file']	)

   UpperCamelCase    :  str							=	[imagea, imagea]

   return images



@require_vision
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

          UpperCamelCase    :  List[str]							=	prepare_images()

          # test non-batched
          UpperCamelCase    :  int							=	image_processing(images[0],			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(1, 1024)       )

          UpperCamelCase    :  Union[str, Any]							=	[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist(),			SCREAMING_SNAKE_CASE_       )

          # test batched
          UpperCamelCase    :  Tuple							=	image_processing(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(2, 1024)       )

          UpperCamelCase    :  Optional[Any]							=	[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist(),			SCREAMING_SNAKE_CASE_       )



