
import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
      import os

      import jax.numpy as jnp
      from jax import jit

      from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
      from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

      _lowerCamelCase					   ="""0.12"""  # assumed parallelism: 8


if is_torch_available():
      import torch





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None							):
 if rng is None:
  lowerCamelCase		:	Tuple							=      random.Random()

 lowerCamelCase		:	Tuple							=      1
 for dim in shape:
  total_dims *= dim

 lowerCamelCase		:	List[str]							=      []
 for _ in range(lowerCamelCase							):
  values.append(rng.randint(0,	vocab_size - 1							)							)

 lowerCamelCase		:	Dict							=      np.array(lowerCamelCase,	dtype=jnp.intaa							).reshape(lowerCamelCase							)

 return output





def     _a					(						lowerCamelCase,	lowerCamelCase=None							):
 lowerCamelCase		:	Tuple							=      ids_tensor(lowerCamelCase,	vocab_size=2,	rng=lowerCamelCase							)
 # make sure that at least one token is attended to for each batch
 lowerCamelCase		:	str							=      1
 return attn_mask





@require_flax
class 							A__		:
      _UpperCAmelCase				:    Dict           =			None
      _UpperCAmelCase				:    List[str]           =			()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	Dict							=      self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       lowerCamelCase		:	List[str]							=      2
       lowerCamelCase		:	Optional[int]							=      inputs["""input_ids"""].shape[-1] // 2
       lowerCamelCase		:	Any							=      inputs["""input_ids"""][:max_batch_size, :sequence_length]

       lowerCamelCase		:	str							=      jnp.ones_like(__magic_name__      )
       lowerCamelCase		:	str							=      attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       lowerCamelCase		:	Tuple							=      input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
        # hack to allow generate for models such as GPT2 as is done in `generate()`
        lowerCamelCase		:	Dict							=      config.eos_token_id
       return config, input_ids, attention_mask, max_length
      @is_pt_flax_cross_test
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      self._get_input_ids_and_config()
       lowerCamelCase		:	List[Any]							=      False
       lowerCamelCase		:	List[str]							=      max_length
       lowerCamelCase		:	str							=      0

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	Optional[int]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Optional[Any]							=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
        lowerCamelCase		:	int							=      getattr(__magic_name__    ,  __magic_name__      )
        lowerCamelCase		:	List[Any]							=      pt_model_class(__magic_name__      ).eval()
        lowerCamelCase		:	List[str]							=      load_flax_weights_in_pytorch_model(__magic_name__    ,  flax_model.params      )

        lowerCamelCase		:	Union[str, Any]							=      flax_model.generate(__magic_name__      ).sequences
        lowerCamelCase		:	Union[str, Any]							=      pt_model.generate(torch.tensor(__magic_name__    ,  dtype=torch.long      )      )

        if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
         lowerCamelCase		:	int							=      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

        self.assertListEqual(pt_generation_outputs.numpy().tolist()    ,  flax_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      self._get_input_ids_and_config()
       lowerCamelCase		:	Tuple							=      False
       lowerCamelCase		:	Tuple							=      max_length

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	List[Any]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Dict							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	str							=      jit(model.generate      )
        lowerCamelCase		:	Any							=      jit_generate(__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Any							=      self._get_input_ids_and_config()
       lowerCamelCase		:	int							=      True
       lowerCamelCase		:	int							=      max_length

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	int							=      model_class(__magic_name__      )

        lowerCamelCase		:	List[str]							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	List[str]							=      jit(model.generate      )
        lowerCamelCase		:	Tuple							=      jit_generate(__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[str]							=      self._get_input_ids_and_config()
       lowerCamelCase		:	Any							=      False
       lowerCamelCase		:	int							=      max_length
       lowerCamelCase		:	Union[str, Any]							=      2

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	Dict							=      model_class(__magic_name__      )

        lowerCamelCase		:	Dict							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	Any							=      jit(model.generate      )
        lowerCamelCase		:	Union[str, Any]							=      jit_generate(__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      self._get_input_ids_and_config()
       lowerCamelCase		:	Optional[int]							=      False
       lowerCamelCase		:	Tuple							=      max_length
       lowerCamelCase		:	List[Any]							=      2
       lowerCamelCase		:	Optional[int]							=      2

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	str							=      model_class(__magic_name__      )

        lowerCamelCase		:	Any							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[0]    ,  input_ids.shape[0] * config.num_return_sequences      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Tuple							=      self._get_input_ids_and_config()
       lowerCamelCase		:	List[str]							=      True
       lowerCamelCase		:	Union[str, Any]							=      max_length
       lowerCamelCase		:	Dict							=      0.8
       lowerCamelCase		:	List[Any]							=      1_0
       lowerCamelCase		:	Any							=      0.3
       lowerCamelCase		:	Dict							=      1
       lowerCamelCase		:	List[str]							=      8
       lowerCamelCase		:	Tuple							=      9

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	Optional[int]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Dict							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	Dict							=      jit(model.generate      )
        lowerCamelCase		:	List[Any]							=      jit_generate(__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      self._get_input_ids_and_config()
       lowerCamelCase		:	Union[str, Any]							=      max_length
       lowerCamelCase		:	str							=      1
       lowerCamelCase		:	Optional[int]							=      8
       lowerCamelCase		:	List[str]							=      9

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	Union[str, Any]							=      model_class(__magic_name__      )

        lowerCamelCase		:	List[str]							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	Dict							=      jit(model.generate      )
        lowerCamelCase		:	Dict							=      jit_generate(__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      self._get_input_ids_and_config()
       lowerCamelCase		:	str							=      max_length
       lowerCamelCase		:	str							=      2
       lowerCamelCase		:	Optional[Any]							=      1
       lowerCamelCase		:	List[str]							=      8
       lowerCamelCase		:	List[str]							=      9

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	Union[str, Any]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Optional[int]							=      model.generate(__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	Optional[int]							=      jit(model.generate      )
        lowerCamelCase		:	Optional[Any]							=      jit_generate(__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      self._get_input_ids_and_config()

       # pad attention mask on the left
       lowerCamelCase		:	Union[str, Any]							=      attention_mask.at[(0, 0)].set(0      )

       lowerCamelCase		:	Dict							=      False
       lowerCamelCase		:	int							=      max_length

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	List[str]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Any							=      model.generate(__magic_name__    ,  attention_mask=__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	List[str]							=      jit(model.generate      )
        lowerCamelCase		:	List[str]							=      jit_generate(__magic_name__    ,  attention_mask=__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Any							=      self._get_input_ids_and_config()

       # pad attention mask on the left
       lowerCamelCase		:	Optional[int]							=      attention_mask.at[(0, 0)].set(0      )

       lowerCamelCase		:	Optional[Any]							=      True
       lowerCamelCase		:	List[str]							=      max_length

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	List[str]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Any							=      model.generate(__magic_name__    ,  attention_mask=__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	Optional[int]							=      jit(model.generate      )
        lowerCamelCase		:	int							=      jit_generate(__magic_name__    ,  attention_mask=__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      self._get_input_ids_and_config()

       # pad attention mask on the left
       lowerCamelCase		:	Tuple							=      attention_mask.at[(0, 0)].set(0      )

       lowerCamelCase		:	Any							=      2
       lowerCamelCase		:	Any							=      max_length

       for model_class in self.all_generative_model_classes:
        lowerCamelCase		:	Union[str, Any]							=      model_class(__magic_name__      )

        lowerCamelCase		:	Any							=      model.generate(__magic_name__    ,  attention_mask=__magic_name__      ).sequences
        self.assertEqual(generation_outputs.shape[-1]    ,  __magic_name__      )

        lowerCamelCase		:	str							=      jit(model.generate      )
        lowerCamelCase		:	int							=      jit_generate(__magic_name__    ,  attention_mask=__magic_name__      ).sequences

        self.assertListEqual(generation_outputs.tolist()    ,  jit_generation_outputs.tolist()      )





@require_flax
class 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""      )
       lowerCamelCase		:	List[Any]							=      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""      )

       lowerCamelCase		:	Tuple							=      """Hello world"""
       lowerCamelCase		:	Dict							=      tokenizer(__magic_name__    ,  return_tensors="""np"""      ).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(__magic_name__    ,  """do_samples"""      ):
        model.generate(__magic_name__    ,  do_samples=__magic_name__      )

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(__magic_name__    ,  """foo"""      ):
        lowerCamelCase		:	Any							=      {"""foo""": """bar"""}
        model.generate(__magic_name__    ,  **__magic_name__      )






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			["""pixel_values"""]
      def __init__(      self    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = PILImageResampling.BILINEAR    ,  __magic_name__ = True    ,  __magic_name__ = 1 / 2_5_5    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      size if size is not None else {"""shortest_edge""": 3_8_4}
       lowerCamelCase		:	Tuple							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	Dict							=      do_resize
       lowerCamelCase		:	List[Any]							=      size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase		:	Any							=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       lowerCamelCase		:	Union[str, Any]							=      resample
       lowerCamelCase		:	str							=      do_rescale
       lowerCamelCase		:	Union[str, Any]							=      rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase		:	int							=      image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = PILImageResampling.BICUBIC    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )
       if "shortest_edge" not in size:
        raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
       lowerCamelCase		:	str							=      size["""shortest_edge"""]

       if shortest_edge < 3_8_4:
        # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
        lowerCamelCase		:	List[str]							=      int(shortest_edge / crop_pct      )
        lowerCamelCase		:	Optional[Any]							=      get_resize_output_image_size(__magic_name__    ,  size=__magic_name__    ,  default_to_square=__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      resize(image=__magic_name__    ,  size=__magic_name__    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
        # then crop to (shortest_edge, shortest_edge)
        return center_crop(image=__magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  data_format=__magic_name__    ,  **__magic_name__      )
       else:
        # warping (no cropping) when evaluated at 384 or larger
        return resize(
            __magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return rescale(__magic_name__    ,  scale=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return normalize(__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = ChannelDimension.FIRST    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	str							=      do_resize if do_resize is not None else self.do_resize
       lowerCamelCase		:	Optional[Any]							=      crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase		:	Optional[int]							=      resample if resample is not None else self.resample
       lowerCamelCase		:	Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase		:	List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else self.image_mean
       lowerCamelCase		:	Tuple							=      image_std if image_std is not None else self.image_std

       lowerCamelCase		:	Dict							=      size if size is not None else self.size
       lowerCamelCase		:	str							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	List[str]							=      make_list_of_images(__magic_name__      )

       if not valid_images(__magic_name__      ):
        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 or resample is None:
        raise ValueError("""Size and resample must be specified if do_resize is True."""      )

       if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
        raise ValueError("""crop_pct must be specified if size < 384."""      )

       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."""      )

       # All transformations expect numpy arrays.
       lowerCamelCase		:	Optional[Any]							=      [to_numpy_array(__magic_name__      ) for image in images]

       if do_resize:
        lowerCamelCase		:	List[Any]							=      [self.resize(image=__magic_name__    ,  size=__magic_name__    ,  crop_pct=__magic_name__    ,  resample=__magic_name__      ) for image in images]

       if do_rescale:
        lowerCamelCase		:	Union[str, Any]							=      [self.rescale(image=__magic_name__    ,  scale=__magic_name__      ) for image in images]

       if do_normalize:
        lowerCamelCase		:	List[Any]							=      [self.normalize(image=__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__      ) for image in images]

       lowerCamelCase		:	Optional[int]							=      [to_channel_dimension_format(__magic_name__    ,  __magic_name__      ) for image in images]

       lowerCamelCase		:	List[str]							=      {"""pixel_values""": images}
       return BatchFeature(data=__magic_name__    ,  tensor_type=__magic_name__      )




