

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

    __A			:					Optional[Any]						=			'0.12'  # assumed parallelism: 8


if is_torch_available():
    import torch





def 	__a     (      A__			:  Any			,  A__			:  Tuple			,  A__			:  str=None       ):
    if rng is None:
        SCREAMING_SNAKE_CASE										=   random.Random()

    SCREAMING_SNAKE_CASE										=   1
    for dim in shape:
        total_dims *= dim

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

    SCREAMING_SNAKE_CASE										=   np.array(A__			,  dtype=jnp.intaa       ).reshape(A__       )

    return output





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   ids_tensor(A__			,  vocab_size=2			,  rng=A__       )
    # make sure that at least one token is attended to for each batch
    SCREAMING_SNAKE_CASE										=   1
    return attn_mask





@require_flax
class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   lowerCamelCase__				=      None
   lowerCamelCase__				=      ()





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   inputs["input_ids"].shape[-1] // 2
       SCREAMING_SNAKE_CASE										=   inputs["input_ids"][:max_batch_size, :sequence_length]

       SCREAMING_SNAKE_CASE										=   jnp.ones_like(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       SCREAMING_SNAKE_CASE										=   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()`
           SCREAMING_SNAKE_CASE										=   config.eos_token_id
       return config, input_ids, attention_mask, max_length





   @is_pt_flax_cross_test
   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   0

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
           SCREAMING_SNAKE_CASE										=   getattr(__lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   pt_model_class(__lowerCamelCase			).eval()
           SCREAMING_SNAKE_CASE										=   load_flax_weights_in_pytorch_model(__lowerCamelCase     , flax_model.params			)

           SCREAMING_SNAKE_CASE										=   flax_model.generate(__lowerCamelCase			).sequences
           SCREAMING_SNAKE_CASE										=   pt_model.generate(torch.tensor(__lowerCamelCase     , dtype=torch.long			)			)

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

           self.assertListEqual(pt_generation_outputs.numpy().tolist()     , flax_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   True
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   2

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   2

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[0]     , input_ids.shape[0] * config.num_return_sequences			)





   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   True
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   0.8
       SCREAMING_SNAKE_CASE										=   10
       SCREAMING_SNAKE_CASE										=   0.3
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   8
       SCREAMING_SNAKE_CASE										=   9

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   8
       SCREAMING_SNAKE_CASE										=   9

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   8
       SCREAMING_SNAKE_CASE										=   9

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()

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

       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()

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

       SCREAMING_SNAKE_CASE										=   True
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences

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





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()

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

       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences

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





@require_flax
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"			)
       SCREAMING_SNAKE_CASE										=   FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"			)

       SCREAMING_SNAKE_CASE										=   "Hello world"
       SCREAMING_SNAKE_CASE										=   tokenizer(__lowerCamelCase     , return_tensors="np"			).input_ids

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

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






'''simple docstring'''

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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							b.T
   __SCREAMING_SNAKE_CASE   				=							np.sum(np.square(__UpperCAmelCase   )				,						axis=1   )
   __SCREAMING_SNAKE_CASE   				=							np.sum(np.square(__UpperCAmelCase   )				,						axis=0   )
   __SCREAMING_SNAKE_CASE   				=							np.matmul(__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							aa[:, None] - 2 * ab + ba[None, :]
   return d

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							x.reshape(-1				,						3   )
   __SCREAMING_SNAKE_CASE   				=							squared_euclidean_distance(__UpperCAmelCase				,						__UpperCAmelCase   )
   return np.argmin(__UpperCAmelCase				,						axis=1   )







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						['pixel_values']





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[Union[List[List[int]], np.ndarray]] = None					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      bool = True					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""height""": 256, """width""": 256}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     ) if clusters is not None else None
      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size
      __SCREAMING_SNAKE_CASE   				=							resample
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_color_quantize





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      int					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      if "height" not in size or "width" not in size:
         raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""     )
      return resize(
          lowerCamelCase					,size=(size["""height"""], size["""width"""])					,resample=lowerCamelCase					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							rescale(image=lowerCamelCase					,scale=1 / 127.5					,data_format=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image - 1
      return image





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      ImageInput					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = None					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Optional[bool] = None					,lowerCamelCase					:      Optional[Union[List[List[int]], np.ndarray]] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else self.size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE   				=							do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      __SCREAMING_SNAKE_CASE   				=							clusters if clusters is not None else self.clusters
      __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							make_list_of_images(lowerCamelCase     )

      if not valid_images(lowerCamelCase     ):
         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_color_quantize and clusters is None:
         raise ValueError("""Clusters must be specified if do_color_quantize is True."""     )

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

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

      if do_normalize:
         __SCREAMING_SNAKE_CASE   				=							[self.normalize(image=lowerCamelCase     ) for image in images]

      if do_color_quantize:
         __SCREAMING_SNAKE_CASE   				=							[to_channel_dimension_format(lowerCamelCase					,ChannelDimension.LAST     ) for image in images]
         # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
         __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							color_quantize(lowerCamelCase					,lowerCamelCase     ).reshape(images.shape[:-1]     )

         # flatten to (batch_size, height*width)
         __SCREAMING_SNAKE_CASE   				=							images.shape[0]
         __SCREAMING_SNAKE_CASE   				=							images.reshape(lowerCamelCase					,-1     )

         # We need to convert back to a list of images to keep consistent behaviour across processors.
         __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							[to_channel_dimension_format(lowerCamelCase					,lowerCamelCase     ) for image in images]

      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": images}
      return BatchFeature(data=lowerCamelCase					,tensor_type=lowerCamelCase     )





