
import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
     from transformers import (
         FlaxBertModel,
         FlaxCLIPVisionModel,
         FlaxVisionTextDualEncoderModel,
         FlaxViTModel,
         VisionTextDualEncoderConfig,
         VisionTextDualEncoderProcessor,
     )
     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )


if is_torch_available():
     import torch

     from transformers import VisionTextDualEncoderModel

if is_vision_available():
     from PIL import Image





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if isinstance(__lowerCAmelCase    ,		collections.abc.Iterable							):
        return x
    return (x, x)




@require_flax
class 					_SCREAMING_SNAKE_CASE  :
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          pass
      def A_			(			self ):
          pass
      def A_			(			self ):
          pass
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				np.abs((a - b) ).max()
          self.assertLessEqual(lowerCamelCase		,	lowerCamelCase		,	F"""Difference between torch and flax is {diff} (>= {tol}).""" )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						=				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				FlaxVisionTextDualEncoderModel(lowerCamelCase )

          snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )

          self.assertEqual(output["text_embeds"].shape		,	(input_ids.shape[0], config.projection_dim) )
          self.assertEqual(output["image_embeds"].shape		,	(pixel_values.shape[0], config.projection_dim) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						,					snake_case__						=				self.get_vision_text_model(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				{"vision_model": vision_model, "text_model": text_model}
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase )

          snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )

          self.assertEqual(output["text_embeds"].shape		,	(input_ids.shape[0], model.config.projection_dim) )
          self.assertEqual(output["image_embeds"].shape		,	(pixel_values.shape[0], model.config.projection_dim) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						,					snake_case__						=				self.get_vision_text_model(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				{"vision_model": vision_model, "text_model": text_model}
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase )

          snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )
          snake_case__						=				output[0]

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(lowerCamelCase )
              snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase )

              snake_case__						=				model(input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase )
              snake_case__						=				after_output[0]
              snake_case__						=				np.amax(np.abs(out_a - out_a ) )
              self.assertLessEqual(lowerCamelCase		,	1e-3 )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None		,	**lowerCamelCase ):
          snake_case__						,					snake_case__						=				self.get_vision_text_model(lowerCamelCase		,	lowerCamelCase )
          snake_case__						=				{"vision_model": vision_model, "text_model": text_model}
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase )

          snake_case__						=				model(
              input_ids=lowerCamelCase		,	pixel_values=lowerCamelCase		,	attention_mask=lowerCamelCase		,	output_attentions=lowerCamelCase )

          snake_case__						=				output.vision_model_output.attentions
          self.assertEqual(len(lowerCamelCase )		,	vision_config.num_hidden_layers )

          # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
          snake_case__						=				to_atuple(vision_model.config.image_size )
          snake_case__						=				to_atuple(vision_model.config.patch_size )
          snake_case__						=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          snake_case__						=				num_patches + 1
          self.assertEqual(vision_attentions[0].shape[-3:]		,	(vision_config.num_attention_heads, seq_len, seq_len) )

          snake_case__						=				output.text_model_output.attentions
          self.assertEqual(len(lowerCamelCase )		,	text_config.num_hidden_layers )

          self.assertEqual(
              text_attentions[0].shape[-3:]		,	(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          pt_model.to(lowerCamelCase )
          pt_model.eval()

          # prepare inputs
          snake_case__						=				inputs_dict
          snake_case__						=				{k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}

          with torch.no_grad():
              snake_case__						=				pt_model(**lowerCamelCase ).to_tuple()

          snake_case__						=				fx_model(**lowerCamelCase ).to_tuple()
          self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase )		,	"Output lengths differ between Flax and PyTorch" )
          for fx_output, pt_output in zip(fx_outputs[:4]		,	pt_outputs[:4] ):
              self.assert_almost_equals(lowerCamelCase		,	pt_output.numpy()		,	4e-2 )

          # PT -> Flax
          with tempfile.TemporaryDirectory() as tmpdirname:
              pt_model.save_pretrained(lowerCamelCase )
              snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase		,	from_pt=lowerCamelCase )

          snake_case__						=				fx_model_loaded(**lowerCamelCase ).to_tuple()
          self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase )		,	"Output lengths differ between Flax and PyTorch" )
          for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]		,	pt_outputs[:4] ):
              self.assert_almost_equals(lowerCamelCase		,	pt_output.numpy()		,	4e-2 )

          # Flax -> PT
          with tempfile.TemporaryDirectory() as tmpdirname:
              fx_model.save_pretrained(lowerCamelCase )
              snake_case__						=				VisionTextDualEncoderModel.from_pretrained(lowerCamelCase		,	from_flax=lowerCamelCase )

          pt_model_loaded.to(lowerCamelCase )
          pt_model_loaded.eval()

          with torch.no_grad():
              snake_case__						=				pt_model_loaded(**lowerCamelCase ).to_tuple()

          self.assertEqual(len(lowerCamelCase )		,	len(lowerCamelCase )		,	"Output lengths differ between Flax and PyTorch" )
          for fx_output, pt_output_loaded in zip(fx_outputs[:4]		,	pt_outputs_loaded[:4] ):
              self.assert_almost_equals(lowerCamelCase		,	pt_output_loaded.numpy()		,	4e-2 )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				VisionTextDualEncoderModel(lowerCamelCase )
          snake_case__						=				FlaxVisionTextDualEncoderModel(lowerCamelCase )

          snake_case__						=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,	lowerCamelCase )
          snake_case__						=				fx_state

          self.check_pt_flax_equivalence(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase		,	lowerCamelCase )

          snake_case__						=				VisionTextDualEncoderModel(lowerCamelCase )
          snake_case__						=				FlaxVisionTextDualEncoderModel(lowerCamelCase )

          snake_case__						=				load_flax_weights_in_pytorch_model(lowerCamelCase		,	fx_model.params )

          self.check_pt_flax_equivalence(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_model_from_pretrained_configs(**lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_save_load(**lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          self.check_vision_text_output_attention(**lowerCamelCase )
      @is_pt_flax_cross_test
      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						=				config_inputs_dict.pop("vision_config" )
          snake_case__						=				config_inputs_dict.pop("text_config" )

          snake_case__						=				config_inputs_dict

          self.check_equivalence_pt_to_flax(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
          self.check_equivalence_flax_to_pt(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )



      @slow
      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.get_pretrained_model_and_inputs()

          snake_case__						=				model_a(**lowerCamelCase )
          snake_case__						=				outputs[0]

          with tempfile.TemporaryDirectory() as tmp_dirname:
              model_a.save_pretrained(lowerCamelCase )
              snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase )

              snake_case__						=				model_a(**lowerCamelCase )
              snake_case__						=				after_outputs[0]
              snake_case__						=				np.amax(np.abs(out_a - out_a ) )
              self.assertLessEqual(lowerCamelCase		,	1e-5 )





@require_flax
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              "hf-internal-testing/tiny-random-vit"		,	"hf-internal-testing/tiny-bert"		,	vision_from_pt=lowerCamelCase		,	text_from_pt=lowerCamelCase		,	)
          snake_case__						=				13
          snake_case__						=				floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ] )
          snake_case__						=				ids_tensor([batch_size, 4]		,	model.config.text_config.vocab_size )
          snake_case__						=				random_attention_mask([batch_size, 4] )
          snake_case__						=				{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

          return model, inputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FlaxViTModel(lowerCamelCase )
          snake_case__						=				FlaxBertModel(lowerCamelCase )
          return vision_model, text_model



      def A_			(			self ):
          snake_case__						=				FlaxViTModelTester(self )
          snake_case__						=				FlaxBertModelTester(self )
          snake_case__						=				vit_model_tester.prepare_config_and_inputs()
          snake_case__						=				bert_model_tester.prepare_config_and_inputs()

          snake_case__						,					snake_case__						=				vision_config_and_inputs

          snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				text_config_and_inputs

          # make sure that cross attention layers are added
          return {
              "text_config": text_config,
              "vision_config": vision_config,
              "pixel_values": pixel_values,
              "attention_mask": attention_mask,
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
          }





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              "hf-internal-testing/tiny-random-clip"		,	"hf-internal-testing/tiny-bert"		,	vision_from_pt=lowerCamelCase		,	text_from_pt=lowerCamelCase		,	)
          snake_case__						=				13
          snake_case__						=				floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ] )
          snake_case__						=				ids_tensor([batch_size, 4]		,	model.config.text_config.vocab_size )
          snake_case__						=				random_attention_mask([batch_size, 4] )
          snake_case__						=				{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

          return model, inputs
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				FlaxCLIPVisionModel(lowerCamelCase )
          snake_case__						=				FlaxBertModel(lowerCamelCase )
          return vision_model, text_model



      def A_			(			self ):
          snake_case__						=				FlaxCLIPVisionModelTester(self )
          snake_case__						=				FlaxBertModelTester(self )
          snake_case__						=				clip_model_tester.prepare_config_and_inputs()
          snake_case__						=				bert_model_tester.prepare_config_and_inputs()

          snake_case__						,					snake_case__						=				vision_config_and_inputs

          snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				text_config_and_inputs

          # make sure that cross attention layers are added
          return {
              "text_config": text_config,
              "vision_config": vision_config,
              "pixel_values": pixel_values,
              "attention_mask": attention_mask,
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
          }





@require_flax
@require_vision
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @slow
      def A_			(			self ):
          snake_case__						=				FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"		,	logit_scale_init_value=1.0 )
          snake_case__						=				VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )

          snake_case__						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
          snake_case__						=				processor(
              text=["una foto di un gatto", "una foto di un cane"]		,	images=lowerCamelCase		,	padding=lowerCamelCase		,	return_tensors="np" )

          snake_case__						=				model(**lowerCamelCase )

          # verify the logits
          self.assertEqual(outputs.logits_per_image.shape		,	(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
          self.assertEqual(
              outputs.logits_per_text.shape		,	(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])		,	)

          snake_case__						=				np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )

          self.assertTrue(np.allclose(outputs.logits_per_image		,	lowerCamelCase		,	atol=1e-3 ) )





import argparse
import struct
import unittest




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase ):
          snake_case__						=				data

          # Initialize hash values
          snake_case__						=				[
              0X6A_09E_667,
              0XBB_67A_E85,
              0X3C_6EF_372,
              0XA5_4FF_53A,
              0X51_0E5_27F,
              0X9B_056_88C,
              0X1F_83D_9AB,
              0X5B_E0C_D19,
          ]

          # Initialize round constants
          snake_case__						=				[
              0X42_8A2_F98,
              0X71_374_491,
              0XB5_C0F_BCF,
              0XE9_B5D_BA5,
              0X39_56C_25B,
              0X59_F11_1F1,
              0X92_3F8_2A4,
              0XAB_1C5_ED5,
              0XD8_07A_A98,
              0X12_835_B01,
              0X24_318_5BE,
              0X55_0C7_DC3,
              0X72_BE5_D74,
              0X80_DEB_1FE,
              0X9B_DC0_6A7,
              0XC1_9BF_174,
              0XE4_9B6_9C1,
              0XEF_BE4_786,
              0X0F_C19_DC6,
              0X24_0CA_1CC,
              0X2D_E92_C6F,
              0X4A_748_4AA,
              0X5C_B0A_9DC,
              0X76_F98_8DA,
              0X98_3E5_152,
              0XA8_31C_66D,
              0XB0_032_7C8,
              0XBF_597_FC7,
              0XC6_E00_BF3,
              0XD5_A79_147,
              0X06_CA6_351,
              0X14_292_967,
              0X27_B70_A85,
              0X2E_1B2_138,
              0X4D_2C6_DFC,
              0X53_380_D13,
              0X65_0A7_354,
              0X76_6A0_ABB,
              0X81_C2C_92E,
              0X92_722_C85,
              0XA2_BFE_8A1,
              0XA8_1A6_64B,
              0XC2_4B8_B70,
              0XC7_6C5_1A3,
              0XD1_92E_819,
              0XD6_990_624,
              0XF4_0E3_585,
              0X10_6AA_070,
              0X19_A4C_116,
              0X1E_376_C08,
              0X27_487_74C,
              0X34_B0B_CB5,
              0X39_1C0_CB3,
              0X4E_D8A_A4A,
              0X5B_9CC_A4F,
              0X68_2E6_FF3,
              0X74_8F8_2EE,
              0X78_A56_36F,
              0X84_C87_814,
              0X8C_C70_208,
              0X90_BEF_FFA,
              0XA4_506_CEB,
              0XBE_F9A_3F7,
              0XC6_717_8F2,
          ]

          snake_case__						=				self.preprocessing(self.data )
          self.final_hash()
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				B"\x80" + (B"\x00" * (63 - (len(lowerCamelCase ) + 8) % 64))
          snake_case__						=				struct.pack(">Q"		,	(len(lowerCamelCase ) * 8) )
          return data + padding + big_endian_integer
      def A_			(			self ):
          # Convert into blocks of 64 bytes
          snake_case__						=				[
              self.preprocessed_data[x : x + 64]
              for x in range(0		,	len(self.preprocessed_data )		,	64 )
          ]

          for block in self.blocks:
              # Convert the given block into a list of 4 byte integers
              snake_case__						=				list(struct.unpack(">16L"		,	lowerCamelCase ) )
              # add 48 0-ed integers
              words += [0] * 48

              snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				self.hashes

              for index in range(0		,	64 ):
                  if index > 15:
                      # modify the zero-ed indexes at the end of the array
                      snake_case__						=				(
                          self.ror(words[index - 15]		,	7 )
                          ^ self.ror(words[index - 15]		,	18 )
                          ^ (words[index - 15] >> 3)
                      )
                      snake_case__						=				(
                          self.ror(words[index - 2]		,	17 )
                          ^ self.ror(words[index - 2]		,	19 )
                          ^ (words[index - 2] >> 10)
                      )

                      snake_case__						=				(
                          words[index - 16] + sa + words[index - 7] + sa
                      ) % 0X100_000_000

                  # Compression
                  snake_case__						=				self.ror(lowerCamelCase		,	6 ) ^ self.ror(lowerCamelCase		,	11 ) ^ self.ror(lowerCamelCase		,	25 )
                  snake_case__						=				(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
                  snake_case__						=				(
                      h + sa + ch + self.round_constants[index] + words[index]
                  ) % 0X100_000_000
                  snake_case__						=				self.ror(lowerCamelCase		,	2 ) ^ self.ror(lowerCamelCase		,	13 ) ^ self.ror(lowerCamelCase		,	22 )
                  snake_case__						=				(a & b) ^ (a & c) ^ (b & c)
                  snake_case__						=				(sa + maj) % 0X100_000_000

                  snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				(
                      g,
                      f,
                      e,
                      ((d + tempa) % 0X100_000_000),
                      c,
                      b,
                      a,
                      ((tempa + tempa) % 0X100_000_000),
                  )

              snake_case__						=				[a, b, c, d, e, f, g, h]

              # Modify final values
              snake_case__						=				[
                  ((element + mutated_hash_values[index]) % 0X100_000_000)
                  for index, element in enumerate(self.hashes )
              ]

          snake_case__						=				"".join([hex(lowerCamelCase )[2:].zfill(8 ) for value in self.hashes] )



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase ):
          return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)





class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          import hashlib

          snake_case__						=				bytes("Test String"		,	"utf-8" )
          self.assertEqual(SHAaaa(lowerCamelCase ).hash		,	hashlib.shaaaa(lowerCamelCase ).hexdigest() )





def 	SCREAMING_SNAKE_CASE__    (      ):
    import doctest

    doctest.testmod()

    snake_case__						=				argparse.ArgumentParser()
    parser.add_argument(
        "-s"    ,		"--string"    ,		dest="input_string"    ,		default="Hello World!! Welcome to Cryptography"    ,		help="Hash the string"    ,		)
    parser.add_argument(
        "-f"    ,		"--file"    ,		dest="input_file"    ,		help="Hash contents of a file"							)

    snake_case__						=				parser.parse_args()

    snake_case__						=				args.input_string

    # hash input should be a bytestring
    if args.input_file:
        with open(args.input_file    ,		"rb"							) as f:
            snake_case__						=				f.read()
    else:
        snake_case__						=				bytes(__lowerCAmelCase    ,		"utf-8"							)

    print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
     main()



