



import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.generation import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )





@require_flax
class 		UpperCAmelCase						(						unittest.TestCase				):





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  jnp.ones((batch_size, length)			) / length
         return scores





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  20

         _lowerCAmelCase 			=  self._get_uniform_logits(batch_size=2       ,     length=UpperCAmelCase__			)

         # tweak scores to not be uniform anymore
         _lowerCAmelCase 			=  scores.at[1, 5].set((1 / length) + 0.1			)  # peak, 1st batch
         _lowerCAmelCase 			=  scores.at[1, 10].set((1 / length) - 0.4			)  # valley, 1st batch

         # compute softmax
         _lowerCAmelCase 			=  jax.nn.softmax(UpperCAmelCase__       ,     axis=-1			)

         _lowerCAmelCase 			=  FlaxTemperatureLogitsWarper(temperature=0.5			)
         _lowerCAmelCase 			=  FlaxTemperatureLogitsWarper(temperature=1.3			)

         _lowerCAmelCase 			=  jax.nn.softmax(temp_dist_warper_sharper(UpperCAmelCase__       ,     scores.copy()       ,     cur_len=UpperCAmelCase__			)       ,     axis=-1			)
         _lowerCAmelCase 			=  jax.nn.softmax(temp_dist_warper_smoother(UpperCAmelCase__       ,     scores.copy()       ,     cur_len=UpperCAmelCase__			)       ,     axis=-1			)

         # uniform distribution stays uniform
         self.assertTrue(jnp.allclose(probs[0, :]       ,     warped_prob_sharp[0, :]       ,     atol=1E-3			)			)
         self.assertTrue(jnp.allclose(probs[0, :]       ,     warped_prob_smooth[0, :]       ,     atol=1E-3			)			)

         # sharp peaks get higher, valleys get lower
         self.assertLess(probs[1, :].max()       ,     warped_prob_sharp[1, :].max()			)
         self.assertGreater(probs[1, :].min()       ,     warped_prob_sharp[1, :].min()			)

         # smooth peaks get lower, valleys get higher
         self.assertGreater(probs[1, :].max()       ,     warped_prob_smooth[1, :].max()			)
         self.assertLess(probs[1, :].min()       ,     warped_prob_smooth[1, :].min()			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  10
         _lowerCAmelCase 			=  2

         # create ramp distribution
         _lowerCAmelCase 			=  np.broadcast_to(np.arange(UpperCAmelCase__			)[None, :]       ,     (batch_size, vocab_size)			).copy()
         _lowerCAmelCase 			=  ramp_logits[1:, : vocab_size // 2] + vocab_size

         _lowerCAmelCase 			=  FlaxTopKLogitsWarper(3			)

         _lowerCAmelCase 			=  top_k_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)

         # check that correct tokens are filtered
         self.assertListEqual(jnp.isinf(scores[0]			).tolist()       ,     7 * [True] + 3 * [False]			)
         self.assertListEqual(jnp.isinf(scores[1]			).tolist()       ,     2 * [True] + 3 * [False] + 5 * [True]			)

         # check special case
         _lowerCAmelCase 			=  5
         _lowerCAmelCase 			=  FlaxTopKLogitsWarper(top_k=1       ,     filter_value=0.0       ,     min_tokens_to_keep=3			)

         _lowerCAmelCase 			=  np.broadcast_to(np.arange(UpperCAmelCase__			)[None, :]       ,     (batch_size, length)			).copy()
         _lowerCAmelCase 			=  top_k_warp_safety_check(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)

         # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
         self.assertListEqual((scores == 0.0).sum(axis=-1			).tolist()       ,     [2, 2]			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  10
         _lowerCAmelCase 			=  2

         # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
         _lowerCAmelCase 			=  np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]			)			)

         _lowerCAmelCase 			=  FlaxTopPLogitsWarper(0.8			)
         _lowerCAmelCase 			=  np.exp(top_p_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)			)

         # dist should be filtered to keep min num values so that sum is >= top_p
         # exp (-inf) => 0
         _lowerCAmelCase 			=  np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]			)
         self.assertTrue(np.allclose(UpperCAmelCase__       ,     UpperCAmelCase__       ,     atol=1E-3			)			)

         # check edge cases with negative and extreme logits
         _lowerCAmelCase 			=  np.broadcast_to(np.arange(UpperCAmelCase__			)[None, :]       ,     (batch_size, vocab_size)			).copy() - (
             vocab_size // 2
         )

         # make ramp_logits more extreme
         _lowerCAmelCase 			=  ramp_logits[1] * 100.0

         # make sure at least 2 tokens are kept
         _lowerCAmelCase 			=  FlaxTopPLogitsWarper(0.9       ,     min_tokens_to_keep=2       ,     filter_value=0.0			)
         _lowerCAmelCase 			=  top_p_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)

         # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).sum(axis=-1			).tolist()       ,     [3, 2]			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  20
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  0

         _lowerCAmelCase 			=  FlaxMinLengthLogitsProcessor(min_length=10       ,     eos_token_id=UpperCAmelCase__			)

         # check that min length is applied at length 5
         _lowerCAmelCase 			=  ids_tensor((batch_size, 20)       ,     vocab_size=20			)
         _lowerCAmelCase 			=  5
         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  min_dist_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()       ,     4 * [-float('''inf'''			)]			)

         # check that min length is not applied anymore at length 15
         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  15
         _lowerCAmelCase 			=  min_dist_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         self.assertFalse(jnp.isinf(UpperCAmelCase__			).any()			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  20
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  0

         _lowerCAmelCase 			=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__			)

         # check that all scores are -inf except the bos_token_id score
         _lowerCAmelCase 			=  ids_tensor((batch_size, 1)       ,     vocab_size=20			)
         _lowerCAmelCase 			=  1
         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  logits_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]			).all()			)
         self.assertListEqual(scores[:, bos_token_id].tolist()       ,     4 * [0]			)  # score for bos_token_id shold be zero

         # check that bos_token_id is not forced if current length is greater than 1
         _lowerCAmelCase 			=  3
         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  logits_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         self.assertFalse(jnp.isinf(UpperCAmelCase__			).any()			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  20
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  0
         _lowerCAmelCase 			=  5

         _lowerCAmelCase 			=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__       ,     eos_token_id=UpperCAmelCase__			)

         # check that all scores are -inf except the eos_token_id when max_length is reached
         _lowerCAmelCase 			=  ids_tensor((batch_size, 4)       ,     vocab_size=20			)
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  logits_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]			).all()			)
         self.assertListEqual(scores[:, eos_token_id].tolist()       ,     4 * [0]			)  # score for eos_token_id should be zero

         # check that eos_token_id is not forced if max_length is not reached
         _lowerCAmelCase 			=  3
         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  logits_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         self.assertFalse(jnp.isinf(UpperCAmelCase__			).any()			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  10
         _lowerCAmelCase 			=  15
         _lowerCAmelCase 			=  2
         _lowerCAmelCase 			=  1
         _lowerCAmelCase 			=  15

         # dummy input_ids and scores
         _lowerCAmelCase 			=  ids_tensor((batch_size, sequence_length)       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  input_ids.copy()

         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  scores.copy()

         # instantiate all dist processors
         _lowerCAmelCase 			=  FlaxTemperatureLogitsWarper(temperature=0.5			)
         _lowerCAmelCase 			=  FlaxTopKLogitsWarper(3			)
         _lowerCAmelCase 			=  FlaxTopPLogitsWarper(0.8			)

         # instantiate all logits processors
         _lowerCAmelCase 			=  FlaxMinLengthLogitsProcessor(min_length=10       ,     eos_token_id=UpperCAmelCase__			)
         _lowerCAmelCase 			=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__			)
         _lowerCAmelCase 			=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__       ,     eos_token_id=UpperCAmelCase__			)

         _lowerCAmelCase 			=  10

         # no processor list
         _lowerCAmelCase 			=  temp_dist_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         _lowerCAmelCase 			=  top_k_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         _lowerCAmelCase 			=  top_p_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         _lowerCAmelCase 			=  min_dist_proc(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         _lowerCAmelCase 			=  bos_dist_proc(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
         _lowerCAmelCase 			=  eos_dist_proc(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)

         # with processor list
         _lowerCAmelCase 			=  FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
         _lowerCAmelCase 			=  processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)

         # scores should be equal
         self.assertTrue(jnp.allclose(UpperCAmelCase__       ,     UpperCAmelCase__       ,     atol=1E-3			)			)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()       ,     input_ids_comp.tolist()			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  4
         _lowerCAmelCase 			=  10
         _lowerCAmelCase 			=  15
         _lowerCAmelCase 			=  2
         _lowerCAmelCase 			=  1
         _lowerCAmelCase 			=  15

         # dummy input_ids and scores
         _lowerCAmelCase 			=  ids_tensor((batch_size, sequence_length)       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  input_ids.copy()

         _lowerCAmelCase 			=  self._get_uniform_logits(UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  scores.copy()

         # instantiate all dist processors
         _lowerCAmelCase 			=  FlaxTemperatureLogitsWarper(temperature=0.5			)
         _lowerCAmelCase 			=  FlaxTopKLogitsWarper(3			)
         _lowerCAmelCase 			=  FlaxTopPLogitsWarper(0.8			)

         # instantiate all logits processors
         _lowerCAmelCase 			=  FlaxMinLengthLogitsProcessor(min_length=10       ,     eos_token_id=UpperCAmelCase__			)
         _lowerCAmelCase 			=  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__			)
         _lowerCAmelCase 			=  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__       ,     eos_token_id=UpperCAmelCase__			)

         _lowerCAmelCase 			=  10

         # no processor list
         def run_no_processor_list(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  temp_dist_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               _lowerCAmelCase 			=  top_k_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               _lowerCAmelCase 			=  top_p_warp(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               _lowerCAmelCase 			=  min_dist_proc(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               _lowerCAmelCase 			=  bos_dist_proc(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               _lowerCAmelCase 			=  eos_dist_proc(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               return scores

         # with processor list
         def run_processor_list(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FlaxLogitsProcessorList(
                   [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]			)
               _lowerCAmelCase 			=  processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     cur_len=UpperCAmelCase__			)
               return scores

         _lowerCAmelCase 			=  jax.jit(UpperCAmelCase__			)
         _lowerCAmelCase 			=  jax.jit(UpperCAmelCase__			)

         _lowerCAmelCase 			=  jitted_run_no_processor_list(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__			)
         _lowerCAmelCase 			=  jitted_run_processor_list(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__			)

         # scores should be equal
         self.assertTrue(jnp.allclose(UpperCAmelCase__       ,     UpperCAmelCase__       ,     atol=1E-3			)			)

         # input_ids should never be changed
         self.assertListEqual(input_ids.tolist()       ,     input_ids_comp.tolist()			)




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_									=							{"UserAgent": UserAgent().random}
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->dict:
      _lowerCAmelCase 			=  script.contents[0]
      _lowerCAmelCase 			=  json.loads(data[data.find('''{"config"'''				) : -1]				)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  F'''https://www.instagram.com/{username}/'''
         _lowerCAmelCase 			=  self.get_json()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  requests.get(self.url       ,     headers=_lowerCAmelCase			).text
         _lowerCAmelCase 			=  BeautifulSoup(_lowerCAmelCase       ,     '''html.parser'''			).find_all('''script'''			)
         try:
               return extract_user_profile(scripts[4]			)
         except (json.decoder.JSONDecodeError, KeyError):
               return extract_user_profile(scripts[3]			)





   def __repr__(	self			):
         return F'''{self.__class__.__name__}(\'{self.username}\')'''





   def __str__(	self			):
         return F'''{self.fullname} ({self.username}) is {self.biography}'''





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["username"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["full_name"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["biography"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["business_email"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["external_url"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_followed_by"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_follow"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_owner_to_timeline_media"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["profile_pic_url_hd"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_verified"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_private"]






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str = "github"				)->None:
      import os

      if os.environ.get('''CI'''				):
            return  # test failing on GitHub Actions
      _lowerCAmelCase 			=  InstagramUser(_SCREAMING_SNAKE_CASE				)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data     ,				_SCREAMING_SNAKE_CASE				)
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 1_5_0
      assert instagram_user.number_of_followers > 1_2_0_0_0_0
      assert instagram_user.number_of_followings > 1_5
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith('''https://instagram.'''				)
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    UpperCAmelCase_									=							InstagramUser("github")
    print(instagram_user)
    print(F"""{instagram_user.number_of_posts = }""")
    print(F"""{instagram_user.number_of_followers = }""")
    print(F"""{instagram_user.number_of_followings = }""")
    print(F"""{instagram_user.email = }""")
    print(F"""{instagram_user.website = }""")
    print(F"""{instagram_user.profile_picture_url = }""")
    print(F"""{instagram_user.is_verified = }""")
    print(F"""{instagram_user.is_private = }""")